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|---|---|---|---|---|---|---|---|---|---|
ray-project/ray | 7,443 | ray-project__ray-7443 | [
"7438"
] | 4198db5038dd465a5d5db91b0ca84d98391aec2d | diff --git a/rllib/agents/trainer_template.py b/rllib/agents/trainer_template.py
--- a/rllib/agents/trainer_template.py
+++ b/rllib/agents/trainer_template.py
@@ -38,40 +38,43 @@ def build_trainer(name,
Arguments:
name (str): name of the trainer (e.g., "PPO")
default_policy (cls): the default Policy class to use
- default_config (dict): The default config dict of the algorithm,
- otherwise uses the Trainer default config.
- validate_config (func): optional callback that checks a given config
- for correctness. It may mutate the config as needed.
- get_initial_state (func): optional function that returns the initial
- state dict given the trainer instance as an argument. The state
- dict must be serializable so that it can be checkpointed, and will
- be available as the `trainer.state` variable.
- get_policy_class (func): optional callback that takes a config and
- returns the policy class to override the default with
- before_init (func): optional function to run at the start of trainer
- init that takes the trainer instance as argument
- make_workers (func): override the method that creates rollout workers.
- This takes in (trainer, env_creator, policy, config) as args.
- make_policy_optimizer (func): optional function that returns a
- PolicyOptimizer instance given (WorkerSet, config)
- after_init (func): optional function to run at the end of trainer init
- that takes the trainer instance as argument
- before_train_step (func): optional callback to run before each train()
- call. It takes the trainer instance as an argument.
- after_optimizer_step (func): optional callback to run after each
- step() call to the policy optimizer. It takes the trainer instance
- and the policy gradient fetches as arguments.
- after_train_result (func): optional callback to run at the end of each
- train() call. It takes the trainer instance and result dict as
- arguments, and may mutate the result dict as needed.
- collect_metrics_fn (func): override the method used to collect metrics.
- It takes the trainer instance as argumnt.
- before_evaluate_fn (func): callback to run before evaluation. This
- takes the trainer instance as argument.
- mixins (list): list of any class mixins for the returned trainer class.
- These mixins will be applied in order and will have higher
- precedence than the Trainer class
- training_pipeline (func): Experimental support for custom
+ default_config (Optional[dict]): The default config dict of the
+ algorithm. If None, uses the Trainer default config.
+ validate_config (Optional[callable]): Optional callback that checks a
+ given config for correctness. It may mutate the config as needed.
+ get_initial_state (Optional[callable]): Optional callable that returns
+ the initial state dict given the trainer instance as an argument.
+ The state dict must be serializable so that it can be checkpointed,
+ and will be available as the `trainer.state` variable.
+ get_policy_class (Optional[callable]): Optional callable that takes a
+ Trainer config and returns the policy class to override the default
+ with.
+ before_init (Optional[callable]): Optional callable to run at the start
+ of trainer init that takes the trainer instance as argument.
+ make_workers (Optional[callable]): Override the default method that
+ creates rollout workers. This takes in (trainer, env_creator,
+ policy, config) as args.
+ make_policy_optimizer (Optional[callable]): Optional callable that
+ returns a PolicyOptimizer instance given (WorkerSet, config).
+ after_init (Optional[callable]): Optional callable to run at the end of
+ trainer init that takes the trainer instance as argument.
+ before_train_step (Optional[callable]): Optional callable to run before
+ each train() call. It takes the trainer instance as an argument.
+ after_optimizer_step (Optional[callable]): Optional callable to run
+ after each step() call to the policy optimizer. It takes the
+ trainer instance and the policy gradient fetches as arguments.
+ after_train_result (Optional[callable]): Optional callable to run at
+ the end of each train() call. It takes the trainer instance and
+ result dict as arguments, and may mutate the result dict as needed.
+ collect_metrics_fn (Optional[callable]): Optional callable to override
+ the default method used to collect metrics. Takes the trainer
+ instance as argumnt.
+ before_evaluate_fn (Optional[callable]): Optional callable to run
+ before evaluation. Takes the trainer instance as argument.
+ mixins (Optional[List[class]]): Optional list of mixin class(es) for
+ the returned trainer class. These mixins will be applied in order
+ and will have higher precedence than the Trainer class.
+ training_pipeline (Optional[callable]): Experimental support for custom
training pipelines. This overrides `make_policy_optimizer`.
Returns:
@@ -92,20 +95,26 @@ def __init__(self, config=None, env=None, logger_creator=None):
def _init(self, config, env_creator):
if validate_config:
validate_config(config)
+
if get_initial_state:
self.state = get_initial_state(self)
else:
self.state = {}
- if get_policy_class is None:
- policy = default_policy
- else:
- policy = get_policy_class(config)
+
+ # Override default policy if `get_policy_class` is provided.
+ if get_policy_class is not None:
+ self._policy = get_policy_class(config)
+
if before_init:
before_init(self)
+
+ # Creating all workers (excluding evaluation workers).
if make_workers:
- self.workers = make_workers(self, env_creator, policy, config)
+ self.workers = make_workers(self, env_creator, self._policy,
+ config)
else:
- self.workers = self._make_workers(env_creator, policy, config,
+ self.workers = self._make_workers(env_creator, self._policy,
+ config,
self.config["num_workers"])
self.train_pipeline = None
self.optimizer = None
| [rllib] Evaluation doesn’t work properly for PyTorch.
This is not a contribution.
Ray version: 0.8.1
Python version: 3.6.8
Pytorch version: 1.4
OS: Ubuntu 18.04 Docker
Training with Pytorch with evaluation enabled doesn’t work properly as it ends up calling tensorflow code. Here's a script that reproduces the problem:
```python
import gym
from gym.spaces import Box
from ray import tune
class DummyEnv(gym.Env):
def __init__(self, config):
self.action_space = Box(0.0, 1.0, shape=(1,))
self.observation_space = Box(0.0, 1.0, shape=(1, ))
def reset(self):
self.num_steps = 0
return [0.0]
def step(self, action):
self.num_steps += 1
return [0.0], 1.0, True if self.num_steps == 30 else False, {}
tune.run(
"PPO",
config={
"env": DummyEnv,
"use_pytorch": True,
"num_workers": 1,
"evaluation_interval": 1,
"evaluation_config": {"seed": 1},
}
)
```
This will result in the following exception:
```
ray.exceptions.RayTaskError(AssertionError): ray::PPO.train() (pid=33990)
File "python/ray/_raylet.pyx", line 452, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 430, in ray._raylet.execute_task.function_executor
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py", line 513, in train
evaluation_metrics = self._evaluate()
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py", line 685, in _evaluate
lambda w: w.restore(ray.get(weights)))
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/worker_set.py", line 110, in foreach_worker
local_result = [func(self.local_worker())]
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/agents/trainer.py", line 685, in <lambda>
lambda w: w.restore(ray.get(weights)))
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 764, in restore
self.policy_map[pid].set_state(state)
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/policy/policy.py", line 320, in set_state
self.set_weights(state)
File "/usr/local/lib/python3.6/dist-packages/ray/rllib/policy/tf_policy.py", line 290, in set_weights
return self._variables.set_weights(weights)
File "/usr/local/lib/python3.6/dist-packages/ray/experimental/tf_utils.py", line 185, in set_weights
assert assign_list, ("No variables in the input matched those in the "
AssertionError: No variables in the input matched those in the network. Possible cause: Two networks were defined in the same TensorFlow graph. To fix this, place each network definition in its own tf.Graph.
```
| This is a bug (seems to have been there for a while). Fixing this now ... | 2020-03-04T09:46:15 |
|
ray-project/ray | 7,444 | ray-project__ray-7444 | [
"7136"
] | 476b5c6196fa734794e395a53d2506e7c8485d12 | diff --git a/rllib/agents/ars/ars.py b/rllib/agents/ars/ars.py
--- a/rllib/agents/ars/ars.py
+++ b/rllib/agents/ars/ars.py
@@ -165,8 +165,7 @@ def _init(self, config, env_creator):
# PyTorch check.
if config["use_pytorch"]:
raise ValueError(
- "ARS does not support PyTorch yet! Use tf instead."
- )
+ "ARS does not support PyTorch yet! Use tf instead.")
env = env_creator(config["env_config"])
from ray.rllib import models
@@ -301,7 +300,7 @@ def _stop(self):
w.__ray_terminate__.remote()
@override(Trainer)
- def compute_action(self, observation):
+ def compute_action(self, observation, *args, **kwargs):
return self.policy.compute(observation, update=True)[0]
def _collect_results(self, theta_id, min_episodes):
diff --git a/rllib/agents/es/es.py b/rllib/agents/es/es.py
--- a/rllib/agents/es/es.py
+++ b/rllib/agents/es/es.py
@@ -171,8 +171,7 @@ def _init(self, config, env_creator):
# PyTorch check.
if config["use_pytorch"]:
raise ValueError(
- "ES does not support PyTorch yet! Use tf instead."
- )
+ "ES does not support PyTorch yet! Use tf instead.")
policy_params = {"action_noise_std": 0.01}
@@ -292,7 +291,7 @@ def _train(self):
return result
@override(Trainer)
- def compute_action(self, observation):
+ def compute_action(self, observation, *args, **kwargs):
return self.policy.compute(observation, update=False)[0]
@override(Trainer)
diff --git a/rllib/rollout.py b/rllib/rollout.py
--- a/rllib/rollout.py
+++ b/rllib/rollout.py
@@ -15,6 +15,7 @@
from ray.rllib.env import MultiAgentEnv
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.evaluation.episode import _flatten_action
+from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.deprecation import deprecation_warning
from ray.tune.utils import merge_dicts
@@ -339,7 +340,7 @@ def rollout(agent,
if saver is None:
saver = RolloutSaver()
- if hasattr(agent, "workers"):
+ if hasattr(agent, "workers") and isinstance(agent.workers, WorkerSet):
env = agent.workers.local_worker().env
multiagent = isinstance(env, MultiAgentEnv)
if agent.workers.local_worker().multiagent:
@@ -349,15 +350,22 @@ def rollout(agent,
policy_map = agent.workers.local_worker().policy_map
state_init = {p: m.get_initial_state() for p, m in policy_map.items()}
use_lstm = {p: len(s) > 0 for p, s in state_init.items()}
- action_init = {
- p: _flatten_action(m.action_space.sample())
- for p, m in policy_map.items()
- }
else:
env = gym.make(env_name)
multiagent = False
+ try:
+ policy_map = {DEFAULT_POLICY_ID: agent.policy}
+ except AttributeError:
+ raise AttributeError(
+ "Agent ({}) does not have a `policy` property! This is needed "
+ "for performing (trained) agent rollouts.".format(agent))
use_lstm = {DEFAULT_POLICY_ID: False}
+ action_init = {
+ p: _flatten_action(m.action_space.sample())
+ for p, m in policy_map.items()
+ }
+
# If monitoring has been requested, manually wrap our environment with a
# gym monitor, which is set to record every episode.
if video_dir:
| [RLlib] Cannot rollout ES after training.
Using ray 0.8.1.
Having trained an agent with ES:
```
rllib train --run=ES --env=LunarLander-v2 --checkpoint-freq=10
```
I can't play it. (It works with PPO).
Executing this:
```
rllib rollout checkpoint_50/checkpoint-50 --run ES --env LunarLander-v2 --steps 600
```
results in:
```
2020-02-12 05:41:30,922 INFO trainable.py:423 -- Current state after restoring: {'_iteration': 50, '_timesteps_total': 16979666, '_time_total': 2578.4048051834106, '_episodes_total': None}
Traceback (most recent call last):
File "/home/andriy/miniconda3/envs/myproj/bin/rllib", line 8, in <module>
sys.exit(cli())
File "/home/andriy/miniconda3/envs/myproj/lib/python3.7/site-packages/ray/rllib/scripts.py", line 36, in cli
rollout.run(options, rollout_parser)
File "/home/andriy/miniconda3/envs/myproj/lib/python3.7/site-packages/ray/rllib/rollout.py", line 265, in run
args.no_render, args.monitor)
File "/home/andriy/miniconda3/envs/myproj/lib/python3.7/site-packages/ray/rllib/rollout.py", line 364, in rollout
prev_action=prev_actions[agent_id],
File "/home/andriy/miniconda3/envs/myproj/lib/python3.7/site-packages/ray/rllib/rollout.py", line 272, in __missing__
self[key] = value = self.default_factory(key)
File "/home/andriy/miniconda3/envs/myproj/lib/python3.7/site-packages/ray/rllib/rollout.py", line 340, in <lambda>
lambda agent_id: action_init[mapping_cache[agent_id]])
NameError: free variable 'action_init' referenced before assignment in enclosing scope
```
| I'll take a look. | 2020-03-04T10:20:33 |
|
ray-project/ray | 7,501 | ray-project__ray-7501 | [
"7412"
] | a644060daa4c22aabedcfd9b891d43f9afa88f97 | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -7,7 +7,6 @@
except ImportError:
pd = None
-from ray.tune.checkpoint_manager import Checkpoint
from ray.tune.error import TuneError
from ray.tune.result import EXPR_PROGRESS_FILE, EXPR_PARAM_FILE,\
CONFIG_PREFIX, TRAINING_ITERATION
@@ -43,7 +42,6 @@ def dataframe(self, metric=None, mode=None):
metric (str): Key for trial info to order on.
If None, uses last result.
mode (str): One of [min, max].
-
"""
rows = self._retrieve_rows(metric=metric, mode=mode)
all_configs = self.get_all_configs(prefix=True)
@@ -59,7 +57,6 @@ def get_best_config(self, metric, mode="max"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
-
"""
rows = self._retrieve_rows(metric=metric, mode=mode)
all_configs = self.get_all_configs()
@@ -73,7 +70,6 @@ def get_best_logdir(self, metric, mode="max"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
-
"""
df = self.dataframe(metric=metric, mode=mode)
if mode == "max":
@@ -98,7 +94,7 @@ def fetch_trial_dataframes(self):
def get_all_configs(self, prefix=False):
"""Returns a list of all configurations.
- Parameters:
+ Args:
prefix (bool): If True, flattens the config dict
and prepends `config/`.
"""
@@ -120,32 +116,30 @@ def get_all_configs(self, prefix=False):
return self._configs
def get_trial_checkpoints_paths(self, trial, metric=TRAINING_ITERATION):
- """Returns a list of [path, metric] lists for all disk checkpoints of
- a trial.
+ """Gets paths and metrics of all persistent checkpoints of a trial.
- Arguments:
- trial(Trial): The log directory of a trial, or a trial instance.
+ Args:
+ trial (Trial): The log directory of a trial, or a trial instance.
metric (str): key for trial info to return, e.g. "mean_accuracy".
"training_iteration" is used by default.
- """
+ Returns:
+ A list of [path, metric] lists for all persistent checkpoints of
+ the trial.
+ """
if isinstance(trial, str):
trial_dir = os.path.expanduser(trial)
-
- # get checkpoints from logdir
+ # Get checkpoints from logdir.
chkpt_df = TrainableUtil.get_checkpoints_paths(trial_dir)
- # join with trial dataframe to get metrics
+ # Join with trial dataframe to get metrics.
trial_df = self.trial_dataframes[trial_dir]
path_metric_df = chkpt_df.merge(
trial_df, on="training_iteration", how="inner")
return path_metric_df[["chkpt_path", metric]].values.tolist()
elif isinstance(trial, Trial):
checkpoints = trial.checkpoint_manager.best_checkpoints()
- # TODO(ujvl): Remove condition once the checkpoint manager is
- # modified to only track PERSISTENT checkpoints.
- return [[c.value, c.result[metric]] for c in checkpoints
- if c.storage == Checkpoint.PERSISTENT]
+ return [[c.value, c.result[metric]] for c in checkpoints]
else:
raise ValueError("trial should be a string or a Trial instance.")
@@ -198,7 +192,8 @@ def __init__(self, experiment_checkpoint_path, trials=None):
"""Initializer.
Args:
- experiment_path (str): Path to where experiment is located.
+ experiment_checkpoint_path (str): Path to where experiment is
+ located.
trials (list|None): List of trials that can be accessed via
`analysis.trials`.
"""
diff --git a/python/ray/tune/trial.py b/python/ray/tune/trial.py
--- a/python/ray/tune/trial.py
+++ b/python/ray/tune/trial.py
@@ -205,10 +205,9 @@ def __init__(self,
self.checkpoint_manager = CheckpointManager(
keep_checkpoints_num, checkpoint_score_attr,
checkpoint_deleter(self._trainable_name(), self.runner))
- checkpoint = Checkpoint(Checkpoint.PERSISTENT, restore_path)
- self.checkpoint_manager.newest_persistent_checkpoint = checkpoint
# Restoration fields
+ self.restore_path = restore_path
self.restoring_from = None
self.num_failures = 0
@@ -243,8 +242,10 @@ def checkpoint(self):
if self.status == Trial.PAUSED:
assert self.checkpoint_manager.newest_memory_checkpoint.value
return self.checkpoint_manager.newest_memory_checkpoint
- else:
- return self.checkpoint_manager.newest_persistent_checkpoint
+ checkpoint = self.checkpoint_manager.newest_persistent_checkpoint
+ if checkpoint.value is None:
+ checkpoint = Checkpoint(Checkpoint.PERSISTENT, self.restore_path)
+ return checkpoint
@classmethod
def generate_id(cls):
| diff --git a/python/ray/tune/tests/test_trial_runner_2.py b/python/ray/tune/tests/test_trial_runner_2.py
--- a/python/ray/tune/tests/test_trial_runner_2.py
+++ b/python/ray/tune/tests/test_trial_runner_2.py
@@ -239,7 +239,6 @@ def testRestoreMetricsAfterCheckpointing(self):
runner.step() # Start trial
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
- # checkpoint = runner.trial_executor.save(trials[0])
runner.step() # Process result, dispatch save
runner.step() # Process save
runner.trial_executor.stop_trial(trials[0])
diff --git a/python/ray/tune/tests/test_tune_restore.py b/python/ray/tune/tests/test_tune_restore.py
--- a/python/ray/tune/tests/test_tune_restore.py
+++ b/python/ray/tune/tests/test_tune_restore.py
@@ -58,6 +58,22 @@ def testTuneRestore(self):
},
)
+ def testPostRestoreCheckpointExistence(self):
+ """Tests that checkpoint restored from is not deleted post-restore."""
+ self.assertTrue(os.path.isfile(self.checkpoint_path))
+ tune.run(
+ "PG",
+ name="TuneRestoreTest",
+ stop={"training_iteration": 2},
+ checkpoint_freq=1,
+ keep_checkpoints_num=1,
+ restore=self.checkpoint_path,
+ config={
+ "env": "CartPole-v0",
+ },
+ )
+ self.assertTrue(os.path.isfile(self.checkpoint_path))
+
class TuneExampleTest(unittest.TestCase):
def setUp(self):
| "restore" deletes the restored from checkpoint
If I specify a checkpoint in the `tune.run(DQNTrainer, restore=X)` - the checkpoint `X` gets deleted!
So if there is a bug or something during the initialization, I loose my checkpoint and can no longer restart from it!
| When I restore from a checkpoint in the following way, the checkpoint is not deleted for me:
`tune.run("DDPG", config=config, restore=checkpoint_path)`
I'm on ray v0.8.2 and tensorflow 2.0.0
**EDIT** nvm also have this problem, didn't check/test properly
Hm... are you setting check-pointing to be done during this trial? E.g.:
```
tune.run(DQNTrainer,
restore=checkpoint,
checkpoint_freq=5, checkpoint_at_end=True, checkpoint_score_attr='episode_reward_mean', keep_checkpoints_num=10)
```
Because when I restore it starts a new trial and starts saving checkpoints in a different trial directory (under `~/ray_results`), but for some reason it deletes the last checkpoint from the old directory.
cc @ujvl
@drozzy thanks for flagging this, will push a fix this weekend. | 2020-03-07T23:34:33 |
ray-project/ray | 7,567 | ray-project__ray-7567 | [
"7310"
] | b38ed4be71d4be096f324c8c666e01e8af5f6c8b | diff --git a/python/ray/state.py b/python/ray/state.py
--- a/python/ray/state.py
+++ b/python/ray/state.py
@@ -318,9 +318,9 @@ def _actor_table(self, actor_id):
return {}
gcs_entries = gcs_utils.GcsEntry.FromString(message)
- assert len(gcs_entries.entries) == 1
+ assert len(gcs_entries.entries) > 0
actor_table_data = gcs_utils.ActorTableData.FromString(
- gcs_entries.entries[0])
+ gcs_entries.entries[-1])
actor_info = {
"ActorID": binary_to_hex(actor_table_data.actor_id),
| diff --git a/python/ray/tests/test_global_state.py b/python/ray/tests/test_global_state.py
--- a/python/ray/tests/test_global_state.py
+++ b/python/ray/tests/test_global_state.py
@@ -76,6 +76,36 @@ def test_add_remove_cluster_resources(ray_start_cluster_head):
assert ray.cluster_resources()["CPU"] == 6
+def test_global_state_actor_table(ray_start_regular):
+ @ray.remote
+ class Actor:
+ def ready(self):
+ pass
+
+ # actor table should be empty at first
+ assert len(ray.actors()) == 0
+
+ # actor table should contain only one entry
+ a = Actor.remote()
+ ray.get(a.ready.remote())
+ assert len(ray.actors()) == 1
+
+ # actor table should contain only this entry
+ # even when the actor goes out of scope
+ del a
+
+ def get_state():
+ return list(ray.actors().values())[0]["State"]
+
+ dead_state = ray.gcs_utils.ActorTableData.DEAD
+ for _ in range(10):
+ if get_state() == dead_state:
+ break
+ else:
+ time.sleep(0.5)
+ assert get_state() == dead_state
+
+
if __name__ == "__main__":
import pytest
import sys
| [dashboard] dashboard.py crashes in example where actors are quickly created and go out of scope.
### What is the problem?
I'm using
- `ray==0.8.2`
- Python 3.7.2
- MacOS
### Reproduction (REQUIRED)
#### Bug 1
This is likely somewhat timing dependent, but when I run the following script
```python
import ray
ray.init()
@ray.remote
class GrandChild:
def __init__(self):
pass
@ray.remote
class Child:
def __init__(self):
grand_child_handle = GrandChild.remote()
@ray.remote
class Parent:
def __init__(self):
children_handles = [Child.remote() for _ in range(2)]
parent_handle = Parent.remote()
```
the `dashboard.py` process crashes with the following error.
```
Traceback (most recent call last):
File "/Users/rkn/opt/anaconda3/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/Users/rkn/opt/anaconda3/lib/python3.7/site-packages/ray/dashboard/dashboard.py", line 546, in run
current_actor_table = ray.actors()
File "/Users/rkn/opt/anaconda3/lib/python3.7/site-packages/ray/state.py", line 1133, in actors
return state.actor_table(actor_id=actor_id)
File "/Users/rkn/opt/anaconda3/lib/python3.7/site-packages/ray/state.py", line 369, in actor_table
ray.ActorID(actor_id_binary))
File "/Users/rkn/opt/anaconda3/lib/python3.7/site-packages/ray/state.py", line 321, in _actor_table
assert len(gcs_entries.entries) == 1
AssertionError
```
#### Bug 2
As a consequence, the dashboard doesn't display anything.
There is a second bug here, which is that the error message wasn't pushed to the driver or displayed in the UI in any way.
We have a test testing that tests that an error message is pushed to the driver when the monitor process dies. https://github.com/ray-project/ray/blob/007333b96006e090b14a415a8c5993d8b907f02a/python/ray/tests/test_failure.py#L544
I thought we had a similar test for the dashboard, but I don't see it anywhere. Note that we appear to have the plumbing in place to push error messages to the driver when the dashboard process dies, but I didn't see the relevant error message. See https://github.com/ray-project/ray/blob/f2faf8d26e489e0d879fd691abddeb3140c86bee/python/ray/dashboard/dashboard.py#L928-L929
cc @pcmoritz @simon-mo @mitchellstern
| 2020-03-11T21:36:29 |
|
ray-project/ray | 7,662 | ray-project__ray-7662 | [
"7645"
] | 90b553ed058a546e036374cd0919e00604892514 | diff --git a/rllib/policy/eager_tf_policy.py b/rllib/policy/eager_tf_policy.py
--- a/rllib/policy/eager_tf_policy.py
+++ b/rllib/policy/eager_tf_policy.py
@@ -5,7 +5,6 @@
import logging
import functools
import numpy as np
-import tree
from ray.util.debug import log_once
from ray.rllib.evaluation.episode import _flatten_action
@@ -24,12 +23,12 @@
def _convert_to_tf(x):
if isinstance(x, SampleBatch):
x = {k: v for k, v in x.items() if k != SampleBatch.INFOS}
- return tree.map_structure(_convert_to_tf, x)
+ return tf.nest.map_structure(_convert_to_tf, x)
if isinstance(x, Policy):
return x
if x is not None:
- x = tree.map_structure(
+ x = tf.nest.map_structure(
lambda f: tf.convert_to_tensor(f) if f is not None else None, x)
return x
@@ -38,7 +37,7 @@ def _convert_to_numpy(x):
if x is None:
return None
try:
- return tree.map_structure(lambda component: component.numpy(), x)
+ return tf.nest.map_structure(lambda component: component.numpy(), x)
except AttributeError:
raise TypeError(
("Object of type {} has no method to convert to numpy.").format(
@@ -66,7 +65,7 @@ def convert_eager_outputs(func):
def _func(*args, **kwargs):
out = func(*args, **kwargs)
if tf.executing_eagerly():
- out = tree.map_structure(_convert_to_numpy, out)
+ out = tf.nest.map_structure(_convert_to_numpy, out)
return out
return _func
@@ -551,7 +550,7 @@ def _initialize_loss_with_dummy_batch(self):
SampleBatch.NEXT_OBS: np.array(
[self.observation_space.sample()]),
SampleBatch.DONES: np.array([False], dtype=np.bool),
- SampleBatch.ACTIONS: tree.map_structure(
+ SampleBatch.ACTIONS: tf.nest.map_structure(
lambda c: np.array([c]), self.action_space.sample()),
SampleBatch.REWARDS: np.array([0], dtype=np.float32),
}
@@ -568,7 +567,8 @@ def _initialize_loss_with_dummy_batch(self):
dummy_batch["seq_lens"] = np.array([1], dtype=np.int32)
# Convert everything to tensors.
- dummy_batch = tree.map_structure(tf.convert_to_tensor, dummy_batch)
+ dummy_batch = tf.nest.map_structure(tf.convert_to_tensor,
+ dummy_batch)
# for IMPALA which expects a certain sample batch size.
def tile_to(tensor, n):
@@ -576,7 +576,7 @@ def tile_to(tensor, n):
[n] + [1 for _ in tensor.shape.as_list()[1:]])
if get_batch_divisibility_req:
- dummy_batch = tree.map_structure(
+ dummy_batch = tf.nest.map_structure(
lambda c: tile_to(c, get_batch_divisibility_req(self)),
dummy_batch)
@@ -595,7 +595,7 @@ def tile_to(tensor, n):
# overwrite any tensor state from that call)
self.model.from_batch(dummy_batch)
- postprocessed_batch = tree.map_structure(
+ postprocessed_batch = tf.nest.map_structure(
lambda c: tf.convert_to_tensor(c), postprocessed_batch.data)
loss_fn(self, self.model, self.dist_class, postprocessed_batch)
diff --git a/rllib/policy/tf_policy.py b/rllib/policy/tf_policy.py
--- a/rllib/policy/tf_policy.py
+++ b/rllib/policy/tf_policy.py
@@ -1,7 +1,6 @@
import errno
import logging
import os
-import tree
import numpy as np
import ray
@@ -212,10 +211,8 @@ def _initialize_loss(self, loss, loss_inputs):
self._loss = loss
self._optimizer = self.optimizer()
- self._grads_and_vars = [
- (g, v) for (g, v) in self.gradients(self._optimizer, self._loss)
- if g is not None
- ]
+ self._grads_and_vars = [(g, v) for (g, v) in self.gradients(
+ self._optimizer, self._loss) if g is not None]
self._grads = [g for (g, v) in self._grads_and_vars]
# TODO(sven/ekl): Deprecate support for v1 models.
@@ -493,7 +490,7 @@ def _build_signature_def(self):
# build output signatures
output_signature = self._extra_output_signature_def()
- for i, a in enumerate(tree.flatten(self._sampled_action)):
+ for i, a in enumerate(tf.nest.flatten(self._sampled_action)):
output_signature["actions_{}".format(i)] = \
tf.saved_model.utils.build_tensor_info(a)
diff --git a/rllib/utils/torch_ops.py b/rllib/utils/torch_ops.py
--- a/rllib/utils/torch_ops.py
+++ b/rllib/utils/torch_ops.py
@@ -1,9 +1,17 @@
-import tree
+import logging
from ray.rllib.utils.framework import try_import_torch
torch, _ = try_import_torch()
+logger = logging.getLogger(__name__)
+
+try:
+ import tree
+except (ImportError, ModuleNotFoundError) as e:
+ logger.warning("`dm-tree` is not installed! Run `pip install dm-tree`.")
+ raise e
+
def sequence_mask(lengths, maxlen, dtype=None):
"""
@@ -34,6 +42,7 @@ def convert_to_non_torch_type(stats):
dict: A new dict with the same structure as stats_dict, but with all
values converted to non-torch Tensor types.
"""
+
# The mapping function used to numpyize torch Tensors.
def mapping(item):
if isinstance(item, torch.Tensor):
| diff --git a/rllib/policy/tests/test_compute_log_likelihoods.py b/rllib/policy/tests/test_compute_log_likelihoods.py
--- a/rllib/policy/tests/test_compute_log_likelihoods.py
+++ b/rllib/policy/tests/test_compute_log_likelihoods.py
@@ -1,5 +1,6 @@
import numpy as np
from scipy.stats import norm
+from tensorflow.python.eager.context import eager_mode
import unittest
import ray.rllib.agents.dqn as dqn
@@ -43,6 +44,14 @@ def do_test_log_likelihood(run,
config["eager"] = fw == "eager"
config["use_pytorch"] = fw == "torch"
+ eager_ctx = None
+ if fw == "eager":
+ eager_ctx = eager_mode()
+ eager_ctx.__enter__()
+ assert tf.executing_eagerly()
+ elif fw == "tf":
+ assert not tf.executing_eagerly()
+
trainer = run(config=config, env=env)
policy = trainer.get_policy()
vars = policy.get_weights()
@@ -104,6 +113,9 @@ def do_test_log_likelihood(run,
prev_reward_batch=np.array([prev_r]))
check(np.exp(logp), expected_prob, atol=0.2)
+ if eager_ctx:
+ eager_ctx.__exit__(None, None, None)
+
class TestComputeLogLikelihood(unittest.TestCase):
def test_dqn(self):
| [rllib] ModuleNotFoundError: No module named 'tree'
*Ray version and other system information (Python version, TensorFlow version, OS):*
Python3.6
pip install -U ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl
On 2020-03-18
### Reproduction (REQUIRED)
$ rllib train -f atari-ddppo.yaml
Traceback (most recent call last):
File "/home/simon/anaconda3/bin/rllib", line 5, in <module>
from ray.rllib.scripts import cli
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/__init__.py", line 9, in <module>
from ray.rllib.evaluation.policy_graph import PolicyGraph
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/evaluation/__init__.py", line 2, in <module>
from ray.rllib.evaluation.rollout_worker import RolloutWorker
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/evaluation/rollout_worker.py", line 19, in <module>
from ray.rllib.evaluation.sampler import AsyncSampler, SyncSampler
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/evaluation/sampler.py", line 11, in <module>
from ray.rllib.evaluation.sample_batch_builder import \
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/evaluation/sample_batch_builder.py", line 6, in <module>
from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/policy/__init__.py", line 2, in <module>
from ray.rllib.policy.torch_policy import TorchPolicy
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/policy/torch_policy.py", line 10, in <module>
from ray.rllib.utils.torch_ops import convert_to_non_torch_type
File "/home/simon/anaconda3/lib/python3.6/site-packages/ray/rllib/utils/torch_ops.py", line 1, in <module>
import tree
ModuleNotFoundError: No module named 'tree'
| 2020-03-19T19:21:25 |
|
ray-project/ray | 7,665 | ray-project__ray-7665 | [
"7663"
] | 90b553ed058a546e036374cd0919e00604892514 | diff --git a/python/setup.py b/python/setup.py
--- a/python/setup.py
+++ b/python/setup.py
@@ -180,7 +180,6 @@ def find_version(*filepath):
"packaging",
"pytest",
"pyyaml",
- "jsonschema",
"redis>=3.3.2",
# NOTE: Don't upgrade the version of six! Doing so causes installation
# problems. See https://github.com/ray-project/ray/issues/4169.
| [Python] jsonschema included twice in setup.py requires list.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
`jsonschema` is included twice in the Python package [setup.py `requires` list](https://github.com/ray-project/ray/blob/master/python/setup.py#L176-L183). This is causing the usage of the Ray Python library within Bazel to fail during the analysis phase due to label duplication in the generated `py_library` target's `'deps'`:
```
ERROR: .../external/requirements_py3_pypi__ray_0_9_0_dev0/BUILD:6:1: Label '@requirements_py3_pypi__jsonschema_3_2_0//:pkg' is duplicated in the 'deps' attribute of rule 'pkg'
```
This bug was introduced in the [cluster json schema validator PR](https://github.com/ray-project/ray/pull/7261/files#diff-8cf6167d58ce775a08acafcfe6f40966).
*Ray version and other system information (Python version, TensorFlow version, OS):*
Ray master commit 90b553ed058a546e036374cd0919e00604892514 (most recent commit as of this issue filing)
### Reproduction (REQUIRED)
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| 2020-03-19T19:43:14 |
||
ray-project/ray | 7,705 | ray-project__ray-7705 | [
"7695"
] | 8b4f5a9431fed9454ff116d7370c72bcbb46fbd9 | diff --git a/python/ray/tune/logger.py b/python/ray/tune/logger.py
--- a/python/ray/tune/logger.py
+++ b/python/ray/tune/logger.py
@@ -239,9 +239,10 @@ def flush(self):
def close(self):
if self._file_writer is not None:
if self.trial and self.trial.evaluated_params and self.last_result:
+ flat_result = flatten_dict(self.last_result, delimiter="/")
scrubbed_result = {
k: value
- for k, value in self.last_result.items()
+ for k, value in flat_result.items()
if type(value) in VALID_SUMMARY_TYPES
}
self._try_log_hparams(scrubbed_result)
@@ -249,9 +250,10 @@ def close(self):
def _try_log_hparams(self, result):
# TBX currently errors if the hparams value is None.
+ flat_params = flatten_dict(self.trial.evaluated_params)
scrubbed_params = {
k: v
- for k, v in self.trial.evaluated_params.items() if v is not None
+ for k, v in flat_params.items() if v is not None
}
from tensorboardX.summary import hparams
experiment_tag, session_start_tag, session_end_tag = hparams(
diff --git a/python/ray/tune/trainable.py b/python/ray/tune/trainable.py
--- a/python/ray/tune/trainable.py
+++ b/python/ray/tune/trainable.py
@@ -178,7 +178,7 @@ def __init__(self, config=None, logger_creator=None):
"slow to initialize, consider setting "
"reuse_actors=True to reduce actor creation "
"overheads.".format(setup_time))
- self._local_ip = ray.services.get_node_ip_address()
+ self._local_ip = self.get_current_ip()
log_sys_usage = self.config.get("log_sys_usage", False)
self._monitor = UtilMonitor(start=log_sys_usage)
@@ -213,7 +213,7 @@ def resource_help(cls, config):
"""
return ""
- def current_ip(self):
+ def get_current_ip(self):
logger.info("Getting current IP.")
self._local_ip = ray.services.get_node_ip_address()
return self._local_ip
@@ -419,8 +419,8 @@ def restore(self, checkpoint_path):
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = True
- logger.info("Restored on %s from checkpoint: %s", self.current_ip(),
- checkpoint_path)
+ logger.info("Restored on %s from checkpoint: %s",
+ self.get_current_ip(), checkpoint_path)
state = {
"_iteration": self._iteration,
"_timesteps_total": self._timesteps_total,
| diff --git a/python/ray/tune/tests/test_logger.py b/python/ray/tune/tests/test_logger.py
--- a/python/ray/tune/tests/test_logger.py
+++ b/python/ray/tune/tests/test_logger.py
@@ -28,30 +28,30 @@ def tearDown(self):
shutil.rmtree(self.test_dir, ignore_errors=True)
def testCSV(self):
- config = {"a": 2, "b": 5}
+ config = {"a": 2, "b": 5, "c": {"c": {"D": 123}, "e": None}}
t = Trial(evaluated_params=config, trial_id="csv")
logger = CSVLogger(config=config, logdir=self.test_dir, trial=t)
logger.on_result(result(2, 4))
logger.on_result(result(2, 4))
- logger.on_result(result(2, 4, score=[1, 2, 3]))
+ logger.on_result(result(2, 4, score=[1, 2, 3], hello={"world": 1}))
logger.close()
def testJSON(self):
- config = {"a": 2, "b": 5}
+ config = {"a": 2, "b": 5, "c": {"c": {"D": 123}, "e": None}}
t = Trial(evaluated_params=config, trial_id="json")
logger = JsonLogger(config=config, logdir=self.test_dir, trial=t)
logger.on_result(result(0, 4))
logger.on_result(result(1, 4))
- logger.on_result(result(2, 4, score=[1, 2, 3]))
+ logger.on_result(result(2, 4, score=[1, 2, 3], hello={"world": 1}))
logger.close()
def testTBX(self):
- config = {"a": 2, "b": 5}
+ config = {"a": 2, "b": 5, "c": {"c": {"D": 123}, "e": None}}
t = Trial(evaluated_params=config, trial_id="tbx")
logger = TBXLogger(config=config, logdir=self.test_dir, trial=t)
logger.on_result(result(0, 4))
logger.on_result(result(1, 4))
- logger.on_result(result(2, 4, score=[1, 2, 3]))
+ logger.on_result(result(2, 4, score=[1, 2, 3], hello={"world": 1}))
logger.close()
| [tune] Reporter fails with nested config keys and {'done': True}
### What is the problem?
When a `Trainable` reports `{'done': True}` with some other nested values, the tensorboard logger fails with `NotImplementedError: Got <class 'dict'>, but expected numpy array or torch tensor.`. The same report works for all the preceding logs that don't include `{'done': True}` though.
*Ray version and other system information (Python version, TensorFlow version, OS):* `0.8.2`
### Reproduction (REQUIRED)
```python
from ray import tune
class MyTrainableClass(tune.Trainable):
def _setup(self, config):
self.timestep = 0
def _train(self):
self.timestep += 1
result = {"episode_reward_mean": self.timestep}
result['what'] = {'1': '2', '3': 4, '5': {'6': 4}}
if self.timestep == 3:
result['done'] = True
return result
tune.run(
MyTrainableClass,
name="done-test",
num_samples=1,
config={
'b': {'c': {'d': '4'}},
'a': {
'b': tune.sample_from(lambda spec: spec['config']['b']['c']),
},
})
```
Fails with:
```
$ python ./tests/tune_done_test.py
2020-03-22 12:17:21,455 INFO resource_spec.py:212 -- Starting Ray with 10.3 GiB memory available for workers and up to 5.17 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2020-03-22 12:17:21,746 INFO services.py:1078 -- View the Ray dashboard at localhost:8265
== Status ==
Memory usage on this node: 17.8/32.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 1/16 CPUs, 0/0 GPUs, 0.0/10.3 GiB heap, 0.0/3.56 GiB objects
Result logdir: /Users/hartikainen/ray_results/done-test
Number of trials: 1 (1 RUNNING)
+---------------------------+----------+-------+-------+
| Trial name | status | loc | a/b |
|---------------------------+----------+-------+-------|
| MyTrainableClass_14fda478 | RUNNING | | |
+---------------------------+----------+-------+-------+
Result for MyTrainableClass_14fda478:
date: 2020-03-22_12-17-23
done: false
episode_reward_mean: 1
experiment_id: 3549c7334c884d3abc309232da4f6679
experiment_tag: '0_b={''d'': ''4''}'
hostname: catz-48fe.stcatz.ox.ac.uk
iterations_since_restore: 1
node_ip: 129.67.48.254
pid: 73820
time_since_restore: 3.0994415283203125e-06
time_this_iter_s: 3.0994415283203125e-06
time_total_s: 3.0994415283203125e-06
timestamp: 1584879443
timesteps_since_restore: 0
training_iteration: 1
trial_id: 14fda478
what:
'1': '2'
'3': 4
'5':
'6': 4
Result for MyTrainableClass_14fda478:
date: 2020-03-22_12-17-23
done: true
episode_reward_mean: 3
experiment_id: 3549c7334c884d3abc309232da4f6679
experiment_tag: '0_b={''d'': ''4''}'
hostname: catz-48fe.stcatz.ox.ac.uk
iterations_since_restore: 3
node_ip: 129.67.48.254
pid: 73820
time_since_restore: 9.775161743164062e-06
time_this_iter_s: 2.86102294921875e-06
time_total_s: 9.775161743164062e-06
timestamp: 1584879443
timesteps_since_restore: 0
training_iteration: 3
trial_id: 14fda478
what:
'1': '2'
'3': 4
'5':
'6': 4
2020-03-22 12:17:23,204 ERROR trial_runner.py:513 -- Trial MyTrainableClass_14fda478: Error processing event.
Traceback (most recent call last):
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 511, in _process_trial
self._execute_action(trial, decision)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 595, in _execute_action
self.trial_executor.stop_trial(trial)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 263, in stop_trial
trial, error=error, error_msg=error_msg, stop_logger=stop_logger)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 204, in _stop_trial
trial.close_logger()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial.py", line 315, in close_logger
self.result_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 305, in close
_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 233, in close
self._try_log_hparams(self.last_result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 244, in _try_log_hparams
hparam_dict=scrubbed_params, metric_dict=result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/summary.py", line 102, in hparams
v = make_np(v)[0]
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/x2num.py", line 34, in make_np
'Got {}, but expected numpy array or torch tensor.'.format(type(x)))
NotImplementedError: Got <class 'dict'>, but expected numpy array or torch tensor.
Traceback (most recent call last):
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 511, in _process_trial
self._execute_action(trial, decision)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 595, in _execute_action
self.trial_executor.stop_trial(trial)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 263, in stop_trial
trial, error=error, error_msg=error_msg, stop_logger=stop_logger)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 204, in _stop_trial
trial.close_logger()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial.py", line 315, in close_logger
self.result_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 305, in close
_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 233, in close
self._try_log_hparams(self.last_result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 244, in _try_log_hparams
hparam_dict=scrubbed_params, metric_dict=result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/summary.py", line 102, in hparams
v = make_np(v)[0]
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/x2num.py", line 34, in make_np
'Got {}, but expected numpy array or torch tensor.'.format(type(x)))
NotImplementedError: Got <class 'dict'>, but expected numpy array or torch tensor.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./tests/tune_done_test.py", line 24, in <module>
'b': tune.sample_from(lambda spec: spec['config']['b']['c']),
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/tune.py", line 324, in run
runner.step()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 335, in step
self._process_events() # blocking
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 444, in _process_events
self._process_trial(trial)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 514, in _process_trial
self._process_trial_failure(trial, traceback.format_exc())
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 580, in _process_trial_failure
trial, error=True, error_msg=error_msg)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 263, in stop_trial
trial, error=error, error_msg=error_msg, stop_logger=stop_logger)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 204, in _stop_trial
trial.close_logger()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/trial.py", line 315, in close_logger
self.result_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 305, in close
_logger.close()
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 233, in close
self._try_log_hparams(self.last_result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/ray/tune/logger.py", line 244, in _try_log_hparams
hparam_dict=scrubbed_params, metric_dict=result)
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/summary.py", line 102, in hparams
v = make_np(v)[0]
File "/Users/hartikainen/conda/envs/softlearning-tf2/lib/python3.7/site-packages/tensorboardX/x2num.py", line 34, in make_np
'Got {}, but expected numpy array or torch tensor.'.format(type(x)))
NotImplementedError: Got <class 'dict'>, but expected numpy array or torch tensor.
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| Actually, this seems to be related to the nested config. If the above script is run with:
```
'b': tune.sample_from(lambda spec: spec['config']['b']['c']['d'])
```
instead of
```
'b': tune.sample_from(lambda spec: spec['config']['b']['c']),
```
it finishes as expected. | 2020-03-23T02:21:38 |
ray-project/ray | 7,752 | ray-project__ray-7752 | [
"7751"
] | ca6eabc9cb728a829d5305a4dd19216ed925f2e4 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -1391,6 +1391,9 @@ def register_custom_serializer(cls,
use_dict: Deprecated.
class_id (str): Unique ID of the class. Autogenerated if None.
"""
+ worker = global_worker
+ worker.check_connected()
+
if use_pickle:
raise DeprecationWarning(
"`use_pickle` is no longer a valid parameter and will be removed "
| AttributeError: 'Worker' object has no attribute 'lock'
the following code
```python
import ray
# ray.init()
class Foo(object):
def __init__(self, value):
self.value = value
def custom_serializer(obj):
return obj.value
def custom_deserializer(value):
object = Foo(value)
return object
ray.register_custom_serializer(
Foo, serializer=custom_serializer, deserializer=custom_deserializer)
object_id = ray.put(Foo(100))
```
trigger an exception when calling `ray.register_custom_serializer`,
```
Traceback (most recent call last):
File "ray_err.py", line 20, in <module>
Foo, serializer=custom_serializer, deserializer=custom_deserializer)
File "/home/cloudhan/ray/python/ray/worker.py", line 1405, in register_custom_serializer
context = global_worker.get_serialization_context()
File "/home/cloudhan/ray/python/ray/worker.py", line 198, in get_serialization_context
with self.lock:
AttributeError: 'Worker' object has no attribute 'lock'
```
inspecting into the code you will find the lock is missing,
https://github.com/ray-project/ray/blob/ca6eabc9cb728a829d5305a4dd19216ed925f2e4/python/ray/worker.py#L182-L202
the reason is simply not initialize ray, but the error is confusing.
| 2020-03-26T10:10:14 |
||
ray-project/ray | 7,758 | ray-project__ray-7758 | [
"7757"
] | e196fcdbaf1a226e8f5ef5f28308f13d9be8261d | diff --git a/rllib/rollout.py b/rllib/rollout.py
--- a/rllib/rollout.py
+++ b/rllib/rollout.py
@@ -11,7 +11,6 @@
import gym
import ray
-from ray.rllib.agents.registry import get_agent_class
from ray.rllib.env import MultiAgentEnv
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.evaluation.episode import _flatten_action
@@ -19,6 +18,7 @@
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.deprecation import deprecation_warning
from ray.tune.utils import merge_dicts
+from ray.tune.registry import get_trainable_cls
EXAMPLE_USAGE = """
Example Usage via RLlib CLI:
@@ -274,7 +274,7 @@ def run(args, parser):
ray.init()
# Create the Trainer from config.
- cls = get_agent_class(args.run)
+ cls = get_trainable_cls(args.run)
agent = cls(env=args.env, config=config)
# Load state from checkpoint.
agent.restore(args.checkpoint)
| [rllib] ray/rllib/rollout.py does not find registered trainables
### What is the problem?
When using rollout.py with custom trainable (registered via `tune.register_trainable`) the script does not find the trainable.
This seems to be caused because rollout.py uses `ray.rllib.agents.registry.get_agent_class` instead of `ray.tune.registry.get_trainable_cls`.
*Ray version and other system information (Python version, TensorFlow version, OS):*
python=3.7.3, ray[rllib,debug]==ray-0.9.0.dev0, tensorflow==1.15.0, Ubuntu 18.04 LTS
### Reproduction
```
#generate checkpoint
rllib train --run DQN --env CartPole-v0 --stop '{"timesteps_total": 5000}' --checkpoint-freq 1
python rollout.py PATH_TO_CHECKPOINT --run OtherDQN --episodes 10 --out rollout.pkl
2020-03-26 16:28:25,858 INFO resource_spec.py:212 -- Starting Ray with 11.62 GiB memory available for workers and up to 5.83 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2020-03-26 16:28:26,332 INFO services.py:1123 -- View the Ray dashboard at localhost:8265
Traceback (most recent call last):
File "rollout.py", line 475, in <module>
run(args, parser)
File "rollout.py", line 285, in run
cls = get_agent_class(args.run)
File "/home/carl/miniconda3/envs/rollout_test/lib/python3.7/site-packages/ray/rllib/agents/registry.py", line 130, in get_agent_class
return _get_agent_class(alg)
File "/home/carl/miniconda3/envs/rollout_test/lib/python3.7/site-packages/ray/rllib/agents/registry.py", line 154, in _get_agent_class
raise Exception(("Unknown algorithm {}.").format(alg))
Exception: Unknown algorithm OtherDQN.
```
The provided rollout.py adds the following lines at the start:
```
from ray.rllib.agents.dqn import DQNTrainer
OtherDQNTrainer = DQNTrainer.with_updates(
name="OtherDQN")
ray.tune.register_trainable("OtherDQN", OtherDQNTrainer)
```
If `ray.rllib.agents.registry.get_agent_class` is replaces with `ray.tune.registry.get_trainable_cls` it works (with both "OtherDQN" and "DQN")
[rollout.zip](https://github.com/ray-project/ray/files/4388161/rollout.zip)
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| 2020-03-26T15:58:21 |
||
ray-project/ray | 7,846 | ray-project__ray-7846 | [
"7843"
] | 1b477c6dd04bb6adf1b910212e5578e18f774bf6 | diff --git a/rllib/agents/ppo/ppo.py b/rllib/agents/ppo/ppo.py
--- a/rllib/agents/ppo/ppo.py
+++ b/rllib/agents/ppo/ppo.py
@@ -67,6 +67,9 @@
# usually slower, but you might want to try it if you run into issues with
# the default optimizer.
"simple_optimizer": False,
+ # Whether to fake GPUs (using CPUs).
+ # Set this to True for debugging on non-GPU machines (set `num_gpus` > 0).
+ "_fake_gpus": False,
# Use PyTorch as framework?
"use_pytorch": False
})
@@ -92,7 +95,8 @@ def choose_policy_optimizer(workers, config):
num_envs_per_worker=config["num_envs_per_worker"],
train_batch_size=config["train_batch_size"],
standardize_fields=["advantages"],
- shuffle_sequences=config["shuffle_sequences"])
+ shuffle_sequences=config["shuffle_sequences"],
+ _fake_gpus=config["_fake_gpus"])
def update_kl(trainer, fetches):
diff --git a/rllib/optimizers/multi_gpu_optimizer.py b/rllib/optimizers/multi_gpu_optimizer.py
--- a/rllib/optimizers/multi_gpu_optimizer.py
+++ b/rllib/optimizers/multi_gpu_optimizer.py
@@ -29,8 +29,9 @@ class LocalMultiGPUOptimizer(PolicyOptimizer):
A number of SGD passes are then taken over the in-memory data. For more
details, see `multi_gpu_impl.LocalSyncParallelOptimizer`.
- This optimizer is Tensorflow-specific and require the underlying
- Policy to be a TFPolicy instance that support `.copy()`.
+ This optimizer is Tensorflow-specific and requires the underlying
+ Policy to be a TFPolicy instance that implements the `copy()` method
+ for multi-GPU tower generation.
Note that all replicas of the TFPolicy will merge their
extra_compute_grad and apply_grad feed_dicts and fetches. This
@@ -46,7 +47,8 @@ def __init__(self,
train_batch_size=1024,
num_gpus=0,
standardize_fields=[],
- shuffle_sequences=True):
+ shuffle_sequences=True,
+ _fake_gpus=False):
"""Initialize a synchronous multi-gpu optimizer.
Arguments:
@@ -62,6 +64,9 @@ def __init__(self,
to normalize
shuffle_sequences (bool): whether to shuffle the train batch prior
to SGD to break up correlations
+ _fake_gpus (bool): Whether to use fake-GPUs (CPUs) instead of
+ actual GPUs (should only be used for testing on non-GPU
+ machines).
"""
PolicyOptimizer.__init__(self, workers)
@@ -71,12 +76,16 @@ def __init__(self,
self.rollout_fragment_length = rollout_fragment_length
self.train_batch_size = train_batch_size
self.shuffle_sequences = shuffle_sequences
+
+ # Collect actual devices to use.
if not num_gpus:
- self.devices = ["/cpu:0"]
- else:
- self.devices = [
- "/gpu:{}".format(i) for i in range(int(math.ceil(num_gpus)))
- ]
+ _fake_gpus = True
+ num_gpus = 1
+ type_ = "cpu" if _fake_gpus else "gpu"
+ self.devices = [
+ "/{}:{}".format(type_, i) for i in range(int(math.ceil(num_gpus)))
+ ]
+
self.batch_size = int(sgd_batch_size / len(self.devices)) * len(
self.devices)
assert self.batch_size % len(self.devices) == 0
diff --git a/rllib/policy/dynamic_tf_policy.py b/rllib/policy/dynamic_tf_policy.py
--- a/rllib/policy/dynamic_tf_policy.py
+++ b/rllib/policy/dynamic_tf_policy.py
@@ -112,6 +112,8 @@ def __init__(self,
prev_actions = existing_inputs[SampleBatch.PREV_ACTIONS]
prev_rewards = existing_inputs[SampleBatch.PREV_REWARDS]
action_input = existing_inputs[SampleBatch.ACTIONS]
+ explore = existing_inputs["is_exploring"]
+ timestep = existing_inputs["timestep"]
else:
obs = tf.placeholder(
tf.float32,
@@ -123,8 +125,9 @@ def __init__(self,
action_space, "prev_action")
prev_rewards = tf.placeholder(
tf.float32, [None], name="prev_reward")
-
- explore = tf.placeholder_with_default(False, (), name="is_exploring")
+ explore = tf.placeholder_with_default(
+ True, (), name="is_exploring")
+ timestep = tf.placeholder(tf.int32, (), name="timestep")
self._input_dict = {
SampleBatch.CUR_OBS: obs,
@@ -175,8 +178,6 @@ def __init__(self,
for s in self.model.get_initial_state()
]
- timestep = tf.placeholder(tf.int32, (), name="timestep")
-
# Fully customized action generation (e.g., custom policy).
if action_sampler_fn:
sampled_action, sampled_action_logp = action_sampler_fn(
@@ -281,8 +282,9 @@ def copy(self, existing_inputs):
existing_inputs[len(self._loss_inputs) + i]))
if rnn_inputs:
rnn_inputs.append(("seq_lens", existing_inputs[-1]))
- input_dict = OrderedDict([(k, existing_inputs[i]) for i, (
- k, _) in enumerate(self._loss_inputs)] + rnn_inputs)
+ input_dict = OrderedDict([("is_exploring", self._is_exploring), (
+ "timestep", self._timestep)] + [(k, existing_inputs[i]) for i, (
+ k, _) in enumerate(self._loss_inputs)] + rnn_inputs)
instance = self.__class__(
self.observation_space,
self.action_space,
| diff --git a/rllib/agents/ppo/tests/test_ppo.py b/rllib/agents/ppo/tests/test_ppo.py
--- a/rllib/agents/ppo/tests/test_ppo.py
+++ b/rllib/agents/ppo/tests/test_ppo.py
@@ -46,6 +46,33 @@ def test_ppo_compilation(self):
for i in range(num_iterations):
trainer.train()
+ def test_ppo_fake_multi_gpu_learning(self):
+ """Test whether PPOTrainer can learn CartPole w/ faked multi-GPU."""
+ config = ppo.DEFAULT_CONFIG.copy()
+ # Fake GPU setup.
+ config["num_gpus"] = 2
+ config["_fake_gpus"] = True
+ # Mimick tuned_example for PPO CartPole.
+ config["num_workers"] = 1
+ config["lr"] = 0.0003
+ config["observation_filter"] = "MeanStdFilter"
+ config["num_sgd_iter"] = 6
+ config["vf_share_layers"] = True
+ config["vf_loss_coeff"] = 0.01
+ config["model"]["fcnet_hiddens"] = [32]
+ config["model"]["fcnet_activation"] = "linear"
+
+ trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
+ num_iterations = 200
+ learnt = False
+ for i in range(num_iterations):
+ results = trainer.train()
+ if results["episode_reward_mean"] > 150:
+ learnt = True
+ break
+ print(results)
+ assert learnt, "PPO multi-GPU (with fake-GPUs) did not learn CartPole!"
+
def test_ppo_exploration_setup(self):
"""Tests, whether PPO runs with different exploration setups."""
config = ppo.DEFAULT_CONFIG.copy()
diff --git a/rllib/tuned_examples/regression_tests/cartpole-ppo-tf-multi-gpu.yaml b/rllib/tuned_examples/regression_tests/cartpole-ppo-tf-multi-gpu.yaml
new file mode 100644
--- /dev/null
+++ b/rllib/tuned_examples/regression_tests/cartpole-ppo-tf-multi-gpu.yaml
@@ -0,0 +1,20 @@
+cartpole-ppo-tf-multi-gpu:
+ env: CartPole-v0
+ run: PPO
+ stop:
+ episode_reward_mean: 150
+ timesteps_total: 100000
+ config:
+ gamma: 0.99
+ lr: 0.0003
+ num_workers: 1
+ observation_filter: MeanStdFilter
+ num_sgd_iter: 6
+ vf_share_layers: true
+ vf_loss_coeff: 0.01
+ model:
+ fcnet_hiddens: [32]
+ fcnet_activation: linear
+ # Use fake-GPU setup to prove towers are working and learning.
+ num_gpus: 6
+ _fake_gpus: true
| [rllib] PPO policy return all zeros for action_logp
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
PPO policy return all zeros when computing the log probability of actions.
ray=0.8.2 (latest)
tensorflow=2.1.0
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```python
import ray
import numpy as np
from ray.rllib.agents.ppo import PPOTrainer
ray.init()
ppo_agent = PPOTrainer(env="BipedalWalker-v2")
ppo_agent.get_policy().get_session().run(
ppo_agent.get_policy()._action_logp,
feed_dict={
ppo_agent.get_policy()._input_dict["obs"]: np.random.random((500, 24))
}
)
```
Result in
```
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.], dtype=float32)
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| The issue still happen in Ray=0.8.3
```python
import ray
import numpy as np
from ray.rllib.agents.ppo import PPOTrainer
print(ray.__version__)
ray.init(ignore_reinit_error=True)
ppo_agent = PPOTrainer(env="BipedalWalker-v2")
ppo_agent.get_policy().get_session().run(
ppo_agent.get_policy()._sampled_action_logp,
feed_dict={
ppo_agent.get_policy()._input_dict["obs"]: np.random.random((500, 24))
}
)
```
Ray 0.8.1 works well! Something wrong with the updates introduced by 0.8.2
I did a bit of digging and it seems related to the is_exploring flag somehow being False for PPO in multi-GPU mode (works fine with simple_optimizer: True). @sven1977
I think this is since the explore placeholder isn't passed to copies in `dynamic_tf_policy.copy()`.
Yeah, that's possible. I'll take a look. | 2020-04-01T06:41:05 |
ray-project/ray | 7,851 | ray-project__ray-7851 | [
"7850"
] | e153e3179f54819e06c07df21bbf49e260dec5f2 | diff --git a/rllib/agents/ddpg/ddpg_policy.py b/rllib/agents/ddpg/ddpg_policy.py
--- a/rllib/agents/ddpg/ddpg_policy.py
+++ b/rllib/agents/ddpg/ddpg_policy.py
@@ -54,6 +54,7 @@ def postprocess_trajectory(self,
feed_dict={
self.cur_observations: states,
self._is_exploring: False,
+ self._timestep: self.global_timestep,
})
distance_in_action_space = np.sqrt(
np.mean(np.square(clean_actions - noisy_actions)))
@@ -414,16 +415,10 @@ def _build_policy_network(self, obs, obs_space, action_space):
activation = getattr(tf.nn, self.config["actor_hidden_activation"])
for hidden in self.config["actor_hiddens"]:
+ action_out = tf.layers.dense(
+ action_out, units=hidden, activation=activation)
if self.config["parameter_noise"]:
- import tensorflow.contrib.layers as layers
- action_out = layers.fully_connected(
- action_out,
- num_outputs=hidden,
- activation_fn=activation,
- normalizer_fn=layers.layer_norm)
- else:
- action_out = tf.layers.dense(
- action_out, units=hidden, activation=activation)
+ action_out = tf.keras.layers.LayerNormalization()(action_out)
action_out = tf.layers.dense(
action_out, units=action_space.shape[0], activation=None)
diff --git a/rllib/agents/dqn/distributional_q_model.py b/rllib/agents/dqn/distributional_q_model.py
--- a/rllib/agents/dqn/distributional_q_model.py
+++ b/rllib/agents/dqn/distributional_q_model.py
@@ -125,14 +125,12 @@ def build_state_score(model_out):
state_out = self._noisy_layer("dueling_hidden_%d" % i,
state_out, q_hiddens[i],
sigma0)
- elif parameter_noise:
- state_out = tf.keras.layers.Dense(
- units=q_hiddens[i],
- activation_fn=tf.nn.relu,
- normalizer_fn=tf.contrib.layers.layer_norm)(state_out)
else:
state_out = tf.keras.layers.Dense(
units=q_hiddens[i], activation=tf.nn.relu)(state_out)
+ if parameter_noise:
+ state_out = tf.keras.layers.LayerNormalization()(
+ state_out)
if use_noisy:
state_score = self._noisy_layer(
"dueling_output",
| [rllib] ModuleNotFoundError: No module named 'tensorflow.contrib'
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
```
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py", line 93, in __init__
Trainer.__init__(self, config, env, logger_creator)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 455, in __init__
super().__init__(config, logger_creator)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/tune/trainable.py", line 174, in __init__
self._setup(copy.deepcopy(self.config))
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 596, in _setup
self._init(self.config, self.env_creator)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py", line 117, in _init
config)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/dqn/apex.py", line 48, in defer_make_workers
return trainer._make_workers(env_creator, policy, config, 0)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 666, in _make_workers
logdir=self.logdir)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
RolloutWorker, env_creator, policy, 0, self._local_config)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/evaluation/worker_set.py", line 271, in _make_worker
_fake_sampler=config.get("_fake_sampler", False))
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
self._build_policy_map(policy_dict, policy_config)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py", line 836, in _build_policy_map
policy_map[name] = cls(obs_space, act_space, merged_conf)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/ddpg/ddpg_policy.py", line 110, in __init__
self.cur_observations, observation_space, action_space)
File "/home/axion/anaconda3/envs/training/lib/python3.7/site-packages/ray/rllib/agents/ddpg/ddpg_policy.py", line 410, in _build_policy_network
import tensorflow.contrib.layers as layers
ModuleNotFoundError: No module named 'tensorflow.contrib'
```
*Ray version and other system information (Python version, TensorFlow version, OS):*
Python 3.7.7
I am running Ray 0.8.3 but this bug is currently still in the master branch
tensorflow==2.1.0
### Reproduction (REQUIRED)
Setting any DDPG algorithm to `parameter_noise=True` forces this import to run
https://github.com/ray-project/ray/blob/master/rllib/agents/ddpg/ddpg_policy.py#L417
**Tensorflow 2 does not have a `contrib` module**
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| Yeah, we should switch to keras here. I'm taking a look.
For now, could you replace the code (~ line 416) in rllib/agents/ddpg/ddpg_policy.py with the following? That should eliminate the dependency on the deprecated tf.contrib.
```
activation = getattr(tf.nn, self.config["actor_hidden_activation"])
for hidden in self.config["actor_hiddens"]:
if self.config["parameter_noise"]:
action_out = tf.keras.layers.Dense(
units=hidden,
activation=activation)(action_out)
action_out = tf.keras.layers.LayerNormalization()(action_out)
else:
action_out = tf.keras.layers.Dense(
units=hidden, activation=activation)(action_out)
action_out = tf.keras.layers.Dense(
units=action_space.shape[0], activation=None)(action_out)
```
Cool. I will give this a try!
Preliminary tests look ok. Please let me know, if this doesn't help. | 2020-04-01T09:56:22 |
|
ray-project/ray | 7,863 | ray-project__ray-7863 | [
"7891"
] | c2cb5c2214f5427415f3fff770840cce6881aa28 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -1349,8 +1349,10 @@ def disconnect(exiting_interpreter=False):
@contextmanager
def _changeproctitle(title, next_title):
setproctitle.setproctitle(title)
- yield
- setproctitle.setproctitle(next_title)
+ try:
+ yield
+ finally:
+ setproctitle.setproctitle(next_title)
def register_custom_serializer(cls,
| Incorrect Title for Failed Tasks
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
When a task fails, the process title of the failed task remains. This appears in both the dashboard and to the OS (`htop`).
*Ray version and other system information (Python version, TensorFlow version, OS):*
Python: 3.7
OS: MacOS 10.15.4
Ray: Latest Master
### Reproduction (REQUIRED)
```
import ray
ray.init()
@ray.remote
def fail():
import NonRealModule
fail.remote()
```
Then run `htop` and "`__main__.fail()`" will remain even after the function errors and returns.
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| 2020-04-02T00:24:35 |
||
ray-project/ray | 7,877 | ray-project__ray-7877 | [
"7878"
] | 7f9ddfcfd869b16b49e47f2d48c027d13dd45922 | diff --git a/python/setup.py b/python/setup.py
--- a/python/setup.py
+++ b/python/setup.py
@@ -191,7 +191,7 @@ def find_version(*filepath):
# The BinaryDistribution argument triggers build_ext.
distclass=BinaryDistribution,
install_requires=requires,
- setup_requires=["cython >= 0.29.14"],
+ setup_requires=["cython >= 0.29.14", "wheel"],
extras_require=extras,
entry_points={
"console_scripts": [
| [ray] Build failure when installing from source
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
I am trying to install ray from source. However, I receive the following error during the install process:
```
HEAD is now at 8ffe41c apply cpython patch bpo-39492 for the reference count issue
+ /usr/bin/python setup.py bdist_wheel
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
or: setup.py --help [cmd1 cmd2 ...]
or: setup.py --help-commands
or: setup.py cmd --help
error: invalid command 'bdist_wheel'
Traceback (most recent call last):
File "setup.py", line 178, in <module>
setup(
File "/usr/lib/python3.8/site-packages/setuptools/__init__.py", line 144, in setup
return distutils.core.setup(**attrs)
File "/usr/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/usr/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/usr/lib/python3.8/distutils/command/build.py", line 135, in run
self.run_command(cmd_name)
File "/usr/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "setup.py", line 107, in run
subprocess.check_call(command)
File "/usr/lib/python3.8/subprocess.py", line 364, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['../build.sh', '-p', '/usr/bin/python']' returned non-zero exit status 1.
```
Note: `error invalid command 'bdist_wheel'`
*Ray version and other system information (Python version, TensorFlow version, OS):*
ray: 0.8.4
python: 3.8.2
os: Arch Linux
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
The above does not apply since this is a build issue.
| 2020-04-02T21:56:48 |
||
ray-project/ray | 7,982 | ray-project__ray-7982 | [
"7981"
] | dd63178e91dbe77b79be8d38bdf67501ceee1d7c | diff --git a/python/ray/tune/progress_reporter.py b/python/ray/tune/progress_reporter.py
--- a/python/ray/tune/progress_reporter.py
+++ b/python/ray/tune/progress_reporter.py
@@ -7,6 +7,11 @@
TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL)
from ray.tune.utils import flatten_dict
+try:
+ from collections.abc import Mapping
+except ImportError:
+ from collections import Mapping
+
try:
from tabulate import tabulate
except ImportError:
@@ -101,7 +106,7 @@ def add_metric_column(self, metric, representation=None):
if metric in self._metric_columns:
raise ValueError("Column {} already exists.".format(metric))
- if isinstance(self._metric_columns, collections.Mapping):
+ if isinstance(self._metric_columns, Mapping):
representation = representation or metric
self._metric_columns[metric] = representation
else:
@@ -291,7 +296,7 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
num_trials, ", ".join(num_trials_strs)))
# Pre-process trials to figure out what columns to show.
- if isinstance(metric_columns, collections.Mapping):
+ if isinstance(metric_columns, Mapping):
keys = list(metric_columns.keys())
else:
keys = metric_columns
@@ -303,7 +308,7 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
params = sorted(set().union(*[t.evaluated_params for t in trials]))
trial_table = [_get_trial_info(trial, params, keys) for trial in trials]
# Format column headings
- if isinstance(metric_columns, collections.Mapping):
+ if isinstance(metric_columns, Mapping):
formatted_columns = [metric_columns[k] for k in keys]
else:
formatted_columns = keys
diff --git a/python/ray/util/sgd/torch/torch_runner.py b/python/ray/util/sgd/torch/torch_runner.py
--- a/python/ray/util/sgd/torch/torch_runner.py
+++ b/python/ray/util/sgd/torch/torch_runner.py
@@ -1,4 +1,3 @@
-import collections
from filelock import FileLock
import logging
import inspect
@@ -17,6 +16,11 @@
logger = logging.getLogger(__name__)
amp = None
+try:
+ from collections.abc import Iterable
+except ImportError:
+ from collections import Iterable
+
try:
from apex import amp
except ImportError:
@@ -133,7 +137,7 @@ def _create_schedulers_if_available(self):
self.schedulers = self.scheduler_creator(self.given_optimizers,
self.config)
- if not isinstance(self.schedulers, collections.Iterable):
+ if not isinstance(self.schedulers, Iterable):
self.schedulers = [self.schedulers]
def _try_setup_apex(self):
@@ -153,7 +157,7 @@ def setup_components(self):
self._initialize_dataloaders()
logger.debug("Creating model")
self.models = self.model_creator(self.config)
- if not isinstance(self.models, collections.Iterable):
+ if not isinstance(self.models, Iterable):
self.models = [self.models]
assert all(isinstance(model, nn.Module) for model in self.models), (
"All models must be PyTorch models: {}.".format(self.models))
@@ -163,7 +167,7 @@ def setup_components(self):
logger.debug("Creating optimizer.")
self.optimizers = self.optimizer_creator(self.given_models,
self.config)
- if not isinstance(self.optimizers, collections.Iterable):
+ if not isinstance(self.optimizers, Iterable):
self.optimizers = [self.optimizers]
self._create_schedulers_if_available()
diff --git a/python/ray/util/sgd/torch/training_operator.py b/python/ray/util/sgd/torch/training_operator.py
--- a/python/ray/util/sgd/torch/training_operator.py
+++ b/python/ray/util/sgd/torch/training_operator.py
@@ -1,4 +1,3 @@
-import collections
import torch
from ray.util.sgd.utils import (TimerCollection, AverageMeterCollection,
@@ -8,6 +7,11 @@
amp = None
+try:
+ from collections.abc import Iterable
+except ImportError:
+ from collections import Iterable
+
try:
from apex import amp
except ImportError:
@@ -25,7 +29,7 @@
def _is_multiple(component):
"""Checks if a component (optimizer, model, etc) is not singular."""
- return isinstance(component, collections.Iterable) and len(component) > 1
+ return isinstance(component, Iterable) and len(component) > 1
class TrainingOperator:
@@ -66,19 +70,24 @@ def __init__(self,
use_tqdm=False):
# You are not expected to override this method.
self._models = models # List of models
- assert isinstance(models, collections.Iterable), (
- "Components need to be iterable. Got: {}".format(type(models)))
+ assert isinstance(
+ models,
+ Iterable), ("Components need to be iterable. Got: {}".format(
+ type(models)))
self._optimizers = optimizers # List of optimizers
- assert isinstance(optimizers, collections.Iterable), (
- "Components need to be iterable. Got: {}".format(type(optimizers)))
+ assert isinstance(
+ optimizers,
+ Iterable), ("Components need to be iterable. Got: {}".format(
+ type(optimizers)))
self._train_loader = train_loader
self._validation_loader = validation_loader
self._world_rank = world_rank
self._criterion = criterion
self._schedulers = schedulers
if schedulers:
- assert isinstance(schedulers, collections.Iterable), (
- "Components need to be iterable. Got: {}".format(
+ assert isinstance(
+ schedulers,
+ Iterable), ("Components need to be iterable. Got: {}".format(
type(schedulers)))
self._config = config
self._use_fp16 = use_fp16
| [python] Importing ABC directly from collections was deprecated and will be removed in Python 3.10
### What is the problem?
Importing ABC directly from collections was deprecated and will be removed in Python 3.10. Use collections.abc instead.
### Reproduction (REQUIRED)
```
python/ray/tune/progress_reporter.py
104: if isinstance(self._metric_columns, collections.Mapping):
294: if isinstance(metric_columns, collections.Mapping):
306: if isinstance(metric_columns, collections.Mapping):
python/ray/util/sgd/torch/training_operator.py
28: return isinstance(component, collections.Iterable) and len(component) > 1
69: assert isinstance(models, collections.Iterable), (
72: assert isinstance(optimizers, collections.Iterable), (
80: assert isinstance(schedulers, collections.Iterable), (
python/ray/util/sgd/torch/torch_runner.py
136: if not isinstance(self.schedulers, collections.Iterable):
156: if not isinstance(self.models, collections.Iterable):
166: if not isinstance(self.optimizers, collections.Iterable):
```
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).
| 2020-04-11T16:27:32 |
||
ray-project/ray | 8,177 | ray-project__ray-8177 | [
"8176"
] | 9dc625318f51f7d2ccaeb44ca331d94bc3b2a2f7 | diff --git a/python/ray/experimental/async_api.py b/python/ray/experimental/async_api.py
--- a/python/ray/experimental/async_api.py
+++ b/python/ray/experimental/async_api.py
@@ -1,7 +1,4 @@
-# Note: asyncio is only compatible with Python 3
-
import asyncio
-import threading
import ray
from ray.experimental.async_plasma import PlasmaEventHandler
@@ -10,7 +7,10 @@
handler = None
-async def _async_init():
+def init():
+ """Initialize plasma event handlers for asyncio support."""
+ assert ray.is_initialized(), "Please call ray.init before async_api.init"
+
global handler
if handler is None:
worker = ray.worker.global_worker
@@ -20,31 +20,6 @@ async def _async_init():
logger.debug("AsyncPlasma Connection Created!")
-def init():
- """
- Initialize synchronously.
- """
- assert ray.is_initialized(), "Please call ray.init before async_api.init"
-
- # Noop when handler is set.
- if handler is not None:
- return
-
- loop = asyncio.get_event_loop()
- if loop.is_running():
- if loop._thread_id != threading.get_ident():
- # If the loop is runing outside current thread, we actually need
- # to do this to make sure the context is initialized.
- asyncio.run_coroutine_threadsafe(_async_init(), loop=loop)
- else:
- async_init_done = asyncio.get_event_loop().create_task(
- _async_init())
- # Block until the async init finishes.
- async_init_done.done()
- else:
- asyncio.get_event_loop().run_until_complete(_async_init())
-
-
def as_future(object_id):
"""Turn an object_id into a Future object.
| diff --git a/python/ray/tests/test_asyncio.py b/python/ray/tests/test_asyncio.py
--- a/python/ray/tests/test_asyncio.py
+++ b/python/ray/tests/test_asyncio.py
@@ -113,8 +113,8 @@ async def test_asyncio_get(ray_start_regular_shared, event_loop):
loop.set_debug(True)
# This is needed for async plasma
- from ray.experimental.async_api import _async_init
- await _async_init()
+ from ray.experimental.async_api import init
+ init()
# Test Async Plasma
@ray.remote
| Ray async api is not working with uvloop.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Current Ray async api uses asyncio event loop's internal attribute to identify if the loop is running in the current current thread.
```python3
loop = asyncio.get_event_loop()
if loop.is_running():
if loop._thread_id != threading.get_ident():
# If the loop is runing outside current thread, we actually need
# to do this to make sure the context is initialized.
asyncio.run_coroutine_threadsafe(_async_init(), loop=loop)
```
This causes a problem when we uses Ray APIs inside Fast API because Fast API uses uvloop as its main event loop, and uvloop doesn't have `_thread_id` attribute.
@simon-mo Any good idea to fix this? It doesn't seem to be trivial. What about we do async_init() whenever asyncio loop is created in a different thread instead of checking if the event loop's thread id? I assume the only use case where asyncio loop is defined in a different thread is only inside async actor?
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```python3
import time
import asyncio
import ray
import psutil
from fastapi import FastAPI, APIRouter
app = FastAPI(
title="API template",
description="Template to build upon for API serving and distributed computation",
version="0.1.0",
openapi_url="/openapi.json",
docs_url="/docs",
)
@app.on_event("startup")
def startup_event():
ray.init(num_cpus=2)
@app.on_event("shutdown")
def shutdown_event():
ray.shutdown()
@app.get('/async')
async def non_seq_async_process():
"""
async distributed execution
"""
@ray.remote
def slow_function(i):
time.sleep(i)
return i
start_time = time.time()
# result_ids = []
# for i in range(10, 60, 10):
# result_ids.append(slow_function.remote(i))
# results = ray.get(result_ids)
results = await asyncio.wait([slow_function.remote(i) for i in range(10, 60, 10)])
duration = time.time() - start_time
out = "Executing the for loop took {:.3f} seconds.\n".format(duration)
out += f"The results are: {results}\n"
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-04-25T05:46:33 |
|
ray-project/ray | 8,211 | ray-project__ray-8211 | [
"8203"
] | eb790bf3a3241b492171c321fc59234c66fae8d5 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -435,10 +435,6 @@ def get_gpu_ids():
"""
# TODO(ilr) Handle inserting resources in local mode
- if _mode() == LOCAL_MODE:
- logger.info("ray.get_gpu_ids() currently does not work in LOCAL "
- "MODE.")
-
all_resource_ids = global_worker.core_worker.resource_ids()
assigned_ids = [
resource_id for resource_id, _ in all_resource_ids.get("GPU", [])
| Lots of "INFO worker.py:439 -- ray.get_gpu_ids() currently does not work in LOCAL MODE." messages printed in local mode
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
When I run in local mode I see lots of the following message being printed many times such that the console isn't really useable:
```
INFO worker.py:439 -- ray.get_gpu_ids() currently does not work in LOCAL MODE.
```
I'm not sure why this message is being printed as I'm not using a GPU (num_gpus=0, although my machine does have a GPU enabled). As I recall some time ago I was running in local mode and found that the GPU was being used when it wasn't supposed to be however. I first see this message printed with code version 9bfc2c4b5462871f0fe669b3aabc5f2772bc3a6f, and so assume it was caused by #7670. I'm not sure why this message is being printed when I shouldn't be using a GPU, and also I think the message should only be printed once and then suppressed in future instances when it would otherwise be printed
*Ray version and other system information (Python version, TensorFlow version, OS):*
Running on Azure NC6s_v2 machine
ray: 0.9.0.dev0 (latest wheels installed on 4/27/20)
python: 3.7.6
tensorflow: 2.1
torch: 1.4.0
OS: Ubuntu 16.04.6 LTS
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
I'm just running the following example, but changing `ray.init()` to `ray.init(local_mode=True)`
```bash
python examples/custom_env.py
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| cc @ijrsvt - can we mute this warning?
Yes we can | 2020-04-28T06:28:51 |
|
ray-project/ray | 8,225 | ray-project__ray-8225 | [
"854"
] | bf25aee39228d25fe34b782ccfb02ded59f64f50 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -1683,7 +1683,7 @@ def kill(actor):
def cancel(object_id, force=False):
- """Cancels a locally-submitted task according to the following conditions.
+ """Cancels a task according to the following conditions.
If the specified task is pending execution, it will not be executed. If
the task is currently executing, the behavior depends on the ``force``
@@ -1702,8 +1702,7 @@ def cancel(object_id, force=False):
force (boolean): Whether to force-kill a running task by killing
the worker that is running the task.
Raises:
- ValueError: This is also raised for actor tasks, already completed
- tasks, and non-locally submitted tasks.
+ TypeError: This is also raised for actor tasks.
"""
worker = ray.worker.global_worker
worker.check_connected()
| diff --git a/python/ray/tests/test_cancel.py b/python/ray/tests/test_cancel.py
--- a/python/ray/tests/test_cancel.py
+++ b/python/ray/tests/test_cancel.py
@@ -228,5 +228,31 @@ def wait_for(y):
assert isinstance(e, valid_exceptions(use_force))
[email protected]("use_force", [True, False])
+def test_remote_cancel(ray_start_regular, use_force):
+ signaler = SignalActor.remote()
+
+ @ray.remote
+ def wait_for(y):
+ return ray.get(y[0])
+
+ @ray.remote
+ def remote_wait(sg):
+ return [wait_for.remote([sg[0]])]
+
+ sig = signaler.wait.remote()
+
+ outer = remote_wait.remote([sig])
+ inner = ray.get(outer)[0]
+
+ with pytest.raises(RayTimeoutError):
+ ray.get(inner, 1)
+
+ ray.cancel(inner)
+
+ with pytest.raises(valid_exceptions(use_force)):
+ ray.get(inner, 10)
+
+
if __name__ == "__main__":
sys.exit(pytest.main(["-v", __file__]))
| Ray kill remote tasks
Is there any python interface for terminating remote tasks?
In some situations, one may need to stop some tasks before they finish.
For example, we may create several NNs to test a dataset.
cnn1 = CNN1.remote()
cnn2 = CNN2.remote()
lstm1 = LSTM1.remote()
lstm2 = LSTM2.remote()
...
then we use `ray.wait` to wait for these tasks.
and results (given by some tasks finished earlier) show that CNN is much better than LSTM for this dataset. So we would like to kill most LSTM tasks (we know their object_ids) to make more space for CNNs.
We wonder how to do that with ray.
@mitar
| Currently there is no interface for terminating tasks, but I agree this could be really useful.
One workaround would be to take your training tasks and divide them into smaller segments (e.g., 10 second chunks). Then whenever one training segment finishes, you can decide to launch a new training segment (picking up where the previous one left off) or to stop training that model.
Does that achieves what you're looking for?
As for canceling tasks, what sort of API do you think would make sense? E.g., `ray.cancel(object_id)` or something like that?
I would suggest provide some mechanism for sending messages and then task can check periodically if there is a message. Because in our case we might want to terminate a task, but also maybe just pause a task. So different messages could be beneficial for this. So API could be:
```python
ray.send_message(object_id, {message})
```
If there is no task running for `object_id`, nothing happens (or maybe a return value could signal that).
On receiving end, one could do something like `ray.get_messages()` to return any current pending messages available for the current task.
I see. You could certainly try that out (e.g., hijacking the existing Redis server for that purpose). I wouldn't want to encourage anyone to actually do this though, but we can see if that provides the functionality that you're looking for.
Something like the following.
```python
def broadcast_message(key, message):
ray.global_worker.redis_client.set(key, message)
def check_message(key):
return ray.global_worker.redis_client.get(key)
```
E.g.,
```python
import ray
import time
ray.init()
def broadcast_message(key, message):
ray.worker.global_worker.redis_client.set(key, message)
def check_message(key):
return ray.worker.global_worker.redis_client.get(key)
@ray.remote
def f():
while True:
if check_message("XXX") == b"stop":
break
time.sleep(1)
object_id = f.remote()
broadcast_message("XXX", b"stop")
# This should return
ray.get(object_id)
```
A good way to send messages to tasks in the current API is to use actors. Each training task is a long running actor which has a train() method that does the training for a few steps (which takes say a few seconds); you can then send messages to the actor by invoking methods on it, for example for killing it or for changing some training settings, etc. These can be invoked in between the training tasks.
-- Philipp.
Does a task have access to its ObjectId? Maybe with some context manager?
@pcmoritz: But wouldn't this introduce some latency? After each iteration, method has to return to the caller, then caller decides if to continue? Wouldn't be easier if method just check for any messages which arrived in meantime and then decide locally?
While a task is executing, it does not have access to the object IDs of its return values. What would you use that for?
As for latency, presumably you would train for 10 seconds or a minute and then you would have 1 millisecond of latency and then do another 10 seconds of training and so it probably wouldn't be an issue.
BTW, if we just leave the actor there (stop calling any function of the actor), will the actor try to release all the resources it used?
For example, when we initialize an actor, it creates a tf session and occupies some resources of GPU. Then we call `step()` of the actor or `keep_training_until_receive_msg()`. After breaking the training process, will it still stay in GPU until we manually close the tensorflow session in the actor?
Once a process using TensorFlow starts to use space on a GPU, it never releases it until the process is killed (see #616), so the actor will continue to occupy space on the GPU until the actor is killed. The actor will naturally be killed when the actor handle goes out of scope in Python.
E.g., if you do
```python
actor = CNN1.remote()
del actor
```
Then the call to `del actor` will send a task to the actor that will cause the actor to kill itself when the task runs.
While we still haven't implemented regular cancelation, now that we have distributed actor handles, it is possible to work around it by using an actor to send messages from the driver to the task.
Note that this requires the cooperation of the task itself. For example,
```python
import ray
import time
ray.init()
@ray.remote
class TaskCanceler(object):
def __init__(self):
self.task_canceled = False
def cancel_task(self):
"""The driver can call this to cancel the task."""
self.task_canceled = True
def is_task_canceled(self):
"""The task calls this to check if it is canceled."""
return self.task_canceled
@ray.remote
def f(task_canceler):
i = 0
while True:
print(i)
i += 1
time.sleep(1)
# Check if the task should abort.
if ray.get(task_canceler.is_task_canceled.remote()):
return
task_canceler = TaskCanceler.remote()
# Create the task.
f.remote(task_canceler)
# Cancel the task.
task_canceler.cancel_task.remote()
```
And of course this can be generalized in a lot of ways (e.g., to use one actor to selectively cancel many different tasks).
This has just again turned up as an issue for our project. I realized that in fact besides cooperative termination also forceful termination would be useful. Sometimes there is simply no small iterations to have (like in traditional ML algorithms) to check some status.
So I would ask for some way to kill a task forcefully, which would probably just terminate whole worker (I assume each worker is running only one task at a time, no). No matter the state or anything.
I also need to kill the worker via API, it would be very handy.
I'd start remote workers, then I'd do .wait with time limit and then I would like to kill the workers that didn't finish.
I tried implementing cooperative stopping in the workers but that's very hard, because the worker is calling compiled 3rd party simulator.
Hi guys,
Any update on this? Do we have any APIs to kill the remote workers yet?
I've ended up doing this by running my task in an actor on a background thread, a la
```python
import ray
import threading
import time
@ray.remote
class Task:
def __init__(self):
self._thread = threading.Thread(target=self._run, daemon=True)
self._thread.start()
def _run(self):
print('Started long-running task')
time.sleep(10)
print('You shouldn\'t see this because the process has been killed')
ray.init()
task = Task.remote()
task.__ray_terminate__.remote() # kills the process with an `exit(0)`
```
This should work as long as your long-running task doesn't hang on `ray.get`/`ray.wait`/the GIL.
`__ray_terminate__` will also be called when the original actor handle goes out of scope, so you don't have to call it explicitly if you don't want to.
You can now kill actors while they're still processing tasks using the `actor.__ray_kill__()`. This is similar to the solution @andyljones posted except it will work even if the actor is blocking on `ray.get()`, `ray.wait()`, or the GIL.
What about non-actors? So a simple Ray task?
(BTW, any pointers how this is implemented for actors?)
Not currently supported for tasks. See the original PR for details:
https://github.com/ray-project/ray/pull/6523
So then it would be reasonable to turn all of my tasks into actors? That looks like a trivial thing to do.
BTW, why is method called `__ray_kill__`? This is generally reserved for Python standard method names for interfaces which have language-based syntax to access. Like if there would be `kill(actor)` function which would then internally call `__ray_kill__` or something.
That seems like a reasonable workaround for now. That's a good point about the API - what would you think of `ray.kill_actor(<actor>)` instead?
What about just `ray.kill(<actor>)`? In the future it could also support killing tasks.
BTW, any reason why it would not be as easy to expose API to kill tasks as well? I am not sure how much overhead is to have an actor instead of a task?
It would be as easy to expose the API, but not to actually implement it. This is because anyone with a handle to an actor knows its location (RPC address) and can send a kill message, but to kill a task given only an `ObjectID` you would first have to look up where the task is running, which is possible but somewhat complicated and not currently supported.
@mitar
@suquark
We'd love to setup a quick call to go over your requirements here and better understand your use case. We're sketching a design and it'd be helpful to have your input. Can we setup some time in the next week or so? shoot us an email my first name at anyscale.com | 2020-04-29T04:42:20 |
ray-project/ray | 8,231 | ray-project__ray-8231 | [
"7920"
] | bf25aee39228d25fe34b782ccfb02ded59f64f50 | diff --git a/rllib/contrib/bandits/examples/LinTS_train_wheel_env.py b/rllib/contrib/bandits/examples/LinTS_train_wheel_env.py
--- a/rllib/contrib/bandits/examples/LinTS_train_wheel_env.py
+++ b/rllib/contrib/bandits/examples/LinTS_train_wheel_env.py
@@ -10,7 +10,7 @@
def plot_model_weights(means, covs):
fmts = ["bo", "ro", "yx", "k+", "gx"]
- labels = [f"arm{i}" for i in range(5)]
+ labels = ["arm{}".format(i) for i in range(5)]
fig, ax = plt.subplots(figsize=(6, 4))
diff --git a/rllib/contrib/bandits/examples/tune_LinTS_train_wheel_env.py b/rllib/contrib/bandits/examples/tune_LinTS_train_wheel_env.py
--- a/rllib/contrib/bandits/examples/tune_LinTS_train_wheel_env.py
+++ b/rllib/contrib/bandits/examples/tune_LinTS_train_wheel_env.py
@@ -15,7 +15,7 @@
def plot_model_weights(means, covs, ax):
fmts = ["bo", "ro", "yx", "k+", "gx"]
- labels = [f"arm{i}" for i in range(5)]
+ labels = ["arm{}".format(i) for i in range(5)]
ax.set_title("Weights distributions of arms")
diff --git a/rllib/contrib/bandits/models/linear_regression.py b/rllib/contrib/bandits/models/linear_regression.py
--- a/rllib/contrib/bandits/models/linear_regression.py
+++ b/rllib/contrib/bandits/models/linear_regression.py
@@ -85,9 +85,9 @@ def _check_inputs(self, x, y=None):
assert x.ndim in [2, 3], \
"Input context tensor must be 2 or 3 dimensional, where the" \
" first dimension is batch size"
- assert x.shape[
- 1] == self.d, f"Feature dimensions of weights ({self.d}) and " \
- f"context ({x.shape[1]}) do not match!"
+ assert x.shape[1] == self.d, \
+ "Feature dimensions of weights ({}) and context ({}) do not " \
+ "match!".format(self.d, x.shape[1])
if y:
assert torch.is_tensor(y) and y.numel() == 1,\
"Target should be a tensor;" \
@@ -134,9 +134,9 @@ def predict(self, x, sample_theta=False, use_ucb=False):
return scores
def partial_fit(self, x, y, arm):
- assert 0 <= arm.item() < len(self.arms),\
- f"Invalid arm: {arm.item()}." \
- f"It should be 0 <= arm < {len(self.arms)}"
+ assert 0 <= arm.item() < len(self.arms), \
+ "Invalid arm: {}. It should be 0 <= arm < {}".format(
+ arm.item(), len(self.arms))
self.arms[arm].partial_fit(x, y)
@override(ModelV2)
| [rllib] Python 3.5 compaltibility
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
In the fresh install of deep learning VM on GCE, rllib could not start training PPO with CartPole-v0.
There are some f-strings in the code and such code do not run on python 3.5.
Tested environment:
+ Debian9 on Google Compute Engine with Deep learning VM (Tensorflow 1.15.2)
+ Python 3.5.3
+ tensorflow-gpu 1.15.2 (Installed with Deep learning VM)
+ some python modules
> pip3 install dm-tree tabulate lz4 tensorboardX torch torchvision gym
+ Ray 0.8.4
> pip3 install ray
+ and Ray 0.9.0.dev0 (installed from snapshot wheel)
> pip3 install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp35-cp35m-manylinux1_x86_64.whl
### Reproduction (REQUIRED)
From the command line on the document "rllib in 60 seconds"
> rllib train --run=PPO --env=CartPole-v0
Results
```
Traceback (most recent call last):
File "/home/yos/bin/rllib", line 5, in <module>
from ray.rllib.scripts import cli
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/__init__.py", line 60, in <module>
_register_all()
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/__init__.py", line 37, in _register_all
register_trainable(key, get_agent_class(key))
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/agents/registry.py", line 130, in get_agent_class
return _get_agent_class(alg)
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/agents/registry.py", line 140, in _get_agent_class
return CONTRIBUTED_ALGORITHMS[alg]()
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/contrib/registry.py", line 26, in _import_bandit_linucb
from ray.rllib.contrib.bandits.agents.lin_ucb import LinUCBTrainer
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/contrib/bandits/agents/__init__.py", line 1, in <module>
from ray.rllib.contrib.bandits.agents.lin_ts import LinTSTrainer
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/contrib/bandits/agents/lin_ts.py", line 5, in <module>
from ray.rllib.contrib.bandits.agents.policy import BanditPolicy
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/contrib/bandits/agents/policy.py", line 6, in <module>
from ray.rllib.contrib.bandits.models.linear_regression import \
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/contrib/bandits/models/linear_regression.py", line 87
1] == self.d, f"Feature dimensions of weights ({self.d}) and " \
^
SyntaxError: invalid syntax
```
I've searched f-string in the source tree and patched like this commit(73ecb585b9f43a021e74f802ae83c9ed9889b4bf), and syntax error is gone.
However, even with this patch, training of CartPole-v0 could not be started because of another error:
```
2020-04-07 14:13:30,625 ERROR trial_runner.py:521 -- Trial PPO_CartPole-v0_00000: Error processing event.
Traceback (most recent call last):
File "/home/yos/.local/lib/python3.5/site-packages/ray/tune/trial_runner.py", line 467, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "/home/yos/.local/lib/python3.5/site-packages/ray/tune/ray_trial_executor.py", line 381, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File "/home/yos/.local/lib/python3.5/site-packages/ray/worker.py", line 1486, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(NameError): ray::PPO.__init__() (pid=1684, ip=10.140.0.3)
File "/home/yos/.local/lib/python3.5/site-packages/ray/rllib/utils/from_config.py", line 157, in from_config
constructor = getattr(module, type_)
AttributeError: module 'ray.rllib.utils.exploration.exploration' has no attribute 'StochasticSampling'
```
I have no idea with this error. Any help please?
- [V ] I have verified my script runs in a clean environment and reproduces the issue.
- [V ] I have verified the issue also occurs with the [latest wheels]
| 2020-04-29T14:58:05 |
||
ray-project/ray | 8,279 | ray-project__ray-8279 | [
"6993"
] | 2f01776d09835ffe93a8c608391059176c7688c5 | diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py
--- a/python/ray/scripts/scripts.py
+++ b/python/ray/scripts/scripts.py
@@ -7,6 +7,8 @@
import subprocess
import sys
import time
+import urllib
+import urllib.parse
import ray.services as services
from ray.autoscaler.commands import (
@@ -566,6 +568,16 @@ def create_or_update(cluster_config_file, min_workers, max_workers, no_restart,
if restart_only or no_restart:
assert restart_only != no_restart, "Cannot set both 'restart_only' " \
"and 'no_restart' at the same time!"
+ if urllib.parse.urlparse(cluster_config_file).scheme in ("http", "https"):
+ try:
+ response = urllib.request.urlopen(cluster_config_file, timeout=5)
+ content = response.read()
+ file_name = cluster_config_file.split("/")[-1]
+ with open(file_name, "wb") as f:
+ f.write(content)
+ cluster_config_file = file_name
+ except urllib.error.HTTPError as e:
+ logger.info("Error downloading file: ", e)
create_or_update_cluster(cluster_config_file, min_workers, max_workers,
no_restart, restart_only, yes, cluster_name)
| diff --git a/python/ray/tests/test_autoscaler_yaml.py b/python/ray/tests/test_autoscaler_yaml.py
--- a/python/ray/tests/test_autoscaler_yaml.py
+++ b/python/ray/tests/test_autoscaler_yaml.py
@@ -2,6 +2,8 @@
import os
import unittest
import yaml
+import urllib
+import tempfile
from ray.autoscaler.autoscaler import fillout_defaults, validate_config
from ray.test_utils import recursive_fnmatch
@@ -25,6 +27,21 @@ def testValidateDefaultConfig(self):
except Exception:
self.fail("Config did not pass validation test!")
+ def testValidateNetworkConfig(self):
+ web_yaml = "https://raw.githubusercontent.com/ray-project/ray/" \
+ "master/python/ray/autoscaler/aws/example-full.yaml"
+ response = urllib.request.urlopen(web_yaml, timeout=5)
+ content = response.read()
+ with tempfile.TemporaryFile() as f:
+ f.write(content)
+ f.seek(0)
+ config = yaml.safe_load(f)
+ config = fillout_defaults(config)
+ try:
+ validate_config(config)
+ except Exception:
+ self.fail("Config did not pass validation test!")
+
def _test_invalid_config(self, config_path):
with open(os.path.join(RAY_PATH, config_path)) as f:
config = yaml.safe_load(f)
| Allow `ray up` to take URL as argument.
To get started with the autoscaler, we tell people to run a line like `ray up ray/python/ray/autoscaler/aws/example-full.yaml`. In practice, the steps are
1. Figure out that this means you have to clone the Ray repository.
2. Clone the Ray repository.
3. Figure out the new correct relative file path based on where you cloned it.
4. Run `ray up ...`.
Instead, we could let people just copy and paste the following command:
```
ray up https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-full.yaml
```
In this blog post https://towardsdatascience.com/10x-faster-parallel-python-without-python-multiprocessing-e5017c93cce1, I posted benchmarks at the end that they could run with the autoscaler, but they still have to `wget` the relevant file. It'd be much easier if they could just use the URL.
Could also just write stuff like the following, but one line would be better.
```
wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/autoscaler/aws/example-full.yaml
ray up example-full.yaml
```
| Actually, even better, maybe we can have default cluster profiles available.
For example,
```
ray up examples.tune
ray up examples.sgd
ray up examples.small
```
The url thing is a standard practice in kubernetes world. for example https://aws.amazon.com/premiumsupport/knowledge-center/eks-cluster-kubernetes-dashboard/
@richardliaw the thing I like about the URL approach is that it is extensible, so if I write a blog post, I can host my own cluster yaml somewhere and then give people one command to run to reproduce the experiments.
This might be within my capability (found via `good first issue` tag). Can someone reference me to the `ray up` section of the codebase?
Sure! Here's the code that correspond to `ray up`
https://github.com/ray-project/ray/blob/b23b6addfcfd4be7a27e58aefbecb3fd7dd6510c/python/ray/scripts/scripts.py#L523-L566
https://github.com/ray-project/ray/blob/b23b6addfcfd4be7a27e58aefbecb3fd7dd6510c/python/ray/scripts/scripts.py#L999
Thanks! Yeah, I think I can do that. I'll take a stab over the weekend and get you a PR. | 2020-05-01T22:12:54 |
ray-project/ray | 8,324 | ray-project__ray-8324 | [
"8319"
] | 5f278c6411eda9d0d5e69e5e14e3496a17daf298 | diff --git a/rllib/examples/env/simple_corridor.py b/rllib/examples/env/simple_corridor.py
--- a/rllib/examples/env/simple_corridor.py
+++ b/rllib/examples/env/simple_corridor.py
@@ -12,24 +12,21 @@ def __init__(self, config):
self.end_pos = config["corridor_length"]
self.cur_pos = 0
self.action_space = Discrete(2)
- self.observation_space = Box(
- 0.0, self.end_pos, shape=(1, ), dtype=np.float32)
+ self.observation_space = Box(0.0, 999.0, shape=(1, ), dtype=np.float32)
def set_corridor_length(self, length):
self.end_pos = length
- self.observation_space = Box(
- 0.0, self.end_pos, shape=(1, ), dtype=np.float32)
print("Updated corridor length to {}".format(length))
def reset(self):
- self.cur_pos = 0
+ self.cur_pos = 0.0
return [self.cur_pos]
def step(self, action):
assert action in [0, 1], action
if action == 0 and self.cur_pos > 0:
- self.cur_pos -= 1
+ self.cur_pos -= 1.0
elif action == 1:
- self.cur_pos += 1
+ self.cur_pos += 1.0
done = self.cur_pos >= self.end_pos
return [self.cur_pos], 1 if done else 0, done, {}
diff --git a/rllib/models/tf/tf_action_dist.py b/rllib/models/tf/tf_action_dist.py
--- a/rllib/models/tf/tf_action_dist.py
+++ b/rllib/models/tf/tf_action_dist.py
@@ -326,6 +326,11 @@ def _unsquash(self, values):
unsquashed = tf.math.atanh(save_normed_values)
return unsquashed
+ @staticmethod
+ @override(ActionDistribution)
+ def required_model_output_shape(action_space, model_config):
+ return np.prod(action_space.shape) * 2
+
class Beta(TFActionDistribution):
"""
@@ -371,6 +376,11 @@ def _squash(self, raw_values):
def _unsquash(self, values):
return (values - self.low) / (self.high - self.low)
+ @staticmethod
+ @override(ActionDistribution)
+ def required_model_output_shape(action_space, model_config):
+ return np.prod(action_space.shape) * 2
+
class Deterministic(TFActionDistribution):
"""Action distribution that returns the input values directly.
diff --git a/rllib/utils/exploration/random.py b/rllib/utils/exploration/random.py
--- a/rllib/utils/exploration/random.py
+++ b/rllib/utils/exploration/random.py
@@ -1,4 +1,4 @@
-from gym.spaces import Discrete, MultiDiscrete, Tuple
+from gym.spaces import Discrete, Box, MultiDiscrete
import numpy as np
import tree
from typing import Union
@@ -9,6 +9,7 @@
from ray.rllib.utils import force_tuple
from ray.rllib.utils.framework import try_import_tf, try_import_torch, \
TensorType
+from ray.rllib.utils.space_utils import get_base_struct_from_space
tf = try_import_tf()
torch, _ = try_import_torch()
@@ -35,13 +36,8 @@ def __init__(self, action_space, *, model, framework, **kwargs):
framework=framework,
**kwargs)
- # Determine py_func types, depending on our action-space.
- if isinstance(self.action_space, (Discrete, MultiDiscrete)) or \
- (isinstance(self.action_space, Tuple) and
- isinstance(self.action_space[0], (Discrete, MultiDiscrete))):
- self.dtype_sample, self.dtype = (tf.int64, tf.int32)
- else:
- self.dtype_sample, self.dtype = (tf.float64, tf.float32)
+ self.action_space_struct = get_base_struct_from_space(
+ self.action_space)
@override(Exploration)
def get_exploration_action(self,
@@ -59,14 +55,46 @@ def get_exploration_action(self,
def get_tf_exploration_action_op(self, action_dist, explore):
def true_fn():
- action = tf.py_function(self.action_space.sample, [],
- self.dtype_sample)
- # Will be unnecessary, once we support batch/time-aware Spaces.
- return tf.expand_dims(tf.cast(action, dtype=self.dtype), 0)
+ batch_size = 1
+ req = force_tuple(
+ action_dist.required_model_output_shape(
+ self.action_space, self.model.model_config))
+ # Add a batch dimension?
+ if len(action_dist.inputs.shape) == len(req) + 1:
+ batch_size = tf.shape(action_dist.inputs)[0]
+
+ # Function to produce random samples from primitive space
+ # components: (Multi)Discrete or Box.
+ def random_component(component):
+ if isinstance(component, Discrete):
+ return tf.random.uniform(
+ shape=(batch_size, ) + component.shape,
+ maxval=component.n,
+ dtype=component.dtype)
+ elif isinstance(component, MultiDiscrete):
+ return tf.random.uniform(
+ shape=(batch_size, ) + component.shape,
+ maxval=component.nvec,
+ dtype=component.dtype)
+ elif isinstance(component, Box):
+ if component.bounded_above.all() and \
+ component.bounded_below.all():
+ return tf.random.uniform(
+ shape=(batch_size, ) + component.shape,
+ minval=component.low,
+ maxval=component.high,
+ dtype=component.dtype)
+ else:
+ return tf.random.normal(
+ shape=(batch_size, ) + component.shape,
+ dtype=component.dtype)
+
+ actions = tree.map_structure(random_component,
+ self.action_space_struct)
+ return actions
def false_fn():
- return tf.cast(
- action_dist.deterministic_sample(), dtype=self.dtype)
+ return action_dist.deterministic_sample()
action = tf.cond(
pred=tf.constant(explore, dtype=tf.bool)
@@ -81,15 +109,17 @@ def false_fn():
def get_torch_exploration_action(self, action_dist, explore):
if explore:
- # Unsqueeze will be unnecessary, once we support batch/time-aware
- # Spaces.
- a = self.action_space.sample()
req = force_tuple(
action_dist.required_model_output_shape(
self.action_space, self.model.model_config))
- # Add a batch dimension.
+ # Add a batch dimension?
if len(action_dist.inputs.shape) == len(req) + 1:
- a = np.expand_dims(a, 0)
+ batch_size = action_dist.inputs.shape[0]
+ a = np.stack(
+ [self.action_space.sample() for _ in range(batch_size)])
+ else:
+ a = self.action_space.sample()
+ # Convert action to torch tensor.
action = torch.from_numpy(a).to(self.device)
else:
action = action_dist.deterministic_sample()
| diff --git a/rllib/agents/ddpg/tests/test_ddpg.py b/rllib/agents/ddpg/tests/test_ddpg.py
--- a/rllib/agents/ddpg/tests/test_ddpg.py
+++ b/rllib/agents/ddpg/tests/test_ddpg.py
@@ -22,11 +22,12 @@ def test_ddpg_compilation(self):
"""Test whether a DDPGTrainer can be built with both frameworks."""
config = ddpg.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
+ config["num_envs_per_worker"] = 2 # Run locally.
num_iterations = 2
# Test against all frameworks.
- for _ in framework_iterator(config, ("torch", "tf")):
+ for _ in framework_iterator(config, ("tf", "torch")):
trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0")
for i in range(num_iterations):
results = trainer.train()
@@ -366,6 +367,8 @@ def test_ddpg_loss_function(self):
else:
check(tf_var, torch_var, rtol=0.07)
+ trainer.stop()
+
def _get_batch_helper(self, obs_size, actions, batch_size):
return {
SampleBatch.CUR_OBS: np.random.random(size=obs_size),
diff --git a/rllib/tests/test_multi_agent_pendulum.py b/rllib/tests/test_multi_agent_pendulum.py
--- a/rllib/tests/test_multi_agent_pendulum.py
+++ b/rllib/tests/test_multi_agent_pendulum.py
@@ -2,9 +2,10 @@
import unittest
import ray
-from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
from ray.tune import run_experiments
from ray.tune.registry import register_env
+from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
+from ray.rllib.utils.test_utils import framework_iterator
class TestMultiAgentPendulum(unittest.TestCase):
@@ -17,34 +18,38 @@ def tearDown(self) -> None:
def test_multi_agent_pendulum(self):
register_env("multi_agent_pendulum",
lambda _: MultiAgentPendulum({"num_agents": 1}))
- trials = run_experiments({
- "test": {
- "run": "PPO",
- "env": "multi_agent_pendulum",
- "stop": {
- "timesteps_total": 500000,
- "episode_reward_mean": -200,
- },
- "config": {
- "train_batch_size": 2048,
- "vf_clip_param": 10.0,
- "num_workers": 0,
- "num_envs_per_worker": 10,
- "lambda": 0.1,
- "gamma": 0.95,
- "lr": 0.0003,
- "sgd_minibatch_size": 64,
- "num_sgd_iter": 10,
- "model": {
- "fcnet_hiddens": [128, 128],
+
+ # Test for both torch and tf.
+ for fw in framework_iterator(frameworks=["torch", "tf"]):
+ trials = run_experiments({
+ "test": {
+ "run": "PPO",
+ "env": "multi_agent_pendulum",
+ "stop": {
+ "timesteps_total": 500000,
+ "episode_reward_mean": -300.0,
+ },
+ "config": {
+ "train_batch_size": 2048,
+ "vf_clip_param": 10.0,
+ "num_workers": 0,
+ "num_envs_per_worker": 10,
+ "lambda": 0.1,
+ "gamma": 0.95,
+ "lr": 0.0003,
+ "sgd_minibatch_size": 64,
+ "num_sgd_iter": 10,
+ "model": {
+ "fcnet_hiddens": [128, 128],
+ },
+ "batch_mode": "complete_episodes",
+ "use_pytorch": fw == "torch",
},
- "batch_mode": "complete_episodes",
- },
- }
- })
- if trials[0].last_result["episode_reward_mean"] < -200:
- raise ValueError("Did not get to -200 reward",
- trials[0].last_result)
+ }
+ })
+ if trials[0].last_result["episode_reward_mean"] < -300.0:
+ raise ValueError("Did not get to -200 reward",
+ trials[0].last_result)
if __name__ == "__main__":
| [rllib] Vectorization & multi-agent are broken in DDPG (both TF and Torch)
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
`./train.py --run=DDPG --env=MountainCarContinuous-v0 --config='{"num_envs_per_worker": 2}'`
and
`./train.py --run=DDPG --env=MountainCarContinuous-v0 --config='{"num_envs_per_worker": 2}' --torch`
both currently crash. The issue seems to be that while the input to compute_actions() is a batch of N observations, only 1 action is returned as output. I ran into this while debugging a hang in a multi-agent test case (hung due to a bug in the env triggered by vectorization returning 1 action instead of N actions).
| 2020-05-05T13:09:48 |
|
ray-project/ray | 8,366 | ray-project__ray-8366 | [
"8307"
] | 2c599dbf05e41e338920ee2fbe692658bcbec4dd | diff --git a/python/ray/tune/ray_trial_executor.py b/python/ray/tune/ray_trial_executor.py
--- a/python/ray/tune/ray_trial_executor.py
+++ b/python/ray/tune/ray_trial_executor.py
@@ -256,9 +256,6 @@ def _stop_trial(self, trial, error=False, error_msg=None,
error_msg (str): Optional error message.
stop_logger (bool): Whether to shut down the trial logger.
"""
- if stop_logger:
- trial.close_logger()
-
self.set_status(trial, Trial.ERROR if error else Trial.TERMINATED)
trial.set_location(Location())
@@ -278,6 +275,8 @@ def _stop_trial(self, trial, error=False, error_msg=None,
self.set_status(trial, Trial.ERROR)
finally:
trial.set_runner(None)
+ if stop_logger:
+ trial.close_logger()
def start_trial(self, trial, checkpoint=None, train=True):
"""Starts the trial.
| [tune] close logger after updating Trial status
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
Several experiment tracking systems, such as [neptune](https://neptune.ai/) or [Weights&Biases](https://www.wandb.com/) offer to categorize experiments as "Succeeded" or "Failed". This status is set when closing the logger.
Today, the `RayTrialExecutor._stop_trial` method closes the loggers before updating the status of the trial and updating its `trial.error_msg` attribute. When loggers are closed, it is impossible to know whether the trial is terminated normally or with an error, and therefore to set correct status in the experiment trackers.
To me, the solution seems as simple as moving these two lines:
https://github.com/ray-project/ray/blob/8625e09067a0f87b9951b2b41a7c7e0fead66f6e/python/ray/tune/ray_trial_executor.py#L259-L260
at the end of the `_stop_trial` method.
Is there any reason to close logger at the beginning of `_stop_trial`?
Happy to create a PR otherwise!
Thanks,
| Yeah that sounds good - in the finally block right?
A PR would be really appreciated - thanks! | 2020-05-08T10:02:35 |
|
ray-project/ray | 8,445 | ray-project__ray-8445 | [
"8147"
] | 5f4c196feda42d022c12c07e5cd4182fbbf46cba | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -220,8 +220,10 @@ def get_best_trial(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last]. If `scope=last`, only look at
+ scope (str): One of [all, last, avg]. If `scope=last`, only look at
each trial's final step for `metric`, and compare across
+ trials based on `mode=[min,max]`. If `scope=avg`, consider the
+ simple average over all steps for `metric` and compare across
trials based on `mode=[min,max]`. If `scope=all`, find each
trial's min/max score for `metric` based on `mode`, and
compare trials based on `mode=[min,max]`.
@@ -231,11 +233,11 @@ def get_best_trial(self, metric, mode="max", scope="all"):
"ExperimentAnalysis: attempting to get best trial for "
"metric {} for mode {} not in [\"max\", \"min\"]".format(
metric, mode))
- if scope not in ["all", "last"]:
+ if scope not in ["all", "last", "avg"]:
raise ValueError(
"ExperimentAnalysis: attempting to get best trial for "
- "metric {} for scope {} not in [\"all\", \"last\"]".format(
- metric, scope))
+ "metric {} for scope {} not in [\"all\", \"last\", \"avg\"]".
+ format(metric, scope))
best_trial = None
best_metric_score = None
for trial in self.trials:
@@ -244,6 +246,8 @@ def get_best_trial(self, metric, mode="max", scope="all"):
if scope == "last":
metric_score = trial.metric_analysis[metric]["last"]
+ elif scope == "avg":
+ metric_score = trial.metric_analysis[metric]["avg"]
else:
metric_score = trial.metric_analysis[metric][mode]
@@ -269,8 +273,10 @@ def get_best_config(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last]. If `scope=last`, only look at
+ scope (str): One of [all, last, avg]. If `scope=last`, only look at
each trial's final step for `metric`, and compare across
+ trials based on `mode=[min,max]`. If `scope=avg`, consider the
+ simple average over all steps for `metric` and compare across
trials based on `mode=[min,max]`. If `scope=all`, find each
trial's min/max score for `metric` based on `mode`, and
compare trials based on `mode=[min,max]`.
@@ -286,8 +292,10 @@ def get_best_logdir(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last]. If `scope=last`, only look at
+ scope (str): One of [all, last, avg]. If `scope=last`, only look at
each trial's final step for `metric`, and compare across
+ trials based on `mode=[min,max]`. If `scope=avg`, consider the
+ simple average over all steps for `metric` and compare across
trials based on `mode=[min,max]`. If `scope=all`, find each
trial's min/max score for `metric` based on `mode`, and
compare trials based on `mode=[min,max]`.
diff --git a/python/ray/tune/trial.py b/python/ray/tune/trial.py
--- a/python/ray/tune/trial.py
+++ b/python/ray/tune/trial.py
@@ -214,7 +214,7 @@ def __init__(self,
self.last_result = {}
self.last_update_time = -float("inf")
- # stores in memory max/min/last result for each metric by trial
+ # stores in memory max/min/avg/last result for each metric by trial
self.metric_analysis = {}
self.export_formats = export_formats
@@ -476,13 +476,18 @@ def update_last_result(self, result, terminate=False):
self.metric_analysis[metric] = {
"max": value,
"min": value,
+ "avg": value,
"last": value
}
else:
+ step = result["training_iteration"] or 1
self.metric_analysis[metric]["max"] = max(
value, self.metric_analysis[metric]["max"])
self.metric_analysis[metric]["min"] = min(
value, self.metric_analysis[metric]["min"])
+ self.metric_analysis[metric]["avg"] = 1 / step * (
+ value +
+ (step - 1) * self.metric_analysis[metric]["avg"])
self.metric_analysis[metric]["last"] = value
def get_trainable_cls(self):
| diff --git a/python/ray/tune/tests/test_experiment_analysis_mem.py b/python/ray/tune/tests/test_experiment_analysis_mem.py
--- a/python/ray/tune/tests/test_experiment_analysis_mem.py
+++ b/python/ray/tune/tests/test_experiment_analysis_mem.py
@@ -3,6 +3,7 @@
import tempfile
import random
import pandas as pd
+import numpy as np
import ray
from ray.tune import run, Trainable, sample_from, Analysis, grid_search
@@ -12,16 +13,17 @@
class ExperimentAnalysisInMemorySuite(unittest.TestCase):
def setUp(self):
class MockTrainable(Trainable):
+ scores_dict = {
+ 0: [5, 4, 0],
+ 1: [4, 3, 1],
+ 2: [2, 1, 8],
+ 3: [9, 7, 6],
+ 4: [7, 5, 3]
+ }
+
def _setup(self, config):
self.id = config["id"]
self.idx = 0
- self.scores_dict = {
- 0: [5, 0],
- 1: [4, 1],
- 2: [2, 8],
- 3: [9, 6],
- 4: [7, 3]
- }
def _train(self):
val = self.scores_dict[self.id][self.idx]
@@ -43,14 +45,15 @@ def tearDown(self):
def testCompareTrials(self):
self.test_dir = tempfile.mkdtemp()
- scores_all = [5, 4, 2, 9, 7, 0, 1, 8, 6, 3]
+ scores = np.asarray(list(self.MockTrainable.scores_dict.values()))
+ scores_all = scores.flatten("F")
scores_last = scores_all[5:]
ea = run(
self.MockTrainable,
name="analysis_exp",
local_dir=self.test_dir,
- stop={"training_iteration": 2},
+ stop={"training_iteration": 3},
num_samples=1,
config={"id": grid_search(list(range(5)))})
@@ -60,9 +63,15 @@ def testCompareTrials(self):
"min").metric_analysis["score"]["min"]
max_last = ea.get_best_trial("score", "max",
"last").metric_analysis["score"]["last"]
+ max_avg = ea.get_best_trial("score", "max",
+ "avg").metric_analysis["score"]["avg"]
+ min_avg = ea.get_best_trial("score", "min",
+ "avg").metric_analysis["score"]["avg"]
self.assertEqual(max_all, max(scores_all))
self.assertEqual(min_all, min(scores_all))
self.assertEqual(max_last, max(scores_last))
+ self.assertAlmostEqual(max_avg, max(np.mean(scores, axis=1)))
+ self.assertAlmostEqual(min_avg, min(np.mean(scores, axis=1)))
self.assertNotEqual(max_last, max(scores_all))
| [tune]add 'last-n-avg' option for scope parameter in get_best_* methods
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
As described in the [Google Groups](https://mail.google.com/mail/u/0/#inbox/FMfcgxwHMZGcqCZltQQjfxMpKMWpXKdK), it makes sense to evaluate model on every batch of time series data (like _hour-01, hour-02, ..., hour-23_) when we are doing some online learning stuff (for example ads\feeds recommending).
When dealing with such situation, it's better to take **average of last n or all** steps' metric of one trial into consideration when we are retrieving the best trial.
So I sugguest add **'avg'**, **'last-n-avg'** options to the **scope** parameter in following methods
- ray.tune.ExperimentAnalysis.get_best_trial
- ray.tune.ExperimentAnalysis.get_best_config
- ray.tune.ExperimentAnalysis.get_best_logdir
| cc @krfricke | 2020-05-14T11:53:14 |
ray-project/ray | 8,466 | ray-project__ray-8466 | [
"8464"
] | 9a83908c460a8bdf38b665c290a003e492a87e16 | diff --git a/python/setup.py b/python/setup.py
--- a/python/setup.py
+++ b/python/setup.py
@@ -194,7 +194,7 @@ def find_version(*filepath):
"google",
"grpcio",
"jsonschema",
- "msgpack >= 0.6.0, < 1.0.0",
+ "msgpack >= 0.6.0, < 2.0.0",
"numpy >= 1.16",
"protobuf >= 3.8.0",
"py-spy >= 0.2.0",
diff --git a/streaming/python/runtime/gateway_client.py b/streaming/python/runtime/gateway_client.py
--- a/streaming/python/runtime/gateway_client.py
+++ b/streaming/python/runtime/gateway_client.py
@@ -69,4 +69,4 @@ def serialize(obj) -> bytes:
def deserialize(data: bytes):
"""Deserialize the binary data serialized by `PythonGateway`"""
- return msgpack.unpackb(data, raw=False)
+ return msgpack.unpackb(data, raw=False, strict_map_key=False)
diff --git a/streaming/python/runtime/serialization.py b/streaming/python/runtime/serialization.py
--- a/streaming/python/runtime/serialization.py
+++ b/streaming/python/runtime/serialization.py
@@ -41,7 +41,7 @@ def serialize(self, obj):
return msgpack.packb(fields, use_bin_type=True)
def deserialize(self, data):
- fields = msgpack.unpackb(data, raw=False)
+ fields = msgpack.unpackb(data, raw=False, strict_map_key=False)
if fields[0] == _RECORD_TYPE_ID:
stream, value = fields[1:]
record = message.Record(value)
| Bump msgpack to 1.0.0 for Python
We've started bumping msgpack to 1.0.0 in some of our repos at our company. However, some of our repos use Ray, which doesn't support version 1.0.0 for msgpack. We're hoping that Ray could support msgpack 1.0.0 - it doesn't look like there are too many breaking changes, and it looks like both major versions 0 and 1 can be supported. Thanks!
| cc @simon-mo.
Thanks for reporting this @alecbrick . We will see if we can have time to work on this, but always feel free to contribute! | 2020-05-16T08:10:05 |
|
ray-project/ray | 8,491 | ray-project__ray-8491 | [
"5982"
] | 5b330de1820b6b0115833dff5b890fd7bbf56080 | diff --git a/python/ray/experimental/tf_utils.py b/python/ray/experimental/tf_utils.py
--- a/python/ray/experimental/tf_utils.py
+++ b/python/ray/experimental/tf_utils.py
@@ -161,10 +161,7 @@ def get_weights(self):
Dictionary mapping variable names to their weights.
"""
self._check_sess()
- return {
- k: v.eval(session=self.sess)
- for k, v in self.variables.items()
- }
+ return self.sess.run(self.variables)
def set_weights(self, new_weights):
"""Sets the weights to new_weights.
| Time to initialize a policy grows linearly with the number of agents
<!--
General questions should be asked on the mailing list [email protected].
Questions about how to use Ray should be asked on
[StackOverflow](https://stackoverflow.com/questions/tagged/ray).
Before submitting an issue, please fill out the following form.
-->
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 18.04
- **Ray installed from (source or binary)**: Binary
- **Ray version**: 0.7.4
- **Python version**: 3.7.4
- **Exact command to reproduce**: N/A
<!--
You can obtain the Ray version with
python -c "import ray; print(ray.__version__)"
-->
### Describe the problem
<!-- Describe the problem clearly here. -->
I noticed that in multi agent settings, the time to initialize a policy per agent increases as more agents are initialized. In the sample output I provided below, you can see that the time to initialize a single DynamicTFPolicy grows from 4.6 seconds to 15.3 seconds from the first agent to the tenth agent created. Line 291 of `rllib/policy/dynamic_tf_policy.py` is
```python
self._sess.run(tf.global_variables_initializer())
```
which I believe will run one time for each agent initialized. If I'm not mistaken, this means that every variable in the computation graph is being initialized each time that we initialize a DynamicTFPolicy. If initializing a DynamicTFPolicy adds new variables to the computation graph (as I believe it does), this would explain why the time to initialize a DynamicTFPolicy grows over time: We are initializing every variable in the computation graph, and the computation graph is growing. My question is, why does line 291 run a global variables initializer? Is there a reason for this that I can't see inside this method? How hard would it be to modify this to only initialize variables in the individual policy that we care to initialize?
I'm asking this because as detailed in #5753, I'm trying to modify rllib to allow initialization and removal of policies during training. The overhead incurred by this initialization quickly slows the training script down enough to be useless. Also, if anyone knows what the resource bottleneck is for policy initialization, that would be very helpful to know for when we're picking new hardware. Does it need a ton of cores to run in parallel, or more memory, or a bigger GPU or more GPUs or something? Thanks.
### Source code / logs
<!-- Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem. -->
```
(pytorch) root@e3a955e42cae:~/bees/bees# python trainer.py settings/settings.json
2019-10-23 12:38:04,168 WARNING worker.py:1426 -- WARNING: Not updating worker name since `setproctitle` is not installed. Install this with `pip install setproctitle` (or ray[debug]) to enable monitoring of worker processes.
2019-10-23 12:38:04,169 INFO resource_spec.py:205 -- Starting Ray with 3.52 GiB memory available for workers and up to 1.78 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2019-10-23 12:38:05,354 INFO trainer.py:344 -- Tip: set 'eager': true or the --eager flag to enable TensorFlow eager execution
2019-10-23 12:38:05,754 WARNING ppo.py:149 -- Using the simple minibatch optimizer. This will significantly reduce performance, consider simple_optimizer=False.
DTFP: 4.604122s
DTFP: 4.856234s
DTFP: 5.630484s
DTFP: 6.850456s
DTFP: 7.856700s
DTFP: 9.624164s
DTFP: 10.894944s
DTFP: 12.129192s
DTFP: 14.210247s
DTFP: 15.342738s
```
Line 130 in `tf_policy_template.py` (modified to print debug output above)
```python
t = time.time()
DynamicTFPolicy.__init__(
self,
obs_space,
action_space,
config,
loss_fn,
stats_fn=stats_fn,
grad_stats_fn=grad_stats_fn,
before_loss_init=before_loss_init_wrapper,
make_model=make_model,
action_sampler_fn=action_sampler_fn,
existing_model=existing_model,
existing_inputs=existing_inputs,
get_batch_divisibility_req=get_batch_divisibility_req,
obs_include_prev_action_reward=obs_include_prev_action_reward)
print("DTFP: %fs" % (time.time() - t))
```
Snippet of trainer script used.
```python
# pylint: disable=invalid-name
if __name__ == "__main__":
ray.init()
# Get ``settings`` file for now.
settings_file = sys.argv[1]
with open(settings_file, "r") as f:
settings = json.load(f)
env_config = settings["env"]
time_steps = env_config["time_steps"]
space_env = create_env(settings)
env = create_env(settings)
# Register environment
register_env("world", lambda _: env)
# Build environment instance to get ``obs_space``.
obs_space = space_env.observation_space
act_space = space_env.action_space
# You can also have multiple policies per trainer, but here we just
# show one each for PPO and DQN.
policies: Dict[str, Tuple[Any, gym.Space, gym.Space, Dict[Any, Any]]] = {
"0": (PPOTFPolicy, obs_space, act_space, {}),
"1": (PPOTFPolicy, obs_space, act_space, {}),
"2": (PPOTFPolicy, obs_space, act_space, {}),
"3": (PPOTFPolicy, obs_space, act_space, {}),
"4": (PPOTFPolicy, obs_space, act_space, {}),
"5": (PPOTFPolicy, obs_space, act_space, {}),
"6": (PPOTFPolicy, obs_space, act_space, {}),
"7": (PPOTFPolicy, obs_space, act_space, {}),
"8": (PPOTFPolicy, obs_space, act_space, {}),
"9": (PPOTFPolicy, obs_space, act_space, {}),
}
def policy_mapping_fn(agent_id: int) -> str:
""" Returns the given agent's policy identifier. """
return str(agent_id)
ppo_trainer = PPOTrainer(
env="bee_world",
config={
"multiagent": {
"policies": policies,
"policy_mapping_fn": policy_mapping_fn,
"policies_to_train": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
},
"simple_optimizer": True,
# Disable filters, otherwise we would need to synchronize those
# as well to the DQN agent.
"observation_filter": "NoFilter",
"num_workers": 2,
"num_gpus": 1,
"train_batch_size": 2,
"sample_batch_size": 1,
"sgd_minibatch_size": 2,
},
)
```
| I am a collaborator on the project with @brendanxwhitaker, and I wanted to add another peculiar thing about the initialization code in DynamicTFPolicy. As pointed out above, line 291 of `rllib/policy/dynamic_tf_policy.py` is
```python
self._sess.run(tf.global_variables_initializer())
```
However, line 350 of the same initialization method in the same file is the exact same line, so that initialization runs twice. This is briefly touched on in a comment on line 290
```python
# postprocessing might depend on variable init, so run it first here
```
This is obviously a bit redundant but seems necessary unless something else in the method changes. My question about this is, under what conditions is the postprocessing dependent on the variable initialization, and how hard would it be to change this method so that the initialization is not redundant?
Thanks for looking into this... agree we should try to fix the redundant initialization. One possible solution may be to initialize uninitialized variables. It looks like TF doesn't have a way to do this out of the box, but there are some workarounds:
https://stackoverflow.com/questions/35164529/in-tensorflow-is-there-any-way-to-just-initialize-uninitialised-variables
Maybe one of these would work?
> This is obviously a bit redundant but seems necessary unless something else in the method changes. My question about this is, under what conditions is the postprocessing dependent on the variable initialization, and how hard would it be to change this method so that the initialization is not redundant?
This happens in for example PPO, where the postprocessor is accessing the value function. If you comment it out, then PPO will crash during policy creation trying to read uninitialized weights.
Okay, so just to be clear, when you say redundant, are you referring to calling `tf.global_variables_initializer` instead of only initializing the uninitialized variables, or are you referring to there being two of those calls for every call to the init function?
>Maybe one of these would work?
That seems like it would. Do you think it's possible to just create a variable scope that only contains the new, uninitialized variables and then just initialize all of those? Or maybe just add them to a list as they're created and pass a reference back up to the init function?
Also, is it expected behavior for the initialization time to be growing with each new policy added? Is this something unique to PPO, or is it going to be observed no matter what algorithm we use?
Are there other algorithms better suited for large numbers of individual policies running on a single machine?
The former (which includes the latter, though double init is less of an
issue).
On Fri, Oct 25, 2019, 5:11 AM Brendan Whitaker <[email protected]>
wrote:
> Okay, so just to be clear, when you say redundant, are you referring to
> calling tf.global_variables_initializer and initializing everything on
> each of those calls, or are you referring to there being two of those calls
> for every call to the init function?
>
> —
> You are receiving this because you were assigned.
> Reply to this email directly, view it on GitHub
> <https://github.com/ray-project/ray/issues/5982?email_source=notifications&email_token=AAADUSWGLFRZ2T4UX4BPOODQQLO6JA5CNFSM4JEA3DT2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOECIFA6Y#issuecomment-546328699>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAADUSS6IPZ3UYTEBZEYLVLQQLO6JANCNFSM4JEA3DTQ>
> .
>
Looking through the stack overflow page, none of those answers are super clean but one of them is not as slow, with only one sess.run to find which variables are uninitialized. We are going to try a workaround and see how the performance is. Also, I am curious about what Brendan asked about regarding the initialization time growing. Do you know if this is something we should expect from initialization, that the initialization time will be larger for the last agents initialized than for the first, even if we implement one of the workarounds above? Thanks.
@mtcrawshaw I don't think it should be slowing down over time once the redundant inits are removed, if so that might be a bug in TensorFlow.
So we have implemented a couple of the workarounds from the stack overflow page that you mentioned, and we noticed some weird behavior from RLlib that we wanted to ask you about. In policy/dynamic_tf_policy.py, we replaced
```python
self._sess.run(tf.global_variables_initializer())
```
on lines 291 and 350 with
```python
initialize_uninitialized(self._sess)
```
where ``initialize_uninitialized`` defined by
```python
def initialize_uninitialized(sess):
bytestr_var_names = sess.run(tf.report_uninitialized_variables())
global_vars = tf.global_variables()
strnames = [name.decode("utf-8") for name in bytestr_var_names]
uninit_vars = [
v
for v in tf.global_variables()
if v.name.split(":")[0]
in set(strnames)
]
initializer = tf.variables_initializer(uninit_vars)
sess.run(initializer)
```
However, we still were getting an increase in the initialization time as the number of initialized agents grew. We threw a couple of print statements in ``initialize_uninitialized`` to get the names and number of uninitialized variables, modified version below.
```python
def initialize_uninitialized(sess):
bytestr_var_names = sess.run(tf.report_uninitialized_variables())
global_vars = tf.global_variables()
strnames = [name.decode("utf-8") for name in bytestr_var_names]
uninit_vars = [
v
for v in tf.global_variables()
if v.name.split(":")[0]
in set(strnames)
]
print("Names of uninitialized variables: %s" % str([v.name for v in uninit_vars]))
print("Number of uninitvars: %d" % len(uninit_vars))
print("Number of names from report: %d" % len(bytestr_var_names))
print("Number of variables: %d" % len(global_vars))
initializer = tf.variables_initializer(uninit_vars)
sess.run(initializer)
```
I won't provide the output here because it is quite long, but it shows that the variables which get initialized during the initialization of the first agent show up as uninitialized during the initialization of the second agent! The variables corresponding to the first and second agent are then initialized, but during the initialization of the third agent, they again reappear as uninitialized. My question is, do you have any idea why these variables might be getting "de-initialized" at any point during the initialization process?
That's really weird, perhaps try printing out the current TF graph id?
Maybe there are new graphs being created each time. I can't think of any
code that would be uninitializing variables otherwise.
On Tue, Oct 29, 2019, 5:57 AM Michael Crawshaw <[email protected]>
wrote:
> So we have implemented a couple of the workarounds from the stack overflow
> page that you mentioned, and we noticed some weird behavior from RLlib that
> we wanted to ask you about. In policy/dynamic_tf_policy.py, we replaced
>
> self._sess.run(tf.global_variables_initializer())
>
> with a call to initialize_uninitialized(self._sess), where
> initialize_uninitialized defined by
>
> def initialize_uninitialized(sess):
> bytestr_var_names = sess.run(tf.report_uninitialized_variables())
> global_vars = tf.global_variables()
> strnames = [name.decode("utf-8") for name in bytestr_var_names]
> uninit_vars = [
> v
> for v in tf.global_variables()
> if v.name.split(":")[0]
> in set(strnames)
> ]
> initializer = tf.variables_initializer(uninit_vars)
> sess.run(initializer)
>
> However, we still were getting an increase in the initialization time as
> the number of initialized agents grew. We threw a couple of print
> statements in initialize_uninitialized to get the names and number of
> uninitialized variables, modified version below.
>
> def initialize_uninitialized(sess):
> bytestr_var_names = sess.run(tf.report_uninitialized_variables())
> global_vars = tf.global_variables()
> strnames = [name.decode("utf-8") for name in bytestr_var_names]
> uninit_vars = [
> v
> for v in tf.global_variables()
> if v.name.split(":")[0]
> in set(strnames)
> ]
> print("Names of uninitialized variables: %s" % str([v.name for v in uninit_vars]))
> print("Number of uninitvars: %d" % len(uninit_vars))
> print("Number of names from report: %d" % len(bytestr_var_names))
> print("Number of variables: %d" % len(global_vars))
> initializer = tf.variables_initializer(uninit_vars)
> sess.run(initializer)
>
> I won't provide the output here because it is quite long, but it shows
> that the variables which get initialized during the initialization of the
> first agent show up as uninitialized during the initialization of the
> second agent! The variables corresponding to the first and second agent are
> then initialized, but during the initialization of the third agent, they
> again reappear as uninitialized. My question is, do you have any idea why
> these variables might be getting "de-initialized" at any point during the
> initialization process?
>
> —
> You are receiving this because you were assigned.
> Reply to this email directly, view it on GitHub
> <https://github.com/ray-project/ray/issues/5982?email_source=notifications&email_token=AAADUSUTNO3MSFXURACF44TQRAXM3A5CNFSM4JEA3DT2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOECQMCAA#issuecomment-547406080>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAADUSQ56CLKJAL5S577XUTQRAXM3ANCNFSM4JEA3DTQ>
> .
>
Alright, so last week we were actually running that in a fork of RLLib. We ran the same tests in a fresh install, and found that the initialization time still grows, but that the number of uninitialized variables is constant (normal).
We think this is entirely because of the (3?) calls to the global variable initializer. We'll investigate the graph id thing.
I'm running into the same situation. As the agent initialization time grows linearly per-agent, this means that there is superlinear growth in total initialization time as the number of agents increases.
My agents all use the same type of custom policy, but each have their own instance of that policy. Although a single part of the graph does grow exponentially with the amount of agents, it is insignificant compared to the rest of of the parameters: a change of <20k parameters versus hundreds of thousands of other fixed parameters.
I ran cprofile/kcachegrind with `local_mode=True` to take a peek.
- 2 agents: Initialization takes 44 seconds
- 4 agents: Initialization takes 172 seconds
- 5 agents: Initialization takes 275 seconds
Respectively, their call graphs look like this:
As the number of agents grows, the 3 functions that dominate are:
1. _pywrap_tensorflow_internal.TF_SessionRun_wrapper
2. _pywrap_tensorflow_internal.ExtendSession
3. _pywrap_tensorflow_internal.TF_SessionMakeCallable
If anyone wants to dig into this, check out these [3 profiler files](https://github.com/ray-project/ray/files/4586880/profiler_files.zip). You can start one in KCachegrind with the following command:
`pyprof2calltree -i prof-2.out -k`
I experimented a bit, and I've found [this line](https://github.com/ray-project/ray/blob/master/rllib/optimizers/multi_gpu_optimizer.py#L139) `self.workers.local_worker().get_weights()` to be the slowest (makes up for 80-90% of initialization time with 5 agents), but only when it is called for the first time. Subsequent calls are far, far faster.
Somewhere down the call stack Ray then calls [this function](https://github.com/ray-project/ray/blob/master/python/ray/experimental/tf_utils.py#L157) for each policy, for each element of that policy. The tf graph is the same size for each agent, and grows linearly (in my case, 2 agents/80k elements, 5 agents/200k elements). Thus, you get a linearly increasing amount of eval calls to a linearly increasingly large graph = exponential growth.
I'm not sure whether the graph size increase is intentional: each policy is after all evaluated independently, as shown in these two lines in sampler.py {[1](https://github.com/ray-project/ray/blob/releases/0.8.5/rllib/evaluation/sampler.py#L590), [2](https://github.com/ray-project/ray/blob/releases/0.8.5/rllib/evaluation/sampler.py#L613)}. Is this done so that optimization is faster?
Here's a graph for memory use while evaluating/training on a small amount of steps (30k-ish) with respectively `[2,3,4,5]` agents. The longest flat lines in each segment are where get_weights() is being called.
| 2020-05-18T21:18:56 |
|
ray-project/ray | 8,493 | ray-project__ray-8493 | [
"8482"
] | aa1cbe8abc845e1a6acf2f237b4b00aa7c3f295d | diff --git a/python/ray/__init__.py b/python/ray/__init__.py
--- a/python/ray/__init__.py
+++ b/python/ray/__init__.py
@@ -9,9 +9,13 @@
# raylet modules.
if "pickle5" in sys.modules:
- raise ImportError("Ray must be imported before pickle5 because Ray "
- "requires a specific version of pickle5 (which is "
- "packaged along with Ray).")
+ import pkg_resources
+ version_info = pkg_resources.require("pickle5")
+ version = tuple(int(n) for n in version_info[0].version.split("."))
+ if version < (0, 0, 10):
+ raise ImportError("You are using an old version of pickle5 that "
+ "leaks memory, please run 'pip install pickle5 -U' "
+ "to upgrade")
if "OMP_NUM_THREADS" not in os.environ:
logger.debug("[ray] Forcing OMP_NUM_THREADS=1 to avoid performance "
| import error
ray 0.8.5,when I try to use ray， it occurs Ray must be imported before pickle5 because Ray requires a specific version of pickle5 (which is packaged along with Ray.
I want to know it must import pickle5 before import ray, Right?
| yes, we are using an internal version of pickle5, because the current pickle5 has memory leak issues. so we have to import ray before importing the pickle5 library.
I have created a PR in the upstream (pickle5-backport), it would address the problem once merged. | 2020-05-18T21:44:03 |
|
ray-project/ray | 8,511 | ray-project__ray-8511 | [
"8492"
] | f8f7efc24f77e4491bc41daa7fb800cd07e524b2 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -1195,10 +1195,6 @@ def connect(node,
worker.lock = threading.RLock()
- # Create an object for interfacing with the global state.
- ray.state.state._initialize_global_state(
- node.redis_address, redis_password=node.redis_password)
-
driver_name = ""
log_stdout_file_name = ""
log_stderr_file_name = ""
@@ -1269,6 +1265,12 @@ def connect(node,
log_stderr_file_name,
)
+ # Create an object for interfacing with the global state.
+ # Note, global state should be intialized after `CoreWorker`, because it will
+ # use glog, which is intialized in `CoreWorker`.
+ ray.state.state._initialize_global_state(
+ node.redis_address, redis_password=node.redis_password)
+
if driver_object_store_memory is not None:
worker.core_worker.set_object_store_client_options(
"ray_driver_{}".format(os.getpid()), driver_object_store_memory)
| Excess INFO messages to console in latest build
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Master build logs this to console on ray init:
```
>>> (pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.151492 3131 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.166244 3131 redis_client.cc:141] RedisClient connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.168131 3135 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.174638 3131 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.175307 3131 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.177665 3132 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.178391 3135 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.181085 3132 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.187088 3135 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.187737 3135 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) I0518 14:41:09.194645 3132 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.195237 3132 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.226564 3133 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.230108 3133 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.238509 3133 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.239135 3133 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.279531 3136 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.280068 3136 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.288298 3136 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.288733 3136 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.300333 3130 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.301705 3130 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.309738 3130 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.310228 3130 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.316042 3134 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.317946 3134 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.326037 3134 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.326454 3134 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
(pid=raylet) WARNING: Logging before InitGoogleLogging() is written to STDERR
(pid=raylet) I0518 14:41:09.357240 3137 global_state_accessor.cc:25] Redis server address = 192.168.5.121:6379, is test flag = 0
(pid=raylet) I0518 14:41:09.357900 3137 redis_client.cc:141] RedisClient connected.
(pid=raylet) I0518 14:41:09.366128 3137 redis_gcs_client.cc:88] RedisGcsClient Connected.
(pid=raylet) I0518 14:41:09.366888 3137 service_based_gcs_client.cc:66] ServiceBasedGcsClient Connected.
```
| @ffbin is this related to your recent change?
@ericl This log is related to this PR. https://github.com/ray-project/ray/pull/8401 I will take a look, thanks. | 2020-05-20T03:31:57 |
|
ray-project/ray | 8,521 | ray-project__ray-8521 | [
"5821"
] | 6b53df95990bfac235313f24f80176948339b606 | diff --git a/python/ray/tune/examples/mlflow_example.py b/python/ray/tune/examples/mlflow_example.py
--- a/python/ray/tune/examples/mlflow_example.py
+++ b/python/ray/tune/examples/mlflow_example.py
@@ -41,7 +41,9 @@ def easy_objective(config):
num_samples=5,
loggers=DEFAULT_LOGGERS + (MLFLowLogger, ),
config={
- "mlflow_experiment_id": experiment_id,
+ "logger_config": {
+ "mlflow_experiment_id": experiment_id,
+ },
"width": tune.sample_from(
lambda spec: 10 + int(90 * random.random())),
"height": tune.sample_from(lambda spec: int(100 * random.random()))
diff --git a/python/ray/tune/logger.py b/python/ray/tune/logger.py
--- a/python/ray/tune/logger.py
+++ b/python/ray/tune/logger.py
@@ -78,9 +78,12 @@ class MLFLowLogger(Logger):
"""
def _init(self):
+ logger_config = self.config.get("logger_config", {})
from mlflow.tracking import MlflowClient
- client = MlflowClient()
- run = client.create_run(self.config.get("mlflow_experiment_id"))
+ client = MlflowClient(
+ tracking_uri=logger_config.get("mlflow_tracking_uri"),
+ registry_uri=logger_config.get("mlflow_registry_uri"))
+ run = client.create_run(logger_config.get("mlflow_experiment_id"))
self._run_id = run.info.run_id
for key, value in self.config.items():
client.log_param(self._run_id, key, value)
diff --git a/rllib/agents/trainer.py b/rllib/agents/trainer.py
--- a/rllib/agents/trainer.py
+++ b/rllib/agents/trainer.py
@@ -370,6 +370,10 @@
"replay_mode": "independent",
},
+ # === Logger ===
+ # Define logger-specific configuration to be used inside Logger
+ "logger_config": {},
+
# === Replay Settings ===
# The number of contiguous environment steps to replay at once. This may
# be set to greater than 1 to support recurrent models.
| MLFlow parameter error using rllib agents
<!--
General questions should be asked on the mailing list [email protected].
Questions about how to use Ray should be asked on
[StackOverflow](https://stackoverflow.com/questions/tagged/ray).
Before submitting an issue, please fill out the following form.
-->
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: MacOS 10.14.6
- **Ray installed from (source or binary)**: source
- **Ray version**: 0.7.5
- **Python version**: 3.7.3
- **Exact command to reproduce**:
<!--
You can obtain the Ray version with
python -c "import ray; print(ray.__version__)"
-->
### Describe the problem
<!-- Describe the problem clearly here. -->
When I tried to integrate MLFlow to my experiment that uses a DQN agent, I get the following error:
```
Trainable.__init__(self, config, logger_creator)
File "/python3.7/site-packages/ray/tune/trainable.py", line 96, in __init__
self._setup(copy.deepcopy(self.config))
File "/python3.7/site-packages/ray/rllib/agents/trainer.py", line 478, in _setup
self._allow_unknown_subkeys)
File "/python3.7/site-packages/ray/tune/util.py", line 160, in deep_update
raise Exception("Unknown config parameter `{}` ".format(k))
Exception: Unknown config parameter mlflow_experiment_id
```
It seems like the agent's config is quite frozen, and there doesn't seem to be an explicit way to allow new keys to that config.
When I digged a bit in the code, I found, in the `rllib/agents/trainer.py` that there's an argument to the deep_update function that merges configs together called `allow_unknown_configs` and that is by default set to False. When I set it to True, for testing purposes, it worked.
So maybe we should add an abstraction layer to that so that it can easily be set from the experiment itself.
### Source code / logs
This is part of the source code of when I ran the experiment on a DQN agent. I got the error above
```
tune.run(
...
config={
"mlflow_experiment_id": experiment_id,
...
)
```
| I have a similar, but not MLflow related, problem. The basic question is the same: How do you pass arguments via the config if the agent does not allow additional config values.
Maybe an abstraction layer to allow additional parameters makes sense.
Or maybe allowing a field like `"custom_config"` in the config dict should be allowed all the time.
I have the same issue, I have to hack the code in trainer.py
put `_allow_unknown_configs=True`. Only in this way I am able to use MLflow
@fatenghriss | 2020-05-20T14:02:19 |
|
ray-project/ray | 8,533 | ray-project__ray-8533 | [
"8523",
"8523"
] | d27e6da1b291887707533cc10f066cd0e888607b | diff --git a/rllib/agents/qmix/qmix_policy.py b/rllib/agents/qmix/qmix_policy.py
--- a/rllib/agents/qmix/qmix_policy.py
+++ b/rllib/agents/qmix/qmix_policy.py
@@ -13,11 +13,13 @@
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.modelv2 import _unpack_obs
from ray.rllib.env.constants import GROUP_REWARDS
+from ray.rllib.utils import try_import_tree
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.annotations import override
# Torch must be installed.
torch, nn = try_import_torch(error=True)
+tree = try_import_tree()
logger = logging.getLogger(__name__)
@@ -463,25 +465,28 @@ def _unpack_observation(self, obs_batch):
state (np.ndarray or None): state tensor of shape [B, state_size]
or None if it is not in the batch
"""
+
unpacked = _unpack_obs(
np.array(obs_batch, dtype=np.float32),
self.observation_space.original_space,
tensorlib=np)
+
+ if isinstance(unpacked[0], dict):
+ unpacked_obs = [
+ np.concatenate(tree.flatten(u["obs"]), 1) for u in unpacked
+ ]
+ else:
+ unpacked_obs = unpacked
+
+ obs = np.concatenate(
+ unpacked_obs,
+ axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
+
if self.has_action_mask:
- obs = np.concatenate(
- [o["obs"] for o in unpacked],
- axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
action_mask = np.concatenate(
[o["action_mask"] for o in unpacked], axis=1).reshape(
[len(obs_batch), self.n_agents, self.n_actions])
else:
- if isinstance(unpacked[0], dict):
- unpacked_obs = [u["obs"] for u in unpacked]
- else:
- unpacked_obs = unpacked
- obs = np.concatenate(
- unpacked_obs,
- axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
action_mask = np.ones(
[len(obs_batch), self.n_agents, self.n_actions],
dtype=np.float32)
| diff --git a/rllib/tests/test_avail_actions_qmix.py b/rllib/tests/test_avail_actions_qmix.py
--- a/rllib/tests/test_avail_actions_qmix.py
+++ b/rllib/tests/test_avail_actions_qmix.py
@@ -1,4 +1,4 @@
-from gym.spaces import Tuple, Discrete, Dict, Box
+from gym.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
import numpy as np
import unittest
@@ -9,10 +9,17 @@
class AvailActionsTestEnv(MultiAgentEnv):
- action_space = Discrete(10)
+ num_actions = 10
+ action_space = Discrete(num_actions)
observation_space = Dict({
- "obs": Discrete(3),
- "action_mask": Box(0, 1, (10, )),
+ "obs": Dict({
+ "test": Dict({
+ "a": Discrete(2),
+ "b": MultiDiscrete([2, 3, 4])
+ }),
+ "state": MultiDiscrete([2, 2, 2])
+ }),
+ "action_mask": Box(0, 1, (num_actions, )),
})
def __init__(self, env_config):
@@ -25,7 +32,7 @@ def reset(self):
self.state = 0
return {
"agent_1": {
- "obs": self.state,
+ "obs": self.observation_space["obs"].sample(),
"action_mask": self.action_mask
}
}
@@ -36,7 +43,12 @@ def step(self, action_dict):
"Failed to obey available actions mask!"
self.state += 1
rewards = {"agent_1": 1}
- obs = {"agent_1": {"obs": 0, "action_mask": self.action_mask}}
+ obs = {
+ "agent_1": {
+ "obs": self.observation_space["obs"].sample(),
+ "action_mask": self.action_mask
+ }
+ }
dones = {"__all__": self.state > 20}
return obs, rewards, dones, {}
| [rllib] Make QMix support complex observation spaces
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
The QMix example and the default model only uses a simple MultiDiscrete observation space. It would be nice if more abstract observation spaces and custom models would be supported. The following example using a nested dict observation space ([based on the two step game](https://github.com/ray-project/ray/blob/master/rllib/examples/env/two_step_game.py)) fails [here](https://github.com/ray-project/ray/blob/master/rllib/agents/qmix/qmix_policy.py#L482) (I am aware that the model would have to be adapted as well, but that does not seem to be the cause of this error):
```
import argparse
from gym.spaces import Tuple, MultiDiscrete, Dict, Discrete
import ray
from ray import tune
from ray.tune import register_env, grid_search
from ray.rllib.env.multi_agent_env import ENV_STATE
from ray.rllib.examples.env.two_step_game import TwoStepGame
from ray.rllib.utils.test_utils import check_learning_achieved
from gym.spaces import MultiDiscrete, Dict, Discrete
import numpy as np
from ray.rllib.env.multi_agent_env import MultiAgentEnv, ENV_STATE
class TwoStepGame(MultiAgentEnv):
action_space = Discrete(2)
def __init__(self, env_config):
self.state = None
self.agent_1 = 0
self.agent_2 = 1
# MADDPG emits action logits instead of actual discrete actions
self.actions_are_logits = env_config.get("actions_are_logits", False)
self.one_hot_state_encoding = env_config.get("one_hot_state_encoding",
False)
self.with_state = env_config.get("separate_state_space", False)
if not self.one_hot_state_encoding:
self.observation_space = Discrete(6)
self.with_state = False
else:
# Each agent gets the full state (one-hot encoding of which of the
# three states are active) as input with the receiving agent's
# ID (1 or 2) concatenated onto the end.
if self.with_state:
self.observation_space = Dict({
"obs": MultiDiscrete([2, 2, 2, 3]),
ENV_STATE: MultiDiscrete([2, 2, 2])
})
else:
self.observation_space = MultiDiscrete([2, 2, 2, 3])
def reset(self):
self.state = np.array([1, 0, 0])
return self._obs()
def step(self, action_dict):
if self.actions_are_logits:
action_dict = {
k: np.random.choice([0, 1], p=v)
for k, v in action_dict.items()
}
state_index = np.flatnonzero(self.state)
if state_index == 0:
action = action_dict[self.agent_1]
assert action in [0, 1], action
if action == 0:
self.state = np.array([0, 1, 0])
else:
self.state = np.array([0, 0, 1])
global_rew = 0
done = False
elif state_index == 1:
global_rew = 7
done = True
else:
if action_dict[self.agent_1] == 0 and action_dict[self.
agent_2] == 0:
global_rew = 0
elif action_dict[self.agent_1] == 1 and action_dict[self.
agent_2] == 1:
global_rew = 8
else:
global_rew = 1
done = True
rewards = {
self.agent_1: global_rew / 2.0,
self.agent_2: global_rew / 2.0
}
obs = self._obs()
dones = {"__all__": done}
infos = {}
return obs, rewards, dones, infos
def _obs(self):
if self.with_state:
return {
self.agent_1: {
"obs": {
"data": self.agent_1_obs(),
"test": 1
},
ENV_STATE: self.state
},
self.agent_2: {
"obs": {
"data": self.agent_2_obs(),
"test": 2
},
ENV_STATE: self.state
}
}
else:
return {
self.agent_1: self.agent_1_obs(),
self.agent_2: self.agent_2_obs()
}
def agent_1_obs(self):
if self.one_hot_state_encoding:
return np.concatenate([self.state, [1]])
else:
return np.flatnonzero(self.state)[0]
def agent_2_obs(self):
if self.one_hot_state_encoding:
return np.concatenate([self.state, [2]])
else:
return np.flatnonzero(self.state)[0] + 3
if __name__ == "__main__":
grouping = {
"group_1": [0, 1],
}
obs_space = Tuple([
Dict({
"obs": Dict({
'data': MultiDiscrete([2, 2, 2, 3]),
'test': Discrete(5)
}),
ENV_STATE: MultiDiscrete([2, 2, 2])
}),
Dict({
"obs": Dict({
'data': MultiDiscrete([2, 2, 2, 3]),
'test': Discrete(5)
}),
ENV_STATE: MultiDiscrete([2, 2, 2])
}),
])
act_space = Tuple([
TwoStepGame.action_space,
TwoStepGame.action_space,
])
register_env(
"grouped_twostep",
lambda config: TwoStepGame(config).with_agent_groups(
grouping, obs_space=obs_space, act_space=act_space))
ray.init()
results = tune.run("QMIX", config={
"rollout_fragment_length": 4,
"train_batch_size": 32,
"exploration_fraction": .4,
"exploration_final_eps": 0.0,
"num_workers": 0,
"mixer": "qmix",
"env_config": {
"separate_state_space": True,
"one_hot_state_encoding": True
},
"use_pytorch": True,
"env": "grouped_twostep"
})
ray.shutdown()
```
```
2020-05-20 15:40:03,477 ERROR trial_runner.py:519 -- Trial QMIX_grouped_twostep_c960c_00002: Error processing event.
Traceback (most recent call last):
File "[...]/ray/tune/trial_runner.py", line 467, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "[...]/ray/tune/ray_trial_executor.py", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File "[...]/ray/worker.py", line 1516, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::QMIX.train() (pid=54834, ip=128.232.69.20)
File "python/ray/_raylet.pyx", line 460, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 414, in ray._raylet.execute_task.function_executor
File "[...]/ray/rllib/agents/trainer.py", line 504, in train
raise e
File "[...]/ray/rllib/agents/trainer.py", line 490, in train
result = Trainable.train(self)
File "[...]/ray/tune/trainable.py", line 260, in train
result = self._train()
File "[...]/ray/rllib/agents/trainer_template.py", line 138, in _train
return self._train_exec_impl()
File "[...]/ray/rllib/agents/trainer_template.py", line 173, in _train_exec_impl
res = next(self.train_exec_impl)
File "[...]/ray/util/iter.py", line 689, in __next__
return next(self.built_iterator)
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 977, in build_union
item = next(it)
File "[...]/ray/util/iter.py", line 689, in __next__
return next(self.built_iterator)
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/rllib/execution/rollout_ops.py", line 70, in sampler
yield workers.local_worker().sample()
File "[...]/ray/rllib/evaluation/rollout_worker.py", line 515, in sample
batches = [self.input_reader.next()]
File "[...]/ray/rllib/evaluation/sampler.py", line 56, in next
batches = [self.get_data()]
File "[...]/ray/rllib/evaluation/sampler.py", line 101, in get_data
item = next(self.rollout_provider)
File "[...]/ray/rllib/evaluation/sampler.py", line 367, in _env_runner
active_episodes)
File "[...]/ray/rllib/evaluation/sampler.py", line 637, in _do_policy_eval
timestep=policy.global_timestep)
File "[...]/ray/rllib/agents/qmix/qmix_policy.py", line 260, in compute_actions
obs_batch, action_mask, _ = self._unpack_observation(obs_batch)
File "[...]/ray/rllib/agents/qmix/qmix_policy.py", line 486, in _unpack_observation
axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
File "<__array_function__ internals>", line 6, in concatenate
ValueError: zero-dimensional arrays cannot be concatenated
```
[rllib] Make QMix support complex observation spaces
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
The QMix example and the default model only uses a simple MultiDiscrete observation space. It would be nice if more abstract observation spaces and custom models would be supported. The following example using a nested dict observation space ([based on the two step game](https://github.com/ray-project/ray/blob/master/rllib/examples/env/two_step_game.py)) fails [here](https://github.com/ray-project/ray/blob/master/rllib/agents/qmix/qmix_policy.py#L482) (I am aware that the model would have to be adapted as well, but that does not seem to be the cause of this error):
```
import argparse
from gym.spaces import Tuple, MultiDiscrete, Dict, Discrete
import ray
from ray import tune
from ray.tune import register_env, grid_search
from ray.rllib.env.multi_agent_env import ENV_STATE
from ray.rllib.examples.env.two_step_game import TwoStepGame
from ray.rllib.utils.test_utils import check_learning_achieved
from gym.spaces import MultiDiscrete, Dict, Discrete
import numpy as np
from ray.rllib.env.multi_agent_env import MultiAgentEnv, ENV_STATE
class TwoStepGame(MultiAgentEnv):
action_space = Discrete(2)
def __init__(self, env_config):
self.state = None
self.agent_1 = 0
self.agent_2 = 1
# MADDPG emits action logits instead of actual discrete actions
self.actions_are_logits = env_config.get("actions_are_logits", False)
self.one_hot_state_encoding = env_config.get("one_hot_state_encoding",
False)
self.with_state = env_config.get("separate_state_space", False)
if not self.one_hot_state_encoding:
self.observation_space = Discrete(6)
self.with_state = False
else:
# Each agent gets the full state (one-hot encoding of which of the
# three states are active) as input with the receiving agent's
# ID (1 or 2) concatenated onto the end.
if self.with_state:
self.observation_space = Dict({
"obs": MultiDiscrete([2, 2, 2, 3]),
ENV_STATE: MultiDiscrete([2, 2, 2])
})
else:
self.observation_space = MultiDiscrete([2, 2, 2, 3])
def reset(self):
self.state = np.array([1, 0, 0])
return self._obs()
def step(self, action_dict):
if self.actions_are_logits:
action_dict = {
k: np.random.choice([0, 1], p=v)
for k, v in action_dict.items()
}
state_index = np.flatnonzero(self.state)
if state_index == 0:
action = action_dict[self.agent_1]
assert action in [0, 1], action
if action == 0:
self.state = np.array([0, 1, 0])
else:
self.state = np.array([0, 0, 1])
global_rew = 0
done = False
elif state_index == 1:
global_rew = 7
done = True
else:
if action_dict[self.agent_1] == 0 and action_dict[self.
agent_2] == 0:
global_rew = 0
elif action_dict[self.agent_1] == 1 and action_dict[self.
agent_2] == 1:
global_rew = 8
else:
global_rew = 1
done = True
rewards = {
self.agent_1: global_rew / 2.0,
self.agent_2: global_rew / 2.0
}
obs = self._obs()
dones = {"__all__": done}
infos = {}
return obs, rewards, dones, infos
def _obs(self):
if self.with_state:
return {
self.agent_1: {
"obs": {
"data": self.agent_1_obs(),
"test": 1
},
ENV_STATE: self.state
},
self.agent_2: {
"obs": {
"data": self.agent_2_obs(),
"test": 2
},
ENV_STATE: self.state
}
}
else:
return {
self.agent_1: self.agent_1_obs(),
self.agent_2: self.agent_2_obs()
}
def agent_1_obs(self):
if self.one_hot_state_encoding:
return np.concatenate([self.state, [1]])
else:
return np.flatnonzero(self.state)[0]
def agent_2_obs(self):
if self.one_hot_state_encoding:
return np.concatenate([self.state, [2]])
else:
return np.flatnonzero(self.state)[0] + 3
if __name__ == "__main__":
grouping = {
"group_1": [0, 1],
}
obs_space = Tuple([
Dict({
"obs": Dict({
'data': MultiDiscrete([2, 2, 2, 3]),
'test': Discrete(5)
}),
ENV_STATE: MultiDiscrete([2, 2, 2])
}),
Dict({
"obs": Dict({
'data': MultiDiscrete([2, 2, 2, 3]),
'test': Discrete(5)
}),
ENV_STATE: MultiDiscrete([2, 2, 2])
}),
])
act_space = Tuple([
TwoStepGame.action_space,
TwoStepGame.action_space,
])
register_env(
"grouped_twostep",
lambda config: TwoStepGame(config).with_agent_groups(
grouping, obs_space=obs_space, act_space=act_space))
ray.init()
results = tune.run("QMIX", config={
"rollout_fragment_length": 4,
"train_batch_size": 32,
"exploration_fraction": .4,
"exploration_final_eps": 0.0,
"num_workers": 0,
"mixer": "qmix",
"env_config": {
"separate_state_space": True,
"one_hot_state_encoding": True
},
"use_pytorch": True,
"env": "grouped_twostep"
})
ray.shutdown()
```
```
2020-05-20 15:40:03,477 ERROR trial_runner.py:519 -- Trial QMIX_grouped_twostep_c960c_00002: Error processing event.
Traceback (most recent call last):
File "[...]/ray/tune/trial_runner.py", line 467, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "[...]/ray/tune/ray_trial_executor.py", line 430, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File "[...]/ray/worker.py", line 1516, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(ValueError): ray::QMIX.train() (pid=54834, ip=128.232.69.20)
File "python/ray/_raylet.pyx", line 460, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 414, in ray._raylet.execute_task.function_executor
File "[...]/ray/rllib/agents/trainer.py", line 504, in train
raise e
File "[...]/ray/rllib/agents/trainer.py", line 490, in train
result = Trainable.train(self)
File "[...]/ray/tune/trainable.py", line 260, in train
result = self._train()
File "[...]/ray/rllib/agents/trainer_template.py", line 138, in _train
return self._train_exec_impl()
File "[...]/ray/rllib/agents/trainer_template.py", line 173, in _train_exec_impl
res = next(self.train_exec_impl)
File "[...]/ray/util/iter.py", line 689, in __next__
return next(self.built_iterator)
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 772, in apply_filter
for item in it:
File "[...]/ray/util/iter.py", line 977, in build_union
item = next(it)
File "[...]/ray/util/iter.py", line 689, in __next__
return next(self.built_iterator)
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/util/iter.py", line 702, in apply_foreach
for item in it:
File "[...]/ray/rllib/execution/rollout_ops.py", line 70, in sampler
yield workers.local_worker().sample()
File "[...]/ray/rllib/evaluation/rollout_worker.py", line 515, in sample
batches = [self.input_reader.next()]
File "[...]/ray/rllib/evaluation/sampler.py", line 56, in next
batches = [self.get_data()]
File "[...]/ray/rllib/evaluation/sampler.py", line 101, in get_data
item = next(self.rollout_provider)
File "[...]/ray/rllib/evaluation/sampler.py", line 367, in _env_runner
active_episodes)
File "[...]/ray/rllib/evaluation/sampler.py", line 637, in _do_policy_eval
timestep=policy.global_timestep)
File "[...]/ray/rllib/agents/qmix/qmix_policy.py", line 260, in compute_actions
obs_batch, action_mask, _ = self._unpack_observation(obs_batch)
File "[...]/ray/rllib/agents/qmix/qmix_policy.py", line 486, in _unpack_observation
axis=1).reshape([len(obs_batch), self.n_agents, self.obs_size])
File "<__array_function__ internals>", line 6, in concatenate
ValueError: zero-dimensional arrays cannot be concatenated
```
| 2020-05-21T10:26:15 |
|
ray-project/ray | 8,572 | ray-project__ray-8572 | [
"8368"
] | 351839bf698dec8dd8592ef70cd063ceab778d0e | diff --git a/rllib/agents/ppo/ppo_torch_policy.py b/rllib/agents/ppo/ppo_torch_policy.py
--- a/rllib/agents/ppo/ppo_torch_policy.py
+++ b/rllib/agents/ppo/ppo_torch_policy.py
@@ -223,4 +223,7 @@ def setup_mixins(policy, obs_space, action_space, config):
extra_grad_process_fn=apply_grad_clipping,
before_init=setup_config,
after_init=setup_mixins,
- mixins=[KLCoeffMixin, ValueNetworkMixin])
+ mixins=[
+ LearningRateSchedule, EntropyCoeffSchedule, KLCoeffMixin,
+ ValueNetworkMixin
+ ])
| "lr_schedule" option ignored using torch framework and PPO algorithm
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
*Ray version and other system information (Python version, TensorFlow version, OS):*
- Ray: [0.9.0.dev0 (2c599dbf05e41e338920ee2fbe692658bcbec4dd)](https://s3-us-west-2.amazonaws.com/ray-wheels/releases/0.8.5/02c1ab0ec6d615ad54ebf33bd93c51c04000534e/ray-0.8.5-cp36-cp36m-manylinux1_x86_64.whl)
- CUDA: 10.1
- Pytorch: 1.4.0 with GPU support
- Ubuntu 18.04
- Python 3.6
### What is the problem?
Setting the hyperparameter "lr_schedule" as no effect when using PyTorch as backend framework and PPO learning algorithm.
### Reproduction (REQUIRED)
```
import ray
from ray.rllib.agents.ppo import PPOTrainer, DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
for key, val in {
"env": "CartPole-v0",
"num_workers": 0,
"use_pytorch": False,
"lr": 1.0e-5,
"lr_schedule": [
[0, 1.0e-6],
[1, 1.0e-7],
]
}.items(): config[key] = val
ray.init()
for use_pytorch in [False, True]:
config["use_pytorch"] = use_pytorch
agent = PPOTrainer(config, "CartPole-v0")
for _ in range(2):
result = agent.train()
print(f"use_pytorch: {use_pytorch} - Current learning rate: "\
f"{result['info']['learner']['default_policy']['cur_lr']}")
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| Thanks for filing this issue @duburcqa . Will take a look ...
cc @krfricke
I can confirm this - would appreciate an update on it | 2020-05-23T10:07:20 |
|
ray-project/ray | 8,617 | ray-project__ray-8617 | [
"8614",
"8614"
] | b0bb0584fb6e2a293eea7620f1a83d0350bdce9f | diff --git a/rllib/agents/a3c/a3c.py b/rllib/agents/a3c/a3c.py
--- a/rllib/agents/a3c/a3c.py
+++ b/rllib/agents/a3c/a3c.py
@@ -54,11 +54,6 @@ def get_policy_class(config):
def validate_config(config):
if config["entropy_coeff"] < 0:
raise DeprecationWarning("entropy_coeff must be >= 0")
- if config["sample_async"] and config["use_pytorch"]:
- config["sample_async"] = False
- logger.warning(
- "The sample_async option is not supported with use_pytorch: "
- "Multithreading can be lead to crashes if used with pytorch.")
def execution_plan(workers, config):
| [rllib] PyTorch and SampleAsync validation
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
PyTorch is supposed to be thread-safe, as long as you don't write a tensor using multiple threads. Please see https://discuss.pytorch.org/t/is-pytorch-supposed-to-be-thread-safe/36540/2
It might be worth removing the validation of sample_async and use_pytorch for A3C (and maybe others?).
Ray Version 0.9.0dev (but this applies to any ray version actually)
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
[rllib] PyTorch and SampleAsync validation
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
PyTorch is supposed to be thread-safe, as long as you don't write a tensor using multiple threads. Please see https://discuss.pytorch.org/t/is-pytorch-supposed-to-be-thread-safe/36540/2
It might be worth removing the validation of sample_async and use_pytorch for A3C (and maybe others?).
Ray Version 0.9.0dev (but this applies to any ray version actually)
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-05-26T15:16:42 |
||
ray-project/ray | 8,628 | ray-project__ray-8628 | [
"8254"
] | cd5a207d69cdaf05b47d956c18e89d928585eec7 | diff --git a/python/ray/node.py b/python/ray/node.py
--- a/python/ray/node.py
+++ b/python/ray/node.py
@@ -4,6 +4,7 @@
import errno
import os
import logging
+import random
import signal
import socket
import subprocess
@@ -24,6 +25,7 @@
logger = logging.getLogger(__name__)
SESSION_LATEST = "session_latest"
+NUMBER_OF_PORT_RETRIES = 40
class Node:
@@ -167,7 +169,8 @@ def __init__(self,
# NOTE: There is a possible but unlikely race condition where
# the port is bound by another process between now and when the
# raylet starts.
- self._ray_params.node_manager_port = self._get_unused_port()
+ self._ray_params.node_manager_port, self._socket = \
+ self._get_unused_port(close_on_exit=False)
if not connect_only and spawn_reaper and not self.kernel_fate_share:
self.start_reaper_process()
@@ -300,6 +303,14 @@ def node_manager_port(self):
"""Get the node manager's port."""
return self._ray_params.node_manager_port
+ @property
+ def socket(self):
+ """Get the socket reserving the node manager's port"""
+ try:
+ return self._socket
+ except AttributeError:
+ return None
+
@property
def address_info(self):
"""Get a dictionary of addresses."""
@@ -395,12 +406,30 @@ def new_log_files(self, name):
log_stderr_file = open(log_stderr, "a", buffering=1)
return log_stdout_file, log_stderr_file
- def _get_unused_port(self):
+ def _get_unused_port(self, close_on_exit=True):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("", 0))
port = s.getsockname()[1]
- s.close()
- return port
+
+ # Try to generate a port that is far above the 'next available' one.
+ # This solves issue #8254 where GRPC fails because the port assigned
+ # from this method has been used by a different process.
+ for _ in range(NUMBER_OF_PORT_RETRIES):
+ new_port = random.randint(port, 65535)
+ new_s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ try:
+ new_s.bind(("", new_port))
+ except OSError:
+ new_s.close()
+ continue
+ s.close()
+ if close_on_exit:
+ new_s.close()
+ return new_port, new_s
+ logger.error("Unable to succeed in selecting a random port.")
+ if close_on_exit:
+ s.close()
+ return port, s
def _prepare_socket_file(self, socket_path, default_prefix):
"""Prepare the socket file for raylet and plasma.
@@ -417,7 +446,7 @@ def _prepare_socket_file(self, socket_path, default_prefix):
if sys.platform == "win32":
if socket_path is None:
result = "tcp://{}:{}".format(self._localhost,
- self._get_unused_port())
+ self._get_unused_port()[0])
else:
if socket_path is None:
result = self._make_inc_temp(
@@ -598,7 +627,8 @@ def start_raylet(self, use_valgrind=False, use_profiler=False):
include_java=self._ray_params.include_java,
java_worker_options=self._ray_params.java_worker_options,
load_code_from_local=self._ray_params.load_code_from_local,
- fate_share=self.kernel_fate_share)
+ fate_share=self.kernel_fate_share,
+ socket_to_use=self.socket)
assert ray_constants.PROCESS_TYPE_RAYLET not in self.all_processes
self.all_processes[ray_constants.PROCESS_TYPE_RAYLET] = [process_info]
diff --git a/python/ray/services.py b/python/ray/services.py
--- a/python/ray/services.py
+++ b/python/ray/services.py
@@ -1230,7 +1230,8 @@ def start_raylet(redis_address,
include_java=False,
java_worker_options=None,
load_code_from_local=False,
- fate_share=None):
+ fate_share=None,
+ socket_to_use=None):
"""Start a raylet, which is a combined local scheduler and object manager.
Args:
@@ -1361,6 +1362,8 @@ def start_raylet(redis_address,
"--temp_dir={}".format(temp_dir),
"--session_dir={}".format(session_dir),
]
+ if socket_to_use:
+ socket_to_use.close()
process_info = start_ray_process(
command,
ray_constants.PROCESS_TYPE_RAYLET,
| [Core] Node Failure Long Running Test Failed at `grpc::ServerInterface::RegisteredAsyncRequest::IssueRequest()`
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Node Failure Long Running test fails with the error log below.
```
[2m[33m(pid=raylet)[0m E0429 02:32:06.263886 22036 process.cc:274] Failed to wait for process 22047 with error system:10: No child processes
E0429 02:32:12.346844 23272 task_manager.cc:288] 3 retries left for task b48f33dc1265b526ffffffff0100, attempting to resubmit.
E0429 02:32:12.346899 23272 core_worker.cc:373] Will resubmit task after a 5000ms delay: Type=NORMAL_TASK, Language=PYTHON, function_descriptor={type=PythonFunctionDescriptor, module_name=__main__, class_name=, function_name=f, function_hash=7d2c6c88e5e801d48a350076f2117e717fe12224}, task_id=b48f33dc1265b526ffffffff0100, job_id=0100, num_args=2, num_returns=1
[2m[33m(pid=raylet)[0m E0429 02:32:12.347446 22089 process.cc:274] Failed to wait for process 22100 with error system:10: No child processes
2020-04-29 02:32:12,653 INFO resource_spec.py:212 -- Starting Ray with 27.88 GiB memory available for workers and up to 0.15 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
[2m[33m(pid=raylet)[0m E0429 02:32:12.732946757 22142 server_chttp2.cc:40] {"created":"@1588127532.732848116","description":"No address added out of total 1 resolved","file":"external/com_github_grpc_grpc/src/core/ext/transport/chttp2/server/chttp2_server.cc","file_line":394,"referenced_errors":[{"created":"@1588127532.732846227","description":"Failed to add any wildcard listeners","file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_posix.cc","file_line":341,"referenced_errors":[{"created":"@1588127532.732832876","description":"Unable to configure socket","fd":44,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":208,"referenced_errors":[{"created":"@1588127532.732823689","description":"Address already in use","errno":98,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":181,"os_error":"Address already in use","syscall":"bind"}]},{"created":"@1588127532.732845812","description":"Unable to configure socket","fd":44,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":208,"referenced_errors":[{"created":"@1588127532.732843382","description":"Address already in use","errno":98,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":181,"os_error":"Address already in use","syscall":"bind"}]}]}]}
[2m[33m(pid=raylet)[0m *** Aborted at 1588127532 (unix time) try "date -d @1588127532" if you are using GNU date ***
[2m[33m(pid=raylet)[0m PC: @ 0x0 (unknown)
[2m[33m(pid=raylet)[0m *** SIGSEGV (@0x58) received by PID 22142 (TID 0x7fc3a66d37c0) from PID 88; stack trace: ***
[2m[33m(pid=raylet)[0m @ 0x7fc3a5c32390 (unknown)
[2m[33m(pid=raylet)[0m @ 0x5596e3957692 grpc::ServerInterface::RegisteredAsyncRequest::IssueRequest()
[2m[33m(pid=raylet)[0m @ 0x5596e35b2149 ray::rpc::NodeManagerService::WithAsyncMethod_RequestWorkerLease<>::RequestRequestWorkerLease()
[2m[33m(pid=raylet)[0m @ 0x5596e35c7b1b ray::rpc::ServerCallFactoryImpl<>::CreateCall()
[2m[33m(pid=raylet)[0m @ 0x5596e380bfe1 ray::rpc::GrpcServer::Run()
[2m[33m(pid=raylet)[0m @ 0x5596e3629acc ray::raylet::NodeManager::NodeManager()
[2m[33m(pid=raylet)[0m @ 0x5596e35cbc07 ray::raylet::Raylet::Raylet()
[2m[33m(pid=raylet)[0m @ 0x5596e359848d main
[2m[33m(pid=raylet)[0m @ 0x7fc3a5459830 __libc_start_main
[2m[33m(pid=raylet)[0m @ 0x5596e35a9391 (unknown)
[2m[36m(pid=22153)[0m E0429 02:32:13.864451 22153 raylet_client.cc:69] Retrying to connect to socket for pathname /tmp/ray/session_2020-04-28_20-19-44_770473_22870/sockets/raylet.8 (num_attempts = 1, num_retries = 5)
[2m[36m(pid=22153)[0m E0429 02:32:14.364712 22153 raylet_client.cc:69] Retrying to connect to socket for pathname /tmp/ray/session_2020-04-28_20-19-44_770473_22870/sockets/raylet.8 (num_attempts = 2, num_retries = 5)
[2m[36m(pid=22153)[0m E0429 02:32:14.864863 22153 raylet_client.cc:69] Retrying to connect to socket for pathname /tmp/ray/session_2020-04-28_20-19-44_770473_22870/sockets/raylet.8 (num_attempts = 3, num_retries = 5)
[2m[36m(pid=22153)[0m E0429 02:32:15.365000 22153 raylet_client.cc:69] Retrying to connect to socket for pathname /tmp/ray/session_2020-04-28_20-19-44_770473_22870/sockets/raylet.8 (num_attempts = 4, num_retries = 5)
[2m[36m(pid=22153)[0m F0429 02:32:15.865115 22153 raylet_client.cc:78] Could not connect to socket /tmp/ray/session_2020-04-28_20-19-44_770473_22870/sockets/raylet.8
[2m[36m(pid=22153)[0m *** Check failure stack trace: ***
[2m[36m(pid=22153)[0m @ 0x7f5d3b2b40ed google::LogMessage::Fail()
[2m[36m(pid=22153)[0m @ 0x7f5d3b2b555c google::LogMessage::SendToLog()
[2m[36m(pid=22153)[0m @ 0x7f5d3b2b3dc9 google::LogMessage::Flush()
[2m[36m(pid=22153)[0m @ 0x7f5d3b2b3fe1 google::LogMessage::~LogMessage()
[2m[36m(pid=22153)[0m @ 0x7f5d3b03bb39 ray::RayLog::~RayLog()
[2m[36m(pid=22153)[0m @ 0x7f5d3ae55133 ray::raylet::RayletConnection::RayletConnection()
[2m[36m(pid=22153)[0m @ 0x7f5d3ae55abf ray::raylet::RayletClient::RayletClient()
[2m[36m(pid=22153)[0m @ 0x7f5d3adf513b ray::CoreWorker::CoreWorker()
[2m[36m(pid=22153)[0m @ 0x7f5d3adf8984 ray::CoreWorkerProcess::CreateWorker()
[2m[36m(pid=22153)[0m @ 0x7f5d3adf8efb ray::CoreWorkerProcess::CoreWorkerProcess()
[2m[36m(pid=22153)[0m @ 0x7f5d3adf93fb ray::CoreWorkerProcess::Initialize()
[2m[36m(pid=22153)[0m @ 0x7f5d3ad6c06c __pyx_pw_3ray_7_raylet_10CoreWorker_1__cinit__()
[2m[36m(pid=22153)[0m @ 0x7f5d3ad6d155 __pyx_tp_new_3ray_7_raylet_CoreWorker()
[2m[36m(pid=22153)[0m @ 0x55db24e47965 type_call
[2m[36m(pid=22153)[0m @ 0x55db24db7d7b _PyObject_FastCallDict
[2m[36m(pid=22153)[0m @ 0x55db24e477ce call_function
[2m[36m(pid=22153)[0m @ 0x55db24e69cba _PyEval_EvalFrameDefault
[2m[36m(pid=22153)[0m @ 0x55db24e40dae _PyEval_EvalCodeWithName
[2m[36m(pid=22153)[0m @ 0x55db24e41941 fast_function
[2m[36m(pid=22153)[0m @ 0x55db24e47755 call_function
[2m[36m(pid=22153)[0m @ 0x55db24e6aa7a _PyEval_EvalFrameDefault
[2m[36m(pid=22153)[0m @ 0x55db24e42459 PyEval_EvalCodeEx
[2m[36m(pid=22153)[0m @ 0x55db24e431ec PyEval_EvalCode
[2m[36m(pid=22153)[0m @ 0x55db24ebd9a4 run_mod
[2m[36m(pid=22153)[0m @ 0x55db24ebdda1 PyRun_FileExFlags
[2m[36m(pid=22153)[0m @ 0x55db24ebdfa4 PyRun_SimpleFileExFlags
[2m[36m(pid=22153)[0m @ 0x55db24ec1a9e Py_Main
[2m[36m(pid=22153)[0m @ 0x55db24d894be main
[2m[36m(pid=22153)[0m @ 0x7f5d3cd85830 __libc_start_main
[2m[36m(pid=22153)[0m @ 0x55db24e70773 (unknown)
Traceback (most recent call last):
File "workloads/node_failures.py", line 57, in <module>
cluster.add_node()
File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ray/cluster_utils.py", line 115, in add_node
self._wait_for_node(node)
File "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ray/cluster_utils.py", line 165, in _wait_for_node
raise TimeoutError("Timed out while waiting for nodes to join.")
TimeoutError: Timed out while waiting for nodes to join.
[2m[33m(pid=raylet)[0m E0429 02:32:42.965368 13125 process.cc:274] Failed to wait for process 13136 with error system:10: No child processes
[2m[33m(pid=raylet)[0m E0429 02:32:43.045863 1167 process.cc:274] Failed to wait for process 1178 with error system:10: No child processes
2020-04-29 02:32:43,942 ERROR import_thread.py:93 -- ImportThread: Connection closed by server.
2020-04-29 02:32:43,942 ERROR worker.py:996 -- print_logs: Connection closed by server.
2020-04-29 02:32:43,942 ERROR worker.py:1096 -- listen_error_messages_raylet: Connection closed by server.
E0429 02:32:45.999132 22870 raylet_client.cc:90] IOError: [RayletClient] Connection closed unexpectedly. [RayletClient] Failed to disconnect from raylet.
```
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
This is hard to reproduce, but it happens consistently in multiple long running tests. For the logs above, it happens after 2909 iterations.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| @stephanie-wang Are you working on this issue?
I don't think this is a P0. It's just a side effect of rapidly killing and adding ray nodes on the same machine. Raylet gRPC server cannot bind to a port that's just released by killing ray node.
This is uncommon in production scenarios. Moving it to P1.
However, we do need to fix the long running test by adding some sleep buffer between killing and adding a new node, maybe inside test_reconstruction as well.
It looks like the ultimate source of the problem is that the port for GPRC is selected before GRPC actually uses that port:
https://github.com/ray-project/ray/blob/137519e19d77debb5427a24279320f21e805867f/python/ray/node.py#L167
@simon-mo What are your thoughts on this?
One possible fix is to randomize the port instead of using kernel to get unused port. This can avoid possible race condition. The reason is that current `_get_unused_port` procedure returns unused port in sequence and is likely to duplicate port numbers, since we close the socket immediately and release the ownership of that port.
```python
def _get_unused_port(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("", 0))
port = s.getsockname()[1]
# fix: add some random integer to the port number, returns if available, else retry
s.close()
return port
```
| 2020-05-27T01:18:16 |
|
ray-project/ray | 8,731 | ray-project__ray-8731 | [
"8470"
] | 64a98e44475b879630dda789b9c9fcd5a1f6931e | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -220,34 +220,35 @@ def get_best_trial(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last, avg]. If `scope=last`, only look at
- each trial's final step for `metric`, and compare across
- trials based on `mode=[min,max]`. If `scope=avg`, consider the
- simple average over all steps for `metric` and compare across
- trials based on `mode=[min,max]`. If `scope=all`, find each
- trial's min/max score for `metric` based on `mode`, and
- compare trials based on `mode=[min,max]`.
+ scope (str): One of [all, last, avg, last-5-avg, last-10-avg].
+ If `scope=last`, only look at each trial's final step for
+ `metric`, and compare across trials based on `mode=[min,max]`.
+ If `scope=avg`, consider the simple average over all steps
+ for `metric` and compare across trials based on
+ `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,
+ consider the simple average over the last 5 or 10 steps for
+ `metric` and compare across trials based on `mode=[min,max]`.
+ If `scope=all`, find each trial's min/max score for `metric`
+ based on `mode`, and compare trials based on `mode=[min,max]`.
"""
if mode not in ["max", "min"]:
raise ValueError(
"ExperimentAnalysis: attempting to get best trial for "
"metric {} for mode {} not in [\"max\", \"min\"]".format(
metric, mode))
- if scope not in ["all", "last", "avg"]:
+ if scope not in ["all", "last", "avg", "last-5-avg", "last-10-avg"]:
raise ValueError(
"ExperimentAnalysis: attempting to get best trial for "
- "metric {} for scope {} not in [\"all\", \"last\", \"avg\"]".
- format(metric, scope))
+ "metric {} for scope {} not in [\"all\", \"last\", \"avg\", "
+ "\"last-5-avg\", \"last-10-avg\"]".format(metric, scope))
best_trial = None
best_metric_score = None
for trial in self.trials:
if metric not in trial.metric_analysis:
continue
- if scope == "last":
- metric_score = trial.metric_analysis[metric]["last"]
- elif scope == "avg":
- metric_score = trial.metric_analysis[metric]["avg"]
+ if scope in ["last", "avg", "last-5-avg", "last-10-avg"]:
+ metric_score = trial.metric_analysis[metric][scope]
else:
metric_score = trial.metric_analysis[metric][mode]
@@ -273,13 +274,16 @@ def get_best_config(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last, avg]. If `scope=last`, only look at
- each trial's final step for `metric`, and compare across
- trials based on `mode=[min,max]`. If `scope=avg`, consider the
- simple average over all steps for `metric` and compare across
- trials based on `mode=[min,max]`. If `scope=all`, find each
- trial's min/max score for `metric` based on `mode`, and
- compare trials based on `mode=[min,max]`.
+ scope (str): One of [all, last, avg, last-5-avg, last-10-avg].
+ If `scope=last`, only look at each trial's final step for
+ `metric`, and compare across trials based on `mode=[min,max]`.
+ If `scope=avg`, consider the simple average over all steps
+ for `metric` and compare across trials based on
+ `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,
+ consider the simple average over the last 5 or 10 steps for
+ `metric` and compare across trials based on `mode=[min,max]`.
+ If `scope=all`, find each trial's min/max score for `metric`
+ based on `mode`, and compare trials based on `mode=[min,max]`.
"""
best_trial = self.get_best_trial(metric, mode, scope)
return best_trial.config if best_trial else None
@@ -292,13 +296,16 @@ def get_best_logdir(self, metric, mode="max", scope="all"):
Args:
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
- scope (str): One of [all, last, avg]. If `scope=last`, only look at
- each trial's final step for `metric`, and compare across
- trials based on `mode=[min,max]`. If `scope=avg`, consider the
- simple average over all steps for `metric` and compare across
- trials based on `mode=[min,max]`. If `scope=all`, find each
- trial's min/max score for `metric` based on `mode`, and
- compare trials based on `mode=[min,max]`.
+ scope (str): One of [all, last, avg, last-5-avg, last-10-avg].
+ If `scope=last`, only look at each trial's final step for
+ `metric`, and compare across trials based on `mode=[min,max]`.
+ If `scope=avg`, consider the simple average over all steps
+ for `metric` and compare across trials based on
+ `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,
+ consider the simple average over the last 5 or 10 steps for
+ `metric` and compare across trials based on `mode=[min,max]`.
+ If `scope=all`, find each trial's min/max score for `metric`
+ based on `mode`, and compare trials based on `mode=[min,max]`.
"""
best_trial = self.get_best_trial(metric, mode, scope)
return best_trial.logdir if best_trial else None
diff --git a/python/ray/tune/trial.py b/python/ray/tune/trial.py
--- a/python/ray/tune/trial.py
+++ b/python/ray/tune/trial.py
@@ -1,4 +1,5 @@
import ray.cloudpickle as cloudpickle
+from collections import deque
import copy
from datetime import datetime
import logging
@@ -214,9 +215,14 @@ def __init__(self,
self.last_result = {}
self.last_update_time = -float("inf")
- # stores in memory max/min/avg/last result for each metric by trial
+ # stores in memory max/min/avg/last-n-avg/last result for each
+ # metric by trial
self.metric_analysis = {}
+ # keep a moving average over these last n steps
+ self.n_steps = [5, 10]
+ self.metric_n_steps = {}
+
self.export_formats = export_formats
self.status = Trial.PENDING
self.start_time = None
@@ -470,6 +476,7 @@ def update_last_result(self, result, terminate=False):
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
+
for metric, value in flatten_dict(result).items():
if isinstance(value, Number):
if metric not in self.metric_analysis:
@@ -479,6 +486,13 @@ def update_last_result(self, result, terminate=False):
"avg": value,
"last": value
}
+ self.metric_n_steps[metric] = {}
+ for n in self.n_steps:
+ key = "last-{:d}-avg".format(n)
+ self.metric_analysis[metric][key] = value
+ # Store n as string for correct restore.
+ self.metric_n_steps[metric][str(n)] = deque(
+ [value], maxlen=n)
else:
step = result["training_iteration"] or 1
self.metric_analysis[metric]["max"] = max(
@@ -490,6 +504,13 @@ def update_last_result(self, result, terminate=False):
(step - 1) * self.metric_analysis[metric]["avg"])
self.metric_analysis[metric]["last"] = value
+ for n in self.n_steps:
+ key = "last-{:d}-avg".format(n)
+ self.metric_n_steps[metric][str(n)].append(value)
+ self.metric_analysis[metric][key] = sum(
+ self.metric_n_steps[metric][str(n)]) / len(
+ self.metric_n_steps[metric][str(n)])
+
def get_trainable_cls(self):
return get_trainable_cls(self.trainable_name)
| diff --git a/python/ray/tune/tests/test_experiment_analysis_mem.py b/python/ray/tune/tests/test_experiment_analysis_mem.py
--- a/python/ray/tune/tests/test_experiment_analysis_mem.py
+++ b/python/ray/tune/tests/test_experiment_analysis_mem.py
@@ -14,11 +14,11 @@ class ExperimentAnalysisInMemorySuite(unittest.TestCase):
def setUp(self):
class MockTrainable(Trainable):
scores_dict = {
- 0: [5, 4, 0],
- 1: [4, 3, 1],
- 2: [2, 1, 8],
- 3: [9, 7, 6],
- 4: [7, 5, 3]
+ 0: [5, 4, 4, 4, 4, 4, 4, 4, 0],
+ 1: [4, 3, 3, 3, 3, 3, 3, 3, 1],
+ 2: [2, 1, 1, 1, 1, 1, 1, 1, 8],
+ 3: [9, 7, 7, 7, 7, 7, 7, 7, 6],
+ 4: [7, 5, 5, 5, 5, 5, 5, 5, 3]
}
def _setup(self, config):
@@ -53,7 +53,7 @@ def testCompareTrials(self):
self.MockTrainable,
name="analysis_exp",
local_dir=self.test_dir,
- stop={"training_iteration": 3},
+ stop={"training_iteration": len(scores[0])},
num_samples=1,
config={"id": grid_search(list(range(5)))})
@@ -67,12 +67,33 @@ def testCompareTrials(self):
"avg").metric_analysis["score"]["avg"]
min_avg = ea.get_best_trial("score", "min",
"avg").metric_analysis["score"]["avg"]
+ max_avg_5 = ea.get_best_trial(
+ "score", "max",
+ "last-5-avg").metric_analysis["score"]["last-5-avg"]
+ min_avg_5 = ea.get_best_trial(
+ "score", "min",
+ "last-5-avg").metric_analysis["score"]["last-5-avg"]
+ max_avg_10 = ea.get_best_trial(
+ "score", "max",
+ "last-10-avg").metric_analysis["score"]["last-10-avg"]
+ min_avg_10 = ea.get_best_trial(
+ "score", "min",
+ "last-10-avg").metric_analysis["score"]["last-10-avg"]
self.assertEqual(max_all, max(scores_all))
self.assertEqual(min_all, min(scores_all))
self.assertEqual(max_last, max(scores_last))
+ self.assertNotEqual(max_last, max(scores_all))
+
self.assertAlmostEqual(max_avg, max(np.mean(scores, axis=1)))
self.assertAlmostEqual(min_avg, min(np.mean(scores, axis=1)))
- self.assertNotEqual(max_last, max(scores_all))
+
+ self.assertAlmostEqual(max_avg_5, max(np.mean(scores[:, -5:], axis=1)))
+ self.assertAlmostEqual(min_avg_5, min(np.mean(scores[:, -5:], axis=1)))
+
+ self.assertAlmostEqual(max_avg_10, max(
+ np.mean(scores[:, -10:], axis=1)))
+ self.assertAlmostEqual(min_avg_10, min(
+ np.mean(scores[:, -10:], axis=1)))
class AnalysisSuite(unittest.TestCase):
| [tune] Ea scope avg n step
<!-- Thank you for your contribution! Please review https://github.com/ray-project/ray/blob/master/CONTRIBUTING.rst before opening a pull request. -->
Continuing #8147, this adds `last-n-avg`, specifically `last-5-avg` and `last-10-avg` scopes to the collected experiment metrics. These _n_ are currently hardcoded since a variable number of steps would need an extension of the API for trials and the experiment runner.
<!-- Please give a short summary of the change and the problem this solves. -->
Closes #8147.
<!-- For example: "Closes #1234" -->
## Checks
- [X] I've run `scripts/format.sh` to lint the changes in this PR.
- [X] I've included any doc changes needed for https://docs.ray.io/en/latest/.
- [X] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failure rates at https://ray-travis-tracker.herokuapp.com/.
- Testing Strategy
- [X] Unit tests
- [ ] Release tests
- [ ] This PR is not tested (please justify below)
| Can one of the admins verify this patch?
Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/Ray-PRB/25977/
Test FAILed.
Looks like tune tests are failing?
There seems to be an error when restoring a run where the `self.metric_n_steps` don't get initialized: https://github.com/ray-project/ray/runs/681419121#step:3:2390 I'll fix this tomorrow.
The error was in restoring the n-step metrics. The dict keys were restored as strings, while they were originally created as integers. We should talk about whether this is expected behavior. For the meantime, we're now converting the integers to strings for the dict keys.
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/Ray-PRB/26084/
Test PASSed.
ah can we fix up the PR? looks like the merge history is broken
I rebased the PR against the current master and resolved any merge conflicts. I hope it works now. Alternatively, I could re-create the changes on a fresh branch to avoid the messy commit history.
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/Ray-PRB/26574/
Test PASSed. | 2020-06-02T11:09:37 |
ray-project/ray | 8,770 | ray-project__ray-8770 | [
"8769"
] | 9410e5884d81d45cb64dc31410c91686c4cbeb4e | diff --git a/rllib/agents/a3c/a3c.py b/rllib/agents/a3c/a3c.py
--- a/rllib/agents/a3c/a3c.py
+++ b/rllib/agents/a3c/a3c.py
@@ -54,6 +54,10 @@ def get_policy_class(config):
def validate_config(config):
if config["entropy_coeff"] < 0:
raise DeprecationWarning("entropy_coeff must be >= 0")
+ if config["sample_async"] and config["framework"] == "torch":
+ config["sample_async"] = False
+ logger.warning("`sample_async=True` is not supported for PyTorch! "
+ "Multithreading can lead to crashes.")
def execution_plan(workers, config):
diff --git a/rllib/agents/ars/ars.py b/rllib/agents/ars/ars.py
--- a/rllib/agents/ars/ars.py
+++ b/rllib/agents/ars/ars.py
@@ -11,8 +11,8 @@
from ray.rllib.agents import Trainer, with_common_config
from ray.rllib.agents.ars.ars_tf_policy import ARSTFPolicy
-from ray.rllib.agents.es import optimizers
-from ray.rllib.agents.es import utils
+from ray.rllib.agents.es import optimizers, utils
+from ray.rllib.agents.es.es import validate_config
from ray.rllib.agents.es.es_tf_policy import rollout
from ray.rllib.env.env_context import EnvContext
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
@@ -179,6 +179,7 @@ class ARSTrainer(Trainer):
@override(Trainer)
def _init(self, config, env_creator):
+ validate_config(config)
env_context = EnvContext(config["env_config"] or {}, worker_index=0)
env = env_creator(env_context)
diff --git a/rllib/agents/es/es.py b/rllib/agents/es/es.py
--- a/rllib/agents/es/es.py
+++ b/rllib/agents/es/es.py
@@ -169,6 +169,11 @@ def get_policy_class(config):
return policy_cls
+def validate_config(config):
+ if config["num_workers"] <= 0:
+ raise ValueError("`num_workers` must be > 0 for ES!")
+
+
class ESTrainer(Trainer):
"""Large-scale implementation of Evolution Strategies in Ray."""
@@ -177,6 +182,7 @@ class ESTrainer(Trainer):
@override(Trainer)
def _init(self, config, env_creator):
+ validate_config(config)
env_context = EnvContext(config["env_config"] or {}, worker_index=0)
env = env_creator(env_context)
policy_cls = get_policy_class(config)
| diff --git a/rllib/tests/test_rollout.py b/rllib/tests/test_rollout.py
--- a/rllib/tests/test_rollout.py
+++ b/rllib/tests/test_rollout.py
@@ -6,9 +6,13 @@
from ray.rllib.utils.test_utils import framework_iterator
-def rollout_test(algo, env="CartPole-v0"):
+def rollout_test(algo, env="CartPole-v0", test_episode_rollout=False):
+ extra_config = ""
+ if algo == "ES":
+ extra_config = ",\"episodes_per_batch\": 1,\"train_batch_size\": 10, "\
+ "\"noise_size\": 250000"
- for fw in framework_iterator(frameworks=("torch", "tf")):
+ for fw in framework_iterator(frameworks=("tf", "torch")):
fw_ = ", \"framework\": \"{}\"".format(fw)
tmp_dir = os.popen("mktemp -d").read()[:-1]
@@ -22,8 +26,8 @@ def rollout_test(algo, env="CartPole-v0"):
os.path.exists(rllib_dir)))
os.system("python {}/train.py --local-dir={} --run={} "
"--checkpoint-freq=1 ".format(rllib_dir, tmp_dir, algo) +
- "--config='{" +
- "\"num_workers\": 0, \"num_gpus\": 0{}".format(fw_) +
+ "--config='{" + "\"num_workers\": 1, \"num_gpus\": 0{}{}".
+ format(fw_, extra_config) +
", \"model\": {\"fcnet_hiddens\": [10]}"
"}' --stop='{\"training_iteration\": 1, "
"\"timesteps_per_iter\": 5, "
@@ -44,12 +48,13 @@ def rollout_test(algo, env="CartPole-v0"):
print("rollout output (10 steps) exists!".format(checkpoint_path))
# Test rolling out 1 episode.
- os.popen("python {}/rollout.py --run={} \"{}\" --episodes=1 "
- "--out=\"{}/rollouts_1episode.pkl\" --no-render".format(
- rllib_dir, algo, checkpoint_path, tmp_dir)).read()
- if not os.path.exists(tmp_dir + "/rollouts_1episode.pkl"):
- sys.exit(1)
- print("rollout output (1 ep) exists!".format(checkpoint_path))
+ if test_episode_rollout:
+ os.popen("python {}/rollout.py --run={} \"{}\" --episodes=1 "
+ "--out=\"{}/rollouts_1episode.pkl\" --no-render".format(
+ rllib_dir, algo, checkpoint_path, tmp_dir)).read()
+ if not os.path.exists(tmp_dir + "/rollouts_1episode.pkl"):
+ sys.exit(1)
+ print("rollout output (1 ep) exists!".format(checkpoint_path))
# Cleanup.
os.popen("rm -rf \"{}\"".format(tmp_dir)).read()
@@ -72,13 +77,10 @@ def test_es(self):
rollout_test("ES")
def test_impala(self):
- rollout_test("IMPALA", env="Pong-ram-v4")
-
- def test_pg(self):
- rollout_test("PG")
+ rollout_test("IMPALA", env="CartPole-v0")
def test_ppo(self):
- rollout_test("PPO", env="Pendulum-v0")
+ rollout_test("PPO", env="CartPole-v0", test_episode_rollout=True)
def test_sac(self):
rollout_test("SAC", env="Pendulum-v0")
diff --git a/rllib/tests/test_supported_multi_agent.py b/rllib/tests/test_supported_multi_agent.py
--- a/rllib/tests/test_supported_multi_agent.py
+++ b/rllib/tests/test_supported_multi_agent.py
@@ -14,23 +14,25 @@ def check_support_multiagent(alg, config):
register_env("multi_agent_cartpole",
lambda _: MultiAgentCartPole({"num_agents": 2}))
config["log_level"] = "ERROR"
- for _ in framework_iterator(config, frameworks=("tf", "torch")):
+ for _ in framework_iterator(config, frameworks=("torch", "tf")):
if alg in ["DDPG", "APEX_DDPG", "SAC"]:
a = get_agent_class(alg)(
config=config, env="multi_agent_mountaincar")
else:
a = get_agent_class(alg)(config=config, env="multi_agent_cartpole")
try:
- a.train()
+ print(a.train())
finally:
a.stop()
-class ModelSupportedSpaces(unittest.TestCase):
- def setUp(self):
- ray.init(num_cpus=4, ignore_reinit_error=True)
+class TestSupportedMultiAgent(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls) -> None:
+ ray.init(num_cpus=4)
- def tearDown(self):
+ @classmethod
+ def tearDownClass(cls) -> None:
ray.shutdown()
def test_a3c_multiagent(self):
@@ -45,10 +47,11 @@ def test_apex_multiagent(self):
check_support_multiagent(
"APEX", {
"num_workers": 2,
- "timesteps_per_iteration": 1000,
+ "timesteps_per_iteration": 100,
"num_gpus": 0,
+ "buffer_size": 1000,
"min_iter_time_s": 1,
- "learning_starts": 1000,
+ "learning_starts": 10,
"target_network_update_freq": 100,
})
@@ -56,10 +59,11 @@ def test_apex_ddpg_multiagent(self):
check_support_multiagent(
"APEX_DDPG", {
"num_workers": 2,
- "timesteps_per_iteration": 1000,
+ "timesteps_per_iteration": 100,
+ "buffer_size": 1000,
"num_gpus": 0,
"min_iter_time_s": 1,
- "learning_starts": 1000,
+ "learning_starts": 10,
"target_network_update_freq": 100,
"use_state_preprocessor": True,
})
@@ -68,12 +72,16 @@ def test_ddpg_multiagent(self):
check_support_multiagent(
"DDPG", {
"timesteps_per_iteration": 1,
+ "buffer_size": 1000,
"use_state_preprocessor": True,
"learning_starts": 500,
})
def test_dqn_multiagent(self):
- check_support_multiagent("DQN", {"timesteps_per_iteration": 1})
+ check_support_multiagent("DQN", {
+ "timesteps_per_iteration": 1,
+ "buffer_size": 1000,
+ })
def test_impala_multiagent(self):
check_support_multiagent("IMPALA", {"num_gpus": 0})
@@ -94,6 +102,7 @@ def test_ppo_multiagent(self):
def test_sac_multiagent(self):
check_support_multiagent("SAC", {
"num_workers": 0,
+ "buffer_size": 1000,
"normalize_actions": False,
})
diff --git a/rllib/tests/test_supported_spaces.py b/rllib/tests/test_supported_spaces.py
--- a/rllib/tests/test_supported_spaces.py
+++ b/rllib/tests/test_supported_spaces.py
@@ -48,7 +48,7 @@
}
-def check_support(alg, config, check_bounds=False, tfe=False):
+def check_support(alg, config, train=True, check_bounds=False, tfe=False):
config["log_level"] = "ERROR"
def _do_check(alg, config, a_name, o_name):
@@ -83,7 +83,8 @@ def _do_check(alg, config, a_name, o_name):
assert isinstance(a.get_policy().model, TorchFCNetV2)
else:
assert isinstance(a.get_policy().model, FCNetV2)
- a.train()
+ if train:
+ a.train()
except UnsupportedSpaceException:
stat = "unsupported"
finally:
@@ -99,19 +100,22 @@ def _do_check(alg, config, a_name, o_name):
if tfe:
frameworks += ("tfe", )
for _ in framework_iterator(config, frameworks=frameworks):
- # Check all action spaces.
+ # Check all action spaces (using a discrete obs-space).
for a_name, action_space in ACTION_SPACES_TO_TEST.items():
_do_check(alg, config, a_name, "discrete")
- # Check all obs spaces.
+ # Check all obs spaces (using a supported action-space).
for o_name, obs_space in OBSERVATION_SPACES_TO_TEST.items():
- _do_check(alg, config, "discrete", o_name)
+ a_name = "discrete" if alg not in ["DDPG", "SAC"] else "vector"
+ _do_check(alg, config, a_name, o_name)
-class ModelSupportedSpaces(unittest.TestCase):
- def setUp(self):
- ray.init(num_cpus=4, ignore_reinit_error=True, local_mode=True)
+class TestSupportedSpaces(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls) -> None:
+ ray.init(num_cpus=4)
- def tearDown(self):
+ @classmethod
+ def tearDownClass(cls) -> None:
ray.shutdown()
def test_a3c(self):
@@ -119,7 +123,7 @@ def test_a3c(self):
check_support("A3C", config, check_bounds=True)
def test_appo(self):
- check_support("APPO", {"num_gpus": 0, "vtrace": False})
+ check_support("APPO", {"num_gpus": 0, "vtrace": False}, train=False)
check_support("APPO", {"num_gpus": 0, "vtrace": True})
def test_ars(self):
@@ -138,12 +142,13 @@ def test_ddpg(self):
"ou_base_scale": 100.0
},
"timesteps_per_iteration": 1,
+ "buffer_size": 1000,
"use_state_preprocessor": True,
},
check_bounds=True)
def test_dqn(self):
- config = {"timesteps_per_iteration": 1}
+ config = {"timesteps_per_iteration": 1, "buffer_size": 1000}
check_support("DQN", config, tfe=True)
def test_es(self):
@@ -170,10 +175,10 @@ def test_ppo(self):
def test_pg(self):
config = {"num_workers": 1, "optimizer": {}}
- check_support("PG", config, check_bounds=True, tfe=True)
+ check_support("PG", config, train=False, check_bounds=True, tfe=True)
def test_sac(self):
- check_support("SAC", {}, check_bounds=True)
+ check_support("SAC", {"buffer_size": 1000}, check_bounds=True)
if __name__ == "__main__":
| [rllib] test_supported_spaces consistent OOM in travis
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
The second-to-last rllib travis build has been consistently failing since: https://github.com/ray-project/ray/pull/8520/files
was merged.
```
E ray.memory_monitor.RayOutOfMemoryError: More than 95% of the memory on node travis-job-04150411-53df-4c8c-9bc4-2c378e131a11 is used (7.4 / 7.79 GB). The top 10 memory consumers are:
E
E PID MEM COMMAND
E 30884 3.23GiB ray::RolloutWorker
E 26855 0.91GiB bazel(ray) -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/home/travis/.cache/bazel/_bazel_travis/
E 29471 0.46GiB /home/travis/build/ray-project/ray/python/ray/core/src/ray/thirdparty/redis/src/redis-server *:10263
E 29501 0.33GiB ray::ARSTrainer.train()
```
See the build history here: https://travis-ci.com/github/ray-project/ray/builds
| This is particularly odd since ARS shouldn't be creating any RolloutWorker actors at all. Maybe it's something that got leaked from another test or ARS is a red herring.
Are we sure it's not a core bug? It's not just this one test. All tests at the end of the alphabet fail due to this memory problem: test_rollout, test_rollout_worker, test_supported_multi_agent, test_supported_spaces. These are the last 4 in the test dir. They all run fine locally.
All I know is it's consistently failing after that particular PR, and never had an issue before. Maybe try reverting it and see if that fixes it?
yeah. This PR in question added many tests for pytorch that were previously only run for tf. Maybe this just made stuff go OOM (+ spillage between tests?). Will check. | 2020-06-03T20:51:29 |
ray-project/ray | 8,771 | ray-project__ray-8771 | [
"7900"
] | a24d117c689840ce1c67dadb78a7511a234a4bb2 | diff --git a/python/ray/tune/syncer.py b/python/ray/tune/syncer.py
--- a/python/ray/tune/syncer.py
+++ b/python/ray/tune/syncer.py
@@ -5,6 +5,7 @@
from shlex import quote
+from ray import ray_constants
from ray import services
from ray.tune.cluster_info import get_ssh_key, get_ssh_user
from ray.tune.sync_client import (CommandBasedClient, get_sync_client,
@@ -12,7 +13,13 @@
logger = logging.getLogger(__name__)
-SYNC_PERIOD = 300
+# Syncing period for syncing local checkpoints to cloud.
+# In env variable is not set, sync happens every 300 seconds.
+CLOUD_SYNC_PERIOD = ray_constants.env_integer(
+ key="TUNE_CLOUD_SYNC_S", default=300)
+
+# Syncing period for syncing worker logs to driver.
+NODE_SYNC_PERIOD = 300
_log_sync_warned = False
_syncers = {}
@@ -70,12 +77,23 @@ def __init__(self, local_dir, remote_dir, sync_client=NOOP):
self.last_sync_down_time = float("-inf")
self.sync_client = sync_client
- def sync_up_if_needed(self):
- if time.time() - self.last_sync_up_time > SYNC_PERIOD:
+ def sync_up_if_needed(self, sync_period):
+ """Syncs up if time since last sync up is greather than sync_period.
+
+ Arguments:
+ sync_period (int): Time period between subsequent syncs.
+ """
+
+ if time.time() - self.last_sync_up_time > sync_period:
self.sync_up()
- def sync_down_if_needed(self):
- if time.time() - self.last_sync_down_time > SYNC_PERIOD:
+ def sync_down_if_needed(self, sync_period):
+ """Syncs down if time since last sync down is greather than sync_period.
+
+ Arguments:
+ sync_period (int): Time period between subsequent syncs.
+ """
+ if time.time() - self.last_sync_down_time > sync_period:
self.sync_down()
def sync_up(self):
@@ -131,6 +149,19 @@ def _remote_path(self):
return self._remote_dir
+class CloudSyncer(Syncer):
+ """Syncer for syncing files to/from the cloud."""
+
+ def __init__(self, local_dir, remote_dir, sync_client):
+ super(CloudSyncer, self).__init__(local_dir, remote_dir, sync_client)
+
+ def sync_up_if_needed(self):
+ return super(CloudSyncer, self).sync_up_if_needed(CLOUD_SYNC_PERIOD)
+
+ def sync_down_if_needed(self):
+ return super(CloudSyncer, self).sync_down_if_needed(CLOUD_SYNC_PERIOD)
+
+
class NodeSyncer(Syncer):
"""Syncer for syncing files to/from a remote dir to a local dir."""
@@ -158,12 +189,12 @@ def has_remote_target(self):
def sync_up_if_needed(self):
if not self.has_remote_target():
return True
- return super(NodeSyncer, self).sync_up_if_needed()
+ return super(NodeSyncer, self).sync_up_if_needed(NODE_SYNC_PERIOD)
def sync_down_if_needed(self):
if not self.has_remote_target():
return True
- return super(NodeSyncer, self).sync_down_if_needed()
+ return super(NodeSyncer, self).sync_down_if_needed(NODE_SYNC_PERIOD)
def sync_up_to_new_location(self, worker_ip):
if worker_ip != self.worker_ip:
@@ -226,16 +257,16 @@ def get_cloud_syncer(local_dir, remote_dir=None, sync_function=None):
return _syncers[key]
if not remote_dir:
- _syncers[key] = Syncer(local_dir, remote_dir, NOOP)
+ _syncers[key] = CloudSyncer(local_dir, remote_dir, NOOP)
return _syncers[key]
client = get_sync_client(sync_function)
if client:
- _syncers[key] = Syncer(local_dir, remote_dir, client)
+ _syncers[key] = CloudSyncer(local_dir, remote_dir, client)
return _syncers[key]
sync_client = get_cloud_sync_client(remote_dir)
- _syncers[key] = Syncer(local_dir, remote_dir, sync_client)
+ _syncers[key] = CloudSyncer(local_dir, remote_dir, sync_client)
return _syncers[key]
diff --git a/python/ray/tune/tune.py b/python/ray/tune/tune.py
--- a/python/ray/tune/tune.py
+++ b/python/ray/tune/tune.py
@@ -144,7 +144,9 @@ def run(run_or_experiment,
from upload_dir. If string, then it must be a string template that
includes `{source}` and `{target}` for the syncer to run. If not
provided, the sync command defaults to standard S3 or gsutil sync
- commands.
+ commands. By default local_dir is synced to remote_dir every 300
+ seconds. To change this, set the TUNE_CLOUD_SYNC_S
+ environment variable in the driver machine.
sync_to_driver (func|str|bool): Function for syncing trial logdir from
remote node to local. If string, then it must be a string template
that includes `{source}` and `{target}` for the syncer to run.
| diff --git a/python/ray/tune/tests/test_cluster.py b/python/ray/tune/tests/test_cluster.py
--- a/python/ray/tune/tests/test_cluster.py
+++ b/python/ray/tune/tests/test_cluster.py
@@ -18,7 +18,7 @@
from ray.tune.ray_trial_executor import RayTrialExecutor
from ray.tune.resources import Resources
from ray.tune.suggest import BasicVariantGenerator
-from ray.tune.syncer import Syncer
+from ray.tune.syncer import CloudSyncer
from ray.tune.trainable import TrainableUtil
from ray.tune.trial import Trial
from ray.tune.trial_runner import TrialRunner
@@ -521,7 +521,7 @@ def test_cluster_down_full(start_connected_cluster, tmpdir, trainable_id):
mock_get_client = "ray.tune.trial_runner.get_cloud_syncer"
with patch(mock_get_client) as mock_get_cloud_syncer:
- mock_syncer = Syncer(local_dir, upload_dir, mock_storage_client())
+ mock_syncer = CloudSyncer(local_dir, upload_dir, mock_storage_client())
mock_get_cloud_syncer.return_value = mock_syncer
tune.run_experiments(all_experiments, raise_on_failed_trial=False)
diff --git a/python/ray/tune/tests/test_sync.py b/python/ray/tune/tests/test_sync.py
--- a/python/ray/tune/tests/test_sync.py
+++ b/python/ray/tune/tests/test_sync.py
@@ -3,6 +3,7 @@
import shutil
import sys
import tempfile
+import time
import unittest
from unittest.mock import patch
@@ -151,6 +152,38 @@ def sync_func(local, remote):
shutil.rmtree(tmpdir)
shutil.rmtree(tmpdir2)
+ @patch("ray.tune.sync_client.S3_PREFIX", "test")
+ def testCloudSyncPeriod(self):
+ """Tests that changing CLOUD_SYNC_PERIOD affects syncing frequency."""
+ tmpdir = tempfile.mkdtemp()
+
+ def trainable(config):
+ for i in range(10):
+ time.sleep(1)
+ tune.report(score=i)
+
+ mock = unittest.mock.Mock()
+
+ def counter(local, remote):
+ mock()
+
+ tune.syncer.CLOUD_SYNC_PERIOD = 1
+ [trial] = tune.run(
+ trainable,
+ name="foo",
+ max_failures=0,
+ local_dir=tmpdir,
+ upload_dir="test",
+ sync_to_cloud=counter,
+ stop={
+ "training_iteration": 10
+ },
+ global_checkpoint_period=0.5,
+ ).trials
+
+ self.assertEqual(mock.call_count, 12)
+ shutil.rmtree(tmpdir)
+
def testClusterSyncFunction(self):
def sync_func_driver(source, target):
assert ":" in source, "Source {} not a remote path.".format(source)
| [tune] parametrize sync-to-cloud period
As a background, our project interacts with tune (0.8.3) via `tune.run_experiments(<dict_from_flatfile>)`. We noticed that sometimes our trial checkpoints were not promptly syncing to our S3 account. Without hardcoding an S3 upload in our `tune.Trainable._save()` function, I uncovered 2 hardcoded parameters that sometimes prevented sync-to-cloud. For now here's our monkey-patch fix, although I propose tune developers to kindly consider native parametrization support for the future please :)
```python
def set_tune_checkpoint_period(checkpoint_period_sec):
"""
Control delay between syncs to cloud (AWS S3), from hardcoded defaults.
- ray.tune.tune.run(global_checkpoint_period): 10 seconds
- ray.tune.syncer.SYNC_PERIOD: 300 seconds
"""
import inspect # noqa: E402
import ray.tune # noqa: E402
argspecs = inspect.getfullargspec(ray.tune.tune.run)
arg_idx = argspecs.args.index("global_checkpoint_period")
mandatory_arg_len = len(argspecs.args) - len(argspecs.defaults)
new_defaults = list(argspecs.defaults)
new_defaults[arg_idx - mandatory_arg_len] = checkpoint_period_sec
ray.tune.tune.run.__defaults__ = tuple(new_defaults)
import ray.tune.syncer # noqa: E402
ray.tune.syncer.SYNC_PERIOD = checkpoint_period_sec
set_tune_checkpoint_period(1)
```
| 2020-06-03T21:31:13 |
|
ray-project/ray | 8,781 | ray-project__ray-8781 | [
"8739"
] | 41072fbcc8b3aea9fe726e960ca695f6365e9e40 | diff --git a/rllib/models/tf/tf_action_dist.py b/rllib/models/tf/tf_action_dist.py
--- a/rllib/models/tf/tf_action_dist.py
+++ b/rllib/models/tf/tf_action_dist.py
@@ -230,7 +230,7 @@ def deterministic_sample(self):
@override(ActionDistribution)
def logp(self, x):
return -0.5 * tf.reduce_sum(
- tf.square((x - self.mean) / self.std), axis=1) - \
+ tf.square((tf.to_float(x) - self.mean) / self.std), axis=1) - \
0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[1]) - \
tf.reduce_sum(self.log_std, axis=1)
diff --git a/rllib/policy/eager_tf_policy.py b/rllib/policy/eager_tf_policy.py
--- a/rllib/policy/eager_tf_policy.py
+++ b/rllib/policy/eager_tf_policy.py
@@ -5,6 +5,7 @@
import functools
import logging
import numpy as np
+from gym.spaces import Tuple, Dict
from ray.util.debug import log_once
from ray.rllib.models.catalog import ModelCatalog
@@ -586,10 +587,17 @@ def _initialize_loss_with_dummy_batch(self):
SampleBatch.NEXT_OBS: np.array(
[self.observation_space.sample()]),
SampleBatch.DONES: np.array([False], dtype=np.bool),
- SampleBatch.ACTIONS: tf.nest.map_structure(
- lambda c: np.array([c]), self.action_space.sample()),
SampleBatch.REWARDS: np.array([0], dtype=np.float32),
}
+ if isinstance(self.action_space, Tuple) or isinstance(
+ self.action_space, Dict):
+ dummy_batch[SampleBatch.ACTIONS] = [
+ flatten_to_single_ndarray(self.action_space.sample())
+ ]
+ else:
+ dummy_batch[SampleBatch.ACTIONS] = tf.nest.map_structure(
+ lambda c: np.array([c]), self.action_space.sample())
+
if obs_include_prev_action_reward:
dummy_batch.update({
SampleBatch.PREV_ACTIONS: dummy_batch[SampleBatch.ACTIONS],
| diff --git a/rllib/tests/test_supported_spaces.py b/rllib/tests/test_supported_spaces.py
--- a/rllib/tests/test_supported_spaces.py
+++ b/rllib/tests/test_supported_spaces.py
@@ -48,7 +48,7 @@
}
-def check_support(alg, config, check_bounds=False):
+def check_support(alg, config, check_bounds=False, tfe=False):
config["log_level"] = "ERROR"
def _do_check(alg, config, a_name, o_name):
@@ -95,7 +95,10 @@ def _do_check(alg, config, a_name, o_name):
pass
print(stat)
- for _ in framework_iterator(config, frameworks=("tf", "torch")):
+ frameworks = ("tf", "torch")
+ if tfe:
+ frameworks += ("tfe", )
+ for _ in framework_iterator(config, frameworks=frameworks):
# Check all action spaces.
for a_name, action_space in ACTION_SPACES_TO_TEST.items():
_do_check(alg, config, a_name, "discrete")
@@ -141,7 +144,7 @@ def test_ddpg(self):
def test_dqn(self):
config = {"timesteps_per_iteration": 1}
- check_support("DQN", config)
+ check_support("DQN", config, tfe=True)
def test_es(self):
check_support(
@@ -163,11 +166,11 @@ def test_ppo(self):
"rollout_fragment_length": 10,
"sgd_minibatch_size": 1,
}
- check_support("PPO", config, check_bounds=True)
+ check_support("PPO", config, check_bounds=True, tfe=True)
def test_pg(self):
config = {"num_workers": 1, "optimizer": {}}
- check_support("PG", config, check_bounds=True)
+ check_support("PG", config, check_bounds=True, tfe=True)
def test_sac(self):
check_support("SAC", {}, check_bounds=True)
| [rllib] Crash when using hybrid action space env and eager mode.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
RLlib crashes on environment with **hybrid action space** (discrete and continuous) and **eager mode enabled**
It works well if eager mode is turned off.
_Python3.7
tensorflow==2.2.0
ray==0.9.0.dev0 (used the nightly version)
Ubuntu 18.04_
### Script to reproduce
Modified from `ray/rllib/examples/custom_env.py` adding `Box` to the action space to make it hybrid
```
import gym
import numpy as np
import ray
from gym.spaces import Discrete, Box
from ray import tune
from ray.rllib.utils.framework import try_import_tf
tf = try_import_tf()
class SimpleCorridor(gym.Env):
"""Example of a custom env in which you have to walk down a corridor.
You can configure the length of the corridor via the env config."""
def __init__(self, config):
self.end_pos = config["corridor_length"]
self.cur_pos = 0
self.action_space = gym.spaces.Tuple([Discrete(2), Box(0.0, 1.0, shape=(1,))]) # Hybrid action space
self.observation_space = Box(
0.0, self.end_pos, shape=(1,), dtype=np.float32)
def reset(self):
self.cur_pos = 0
return [self.cur_pos]
def step(self, action):
action = action[0]
assert action in [0, 1], action
if action == 0 and self.cur_pos > 0:
self.cur_pos -= 1
elif action == 1:
self.cur_pos += 1
done = self.cur_pos >= self.end_pos
return [self.cur_pos], 1.0 if done else -0.1, done, {}
if __name__ == "__main__":
ray.init()
config = {
"env": SimpleCorridor, # or "corridor" if registered above
"env_config": {
"corridor_length": 5,
},
"model": {
},
"eager": True, # If True crashes
"vf_share_layers": True,
"lr": 1e-2,
"num_workers": 1,
"framework": "tf",
}
results = tune.run("PPO", config=config)
ray.shutdown()
```
Outputs following error
```
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
Trainer.__init__(self, config, env, logger_creator)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 444, in __init__
super().__init__(config, logger_creator)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/tune/trainable.py", line 174, in __init__
self._setup(copy.deepcopy(self.config))
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 613, in _setup
self._init(self.config, self.env_creator)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
self.config["num_workers"])
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/agents/trainer.py", line 684, in _make_workers
logdir=self.logdir)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/evaluation/worker_set.py", line 59, in __init__
RolloutWorker, env_creator, policy, 0, self._local_config)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/evaluation/worker_set.py", line 282, in _make_worker
extra_python_environs=extra_python_environs)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py", line 393, in __init__
policy_dict, policy_config)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py", line 930, in _build_policy_map
policy_map[name] = cls(obs_space, act_space, merged_conf)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/policy/eager_tf_policy.py", line 258, in __init__
self._initialize_loss_with_dummy_batch()
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/policy/eager_tf_policy.py", line 626, in _initialize_loss_with_dummy_batch
explore=False)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/policy/eager_tf_policy.py", line 60, in _func
return func(*args, **kwargs)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/policy/eager_tf_policy.py", line 68, in _func
out = func(*args, **kwargs)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/ray/rllib/policy/eager_tf_policy.py", line 335, in compute_actions
tf.convert_to_tensor(prev_action_batch)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1217, in convert_to_tensor_v1
return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1283, in convert_to_tensor_v2
as_ref=False)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1341, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py", line 1455, in _autopacking_conversion_function
return _autopacking_helper(v, dtype, name or "packed")
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py", line 1361, in _autopacking_helper
return gen_array_ops.pack(list_or_tuple, name=name)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 6337, in pack
_ops.raise_from_not_ok_status(e, name)
File "/home/coac/anaconda3/envs/rllib/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute Pack as input #1(zero-based) was expected to be a int64 tensor but is a float tensor [Op:Pack] name: packed
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| I think this is a matter of missing tf.cast() to float32 somewhere. Unfortunately, eager mode has slightly different type conversions than graph mode. Can you try finding the right place to add this conversion?
Sure, I will do a PR | 2020-06-04T13:52:01 |
ray-project/ray | 8,782 | ray-project__ray-8782 | [
"8689"
] | 41072fbcc8b3aea9fe726e960ca695f6365e9e40 | diff --git a/python/ray/dashboard/dashboard.py b/python/ray/dashboard/dashboard.py
--- a/python/ray/dashboard/dashboard.py
+++ b/python/ray/dashboard/dashboard.py
@@ -875,7 +875,7 @@ def clean_trials(self, trial_details):
# round all floats
for key in float_keys:
- details[key] = round(details[key], 3)
+ details[key] = round(details[key], 12)
# group together config attributes
for key in config_keys:
| [tune] Tune dashboard : use scientific notation
When displaying parameters in the tune dashboard, they are displayer using standard float format
with only 2 significant digits.
For example, if your learning rate is lower than 1e-2, it appears as zero.
Similarly, if your experiments return a metric whose value are in a narrow range, the displayed digits are all equal and you cannot know which one is better (for example, for an accuracy between 0 and 100% treated as a float, values displayed are between 0.00 and 1.00, with a resolution corresponding to 1%, which is really not much).
| 2020-06-04T15:23:32 |
||
ray-project/ray | 8,802 | ray-project__ray-8802 | [
"8711"
] | d78757623df968ef62e12070e29e609cee470de5 | diff --git a/python/ray/tune/progress_reporter.py b/python/ray/tune/progress_reporter.py
--- a/python/ray/tune/progress_reporter.py
+++ b/python/ray/tune/progress_reporter.py
@@ -56,6 +56,11 @@ class TuneReporterBase(ProgressReporter):
include in progress table. If this is a dict, the keys should
be metric names and the values should be the displayed names.
If this is a list, the metric name is used directly.
+ parameter_columns (dict[str, str]|list[str]): Names of parameters to
+ include in progress table. If this is a dict, the keys should
+ be parameter names and the values should be the displayed names.
+ If this is a list, the parameter name is used directly. If empty,
+ defaults to all available parameters.
max_progress_rows (int): Maximum number of rows to print
in the progress table. The progress table describes the
progress of each trial. Defaults to 20.
@@ -78,10 +83,12 @@ class TuneReporterBase(ProgressReporter):
def __init__(self,
metric_columns=None,
+ parameter_columns=None,
max_progress_rows=20,
max_error_rows=20,
max_report_frequency=5):
self._metric_columns = metric_columns or self.DEFAULT_COLUMNS
+ self._parameter_columns = parameter_columns or []
self._max_progress_rows = max_progress_rows
self._max_error_rows = max_error_rows
@@ -117,6 +124,29 @@ def add_metric_column(self, metric, representation=None):
"of metric columns.")
self._metric_columns.append(metric)
+ def add_parameter_column(self, parameter, representation=None):
+ """Adds a parameter to the existing columns.
+
+ Args:
+ parameter (str): Parameter to add. This must be a parameter
+ specified in the configuration.
+ representation (str): Representation to use in table. Defaults to
+ `parameter`.
+ """
+ if parameter in self._parameter_columns:
+ raise ValueError("Column {} already exists.".format(parameter))
+
+ if isinstance(self._parameter_columns, Mapping):
+ representation = representation or parameter
+ self._parameter_columns[parameter] = representation
+ else:
+ if representation is not None and representation != parameter:
+ raise ValueError(
+ "`representation` cannot differ from `parameter` "
+ "if this reporter was initialized with a list "
+ "of metric columns.")
+ self._parameter_columns.append(parameter)
+
def _progress_str(self, trials, done, *sys_info, fmt="psql", delim="\n"):
"""Returns full progress string.
@@ -142,6 +172,7 @@ def _progress_str(self, trials, done, *sys_info, fmt="psql", delim="\n"):
trial_progress_str(
trials,
metric_columns=self._metric_columns,
+ parameter_columns=self._parameter_columns,
fmt=fmt,
max_rows=max_progress))
messages.append(trial_errors_str(trials, fmt=fmt, max_rows=max_error))
@@ -157,6 +188,11 @@ class JupyterNotebookReporter(TuneReporterBase):
include in progress table. If this is a dict, the keys should
be metric names and the values should be the displayed names.
If this is a list, the metric name is used directly.
+ parameter_columns (dict[str, str]|list[str]): Names of parameters to
+ include in progress table. If this is a dict, the keys should
+ be parameter names and the values should be the displayed names.
+ If this is a list, the parameter name is used directly. If empty,
+ defaults to all available parameters.
max_progress_rows (int): Maximum number of rows to print
in the progress table. The progress table describes the
progress of each trial. Defaults to 20.
@@ -170,12 +206,13 @@ class JupyterNotebookReporter(TuneReporterBase):
def __init__(self,
overwrite,
metric_columns=None,
+ parameter_columns=None,
max_progress_rows=20,
max_error_rows=20,
max_report_frequency=5):
- super(JupyterNotebookReporter,
- self).__init__(metric_columns, max_progress_rows, max_error_rows,
- max_report_frequency)
+ super(JupyterNotebookReporter, self).__init__(
+ metric_columns, parameter_columns, max_progress_rows,
+ max_error_rows, max_report_frequency)
self._overwrite = overwrite
def report(self, trials, done, *sys_info):
@@ -196,6 +233,11 @@ class CLIReporter(TuneReporterBase):
include in progress table. If this is a dict, the keys should
be metric names and the values should be the displayed names.
If this is a list, the metric name is used directly.
+ parameter_columns (dict[str, str]|list[str]): Names of parameters to
+ include in progress table. If this is a dict, the keys should
+ be parameter names and the values should be the displayed names.
+ If this is a list, the parameter name is used directly. If empty,
+ defaults to all available parameters.
max_progress_rows (int): Maximum number of rows to print
in the progress table. The progress table describes the
progress of each trial. Defaults to 20.
@@ -208,12 +250,14 @@ class CLIReporter(TuneReporterBase):
def __init__(self,
metric_columns=None,
+ parameter_columns=None,
max_progress_rows=20,
max_error_rows=20,
max_report_frequency=5):
- super(CLIReporter, self).__init__(metric_columns, max_progress_rows,
- max_error_rows, max_report_frequency)
+ super(CLIReporter, self).__init__(metric_columns, parameter_columns,
+ max_progress_rows, max_error_rows,
+ max_report_frequency)
def report(self, trials, done, *sys_info):
print(self._progress_str(trials, done, *sys_info))
@@ -241,7 +285,11 @@ def memory_debug_str():
"(or ray[debug]) to resolve)")
-def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
+def trial_progress_str(trials,
+ metric_columns,
+ parameter_columns=None,
+ fmt="psql",
+ max_rows=None):
"""Returns a human readable message for printing to the console.
This contains a table where each row represents a trial, its parameters
@@ -253,6 +301,11 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
If this is a dict, the keys are metric names and the values are
the names to use in the message. If this is a list, the metric
name is used in the message directly.
+ parameter_columns (dict[str, str]|list[str]): Names of parameters to
+ include. If this is a dict, the keys are parameter names and the
+ values are the names to use in the message. If this is a list,
+ the parameter name is used in the message directly. If this is
+ empty, all parameters are used in the message.
fmt (str): Output format (see tablefmt in tabulate API).
max_rows (int): Maximum number of rows in the trial table. Defaults to
unlimited.
@@ -297,22 +350,39 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
# Pre-process trials to figure out what columns to show.
if isinstance(metric_columns, Mapping):
- keys = list(metric_columns.keys())
+ metric_keys = list(metric_columns.keys())
else:
- keys = metric_columns
- keys = [
- k for k in keys if any(
+ metric_keys = metric_columns
+ metric_keys = [
+ k for k in metric_keys if any(
t.last_result.get(k) is not None for t in trials)
]
+
+ if not parameter_columns:
+ parameter_keys = sorted(
+ set().union(*[t.evaluated_params for t in trials]))
+ elif isinstance(parameter_columns, Mapping):
+ parameter_keys = list(parameter_columns.keys())
+ else:
+ parameter_keys = parameter_columns
+
# Build trial rows.
- params = sorted(set().union(*[t.evaluated_params for t in trials]))
- trial_table = [_get_trial_info(trial, params, keys) for trial in trials]
+ trial_table = [
+ _get_trial_info(trial, parameter_keys, metric_keys) for trial in trials
+ ]
# Format column headings
if isinstance(metric_columns, Mapping):
- formatted_columns = [metric_columns[k] for k in keys]
+ formatted_metric_columns = [metric_columns[k] for k in metric_keys]
+ else:
+ formatted_metric_columns = metric_keys
+ if isinstance(parameter_columns, Mapping):
+ formatted_parameter_columns = [
+ parameter_columns[k] for k in parameter_keys
+ ]
else:
- formatted_columns = keys
- columns = (["Trial name", "status", "loc"] + params + formatted_columns)
+ formatted_parameter_columns = parameter_keys
+ columns = (["Trial name", "status", "loc"] + formatted_parameter_columns +
+ formatted_metric_columns)
# Tabulate.
messages.append(
tabulate(trial_table, headers=columns, tablefmt=fmt, showindex=False))
| diff --git a/python/ray/tune/tests/test_progress_reporter.py b/python/ray/tune/tests/test_progress_reporter.py
--- a/python/ray/tune/tests/test_progress_reporter.py
+++ b/python/ray/tune/tests/test_progress_reporter.py
@@ -12,13 +12,13 @@
EXPECTED_RESULT_1 = """Result logdir: /foo
Number of trials: 5 (1 PENDING, 3 RUNNING, 1 TERMINATED)
-+--------------+------------+-------+-----+-----+
-| Trial name | status | loc | a | b |
-|--------------+------------+-------+-----+-----|
-| 00001 | PENDING | here | 1 | 2 |
-| 00002 | RUNNING | here | 2 | 4 |
-| 00000 | TERMINATED | here | 0 | 0 |
-+--------------+------------+-------+-----+-----+
++--------------+------------+-------+-----+-----+------------+
+| Trial name | status | loc | a | b | metric_1 |
+|--------------+------------+-------+-----+-----+------------|
+| 00001 | PENDING | here | 1 | 2 | 0.5 |
+| 00002 | RUNNING | here | 2 | 4 | 1 |
+| 00000 | TERMINATED | here | 0 | 0 | 0 |
++--------------+------------+-------+-----+-----+------------+
... 2 more trials not shown (2 RUNNING)"""
EXPECTED_RESULT_2 = """Result logdir: /foo
@@ -33,6 +33,17 @@
| 00004 | RUNNING | here | 4 | 8 |
+--------------+------------+-------+-----+-----+"""
+EXPECTED_RESULT_3 = """Result logdir: /foo
+Number of trials: 5 (1 PENDING, 3 RUNNING, 1 TERMINATED)
++--------------+------------+-------+-----+------------+------------+
+| Trial name | status | loc | A | Metric 1 | Metric 2 |
+|--------------+------------+-------+-----+------------+------------|
+| 00001 | PENDING | here | 1 | 0.5 | 0.25 |
+| 00002 | RUNNING | here | 2 | 1 | 0.5 |
+| 00000 | TERMINATED | here | 0 | 0 | 0 |
++--------------+------------+-------+-----+------------+------------+
+... 2 more trials not shown (2 RUNNING)"""
+
END_TO_END_COMMAND = """
import ray
from ray import tune
@@ -125,6 +136,42 @@ def f(config):
| f_xxxxx_00029 | TERMINATED | | | | 9 |
+---------------+------------+-------+-----+-----+-----+"""
+EXPECTED_END_TO_END_AC = """Number of trials: 30 (30 TERMINATED)
++---------------+------------+-------+-----+-----+-----+
+| Trial name | status | loc | a | b | c |
+|---------------+------------+-------+-----+-----+-----|
+| f_xxxxx_00000 | TERMINATED | | 0 | | |
+| f_xxxxx_00001 | TERMINATED | | 1 | | |
+| f_xxxxx_00002 | TERMINATED | | 2 | | |
+| f_xxxxx_00003 | TERMINATED | | 3 | | |
+| f_xxxxx_00004 | TERMINATED | | 4 | | |
+| f_xxxxx_00005 | TERMINATED | | 5 | | |
+| f_xxxxx_00006 | TERMINATED | | 6 | | |
+| f_xxxxx_00007 | TERMINATED | | 7 | | |
+| f_xxxxx_00008 | TERMINATED | | 8 | | |
+| f_xxxxx_00009 | TERMINATED | | 9 | | |
+| f_xxxxx_00010 | TERMINATED | | | 0 | |
+| f_xxxxx_00011 | TERMINATED | | | 1 | |
+| f_xxxxx_00012 | TERMINATED | | | 2 | |
+| f_xxxxx_00013 | TERMINATED | | | 3 | |
+| f_xxxxx_00014 | TERMINATED | | | 4 | |
+| f_xxxxx_00015 | TERMINATED | | | 5 | |
+| f_xxxxx_00016 | TERMINATED | | | 6 | |
+| f_xxxxx_00017 | TERMINATED | | | 7 | |
+| f_xxxxx_00018 | TERMINATED | | | 8 | |
+| f_xxxxx_00019 | TERMINATED | | | 9 | |
+| f_xxxxx_00020 | TERMINATED | | | | 0 |
+| f_xxxxx_00021 | TERMINATED | | | | 1 |
+| f_xxxxx_00022 | TERMINATED | | | | 2 |
+| f_xxxxx_00023 | TERMINATED | | | | 3 |
+| f_xxxxx_00024 | TERMINATED | | | | 4 |
+| f_xxxxx_00025 | TERMINATED | | | | 5 |
+| f_xxxxx_00026 | TERMINATED | | | | 6 |
+| f_xxxxx_00027 | TERMINATED | | | | 7 |
+| f_xxxxx_00028 | TERMINATED | | | | 8 |
+| f_xxxxx_00029 | TERMINATED | | | | 9 |
++---------------+------------+-------+-----+-----+-----+"""
+
class ProgressReporterTest(unittest.TestCase):
def mock_trial(self, status, i):
@@ -202,17 +249,38 @@ def testProgressStr(self):
t.location = "here"
t.config = {"a": i, "b": i * 2}
t.evaluated_params = t.config
- t.last_result = {"config": {"a": i, "b": i * 2}}
+ t.last_result = {
+ "config": {
+ "a": i,
+ "b": i * 2
+ },
+ "metric_1": i / 2,
+ "metric_2": i / 4
+ }
t.__str__ = lambda self: self.trial_id
trials.append(t)
- prog1 = trial_progress_str(trials, ["a", "b"], fmt="psql", max_rows=3)
+ # One metric, all parameters
+ prog1 = trial_progress_str(
+ trials, ["metric_1"], None, fmt="psql", max_rows=3)
print(prog1)
assert prog1 == EXPECTED_RESULT_1
- prog2 = trial_progress_str(
- trials, ["a", "b"], fmt="psql", max_rows=None)
+
+ # No metric, all parameters
+ prog2 = trial_progress_str(trials, [], None, fmt="psql", max_rows=None)
print(prog2)
assert prog2 == EXPECTED_RESULT_2
+ # Both metrics, one parameter, all with custom representation
+ prog3 = trial_progress_str(
+ trials, {
+ "metric_1": "Metric 1",
+ "metric_2": "Metric 2"
+ }, {"a": "A"},
+ fmt="psql",
+ max_rows=3)
+ print(prog3)
+ assert prog3 == EXPECTED_RESULT_3
+
def testEndToEndReporting(self):
try:
os.environ["_TEST_TUNE_TRIAL_UUID"] = "xxxxx"
| [tune] Configure parameters in output table
We want to be able to configure the hyperparameters printed during the experiment execution.
cc @krfricke
| 2020-06-05T13:33:47 |
|
ray-project/ray | 8,842 | ray-project__ray-8842 | [
"8841"
] | 3388864768fa198f27496824407f9aee16256c2f | diff --git a/python/ray/async_compat.py b/python/ray/async_compat.py
--- a/python/ray/async_compat.py
+++ b/python/ray/async_compat.py
@@ -75,6 +75,11 @@ def done_callback(future):
# Result from direct call.
assert isinstance(result, AsyncGetResponse), result
if result.plasma_fallback_id is None:
+ # If this future has result set already, we just need to
+ # skip the set result/exception procedure.
+ if user_future.done():
+ return
+
if isinstance(result.result, ray.exceptions.RayTaskError):
ray.worker.last_task_error_raise_time = time.time()
user_future.set_exception(
| diff --git a/python/ray/tests/test_asyncio.py b/python/ray/tests/test_asyncio.py
--- a/python/ray/tests/test_asyncio.py
+++ b/python/ray/tests/test_asyncio.py
@@ -5,6 +5,7 @@
import sys
import ray
+from ray.test_utils import SignalActor
def test_asyncio_actor(ray_start_regular_shared):
@@ -177,6 +178,26 @@ async def plasma_get(self, plasma_object):
assert ray.get(getter.plasma_get.remote([plasma_object])) == 2
[email protected]
+async def test_asyncio_double_await(ray_start_regular_shared):
+ # This is a regression test for
+ # https://github.com/ray-project/ray/issues/8841
+
+ signal = SignalActor.remote()
+ waiting = signal.wait.remote()
+
+ future = waiting.as_future()
+ with pytest.raises(asyncio.TimeoutError):
+ await asyncio.wait_for(future, timeout=0.1)
+ assert future.cancelled()
+
+ # We are explicitly waiting multiple times here to test asyncio state
+ # override.
+ await signal.send.remote()
+ await waiting
+ await waiting
+
+
if __name__ == "__main__":
import pytest
sys.exit(pytest.main(["-v", __file__]))
| [Asyncio] InvalidStateError when multiple awaits on same oid
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
```python
import ray
import time
ray.init()
@ray.remote
def f():
time.sleep(5)
oid = f.remote()
await asyncio.wait_for(oid, timeout=1)
await asyncio.wait_for(oid, timeout=1)
```
Output
```
Exception in callback get_async.<locals>.done_callback(<Future finis... result=None)>) at /Users/simonmo/Desktop/ray/ray/python/ray/async_compat.py:65
handle: <Handle get_async.<locals>.done_callback(<Future finis... result=None)>) at /Users/simonmo/Desktop/ray/ray/python/ray/async_compat.py:65>
Traceback (most recent call last):
File "/Users/simonmo/miniconda3/lib/python3.6/asyncio/events.py", line 145, in _run
self._callback(*self._args)
File "/Users/simonmo/Desktop/ray/ray/python/ray/async_compat.py", line 83, in done_callback
user_future.set_result(result.result)
asyncio.base_futures.InvalidStateError: invalid state
```
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| Oh nice. This must be related to @allenyin55 's issue right? | 2020-06-08T20:06:07 |
ray-project/ray | 8,886 | ray-project__ray-8886 | [
"8878"
] | cf53b351471716e7bfa71d36368ebea9b0e219c5 | diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py
--- a/python/ray/scripts/scripts.py
+++ b/python/ray/scripts/scripts.py
@@ -60,6 +60,7 @@ def check_no_existing_redis_clients(node_ip_address, redis_client):
default=ray_constants.LOGGER_FORMAT,
type=str,
help=ray_constants.LOGGER_FORMAT_HELP)
[email protected]_option()
def cli(logging_level, logging_format):
level = logging.getLevelName(logging_level.upper())
ray.utils.setup_logger(level, logging_format)
| [CLI] version option
### Describe your feature request
`ray --version` should output the version. Judging from the output of `ray --help`, it looks like Click is used. Version flags should be easy in click; see https://click.palletsprojects.com/en/7.x/api/#click.version_option.
| This wouldn't be hard to implement; @sumanthratna would you be willing to push a quick PR?
just tag me and I'll help you get it merged!
Sure! Is [python/ray/scripts/scripts.py](https://github.com/ray-project/ray/edit/master/python/ray/scripts/scripts.py) the only file I need to change? | 2020-06-10T20:50:05 |
|
ray-project/ray | 8,898 | ray-project__ray-8898 | [
"8889",
"8889"
] | 9166e2208574f61eea3454b28ca0f2e0a45186e8 | diff --git a/rllib/evaluation/sampler.py b/rllib/evaluation/sampler.py
--- a/rllib/evaluation/sampler.py
+++ b/rllib/evaluation/sampler.py
@@ -888,15 +888,18 @@ def _process_policy_eval_results(*, to_eval, eval_results, active_episodes,
pi_info_cols["state_out_{}".format(f_i)] = column
policy = _get_or_raise(policies, policy_id)
- # Clip if necessary (while action components are still batched).
- if clip_actions:
- actions = clip_action(actions, policy.action_space_struct)
# Split action-component batches into single action rows.
actions = unbatch(actions)
for i, action in enumerate(actions):
env_id = eval_data[i].env_id
agent_id = eval_data[i].agent_id
- actions_to_send[env_id][agent_id] = action
+ # Clip if necessary.
+ if clip_actions:
+ clipped_action = clip_action(action,
+ policy.action_space_struct)
+ else:
+ clipped_action = action
+ actions_to_send[env_id][agent_id] = clipped_action
episode = active_episodes[env_id]
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
episode._set_last_pi_info(
diff --git a/rllib/tuned_examples/debug_learning_failure_git_bisect.py b/rllib/tuned_examples/debug_learning_failure_git_bisect.py
new file mode 100644
--- /dev/null
+++ b/rllib/tuned_examples/debug_learning_failure_git_bisect.py
@@ -0,0 +1,88 @@
+"""Example of testing, whether RLlib can still learn with a certain config.
+
+Can be used with git bisect to find the faulty commit responsible for a
+learning failure. Produces an error if the given reward is not reached within
+the stopping criteria (training iters or timesteps) allowing git bisect to
+properly analyze and find the faulty commit.
+
+Run as follows using a simple command line config:
+$ python debug_learning_failure_git_bisect.py --config '{...}'
+ --env CartPole-v0 --run PPO --stop-reward=180 --stop-iters=100
+
+With a yaml file:
+$ python debug_learning_failure_git_bisect.py -f [yaml file] --stop-reward=180
+ --stop-iters=100
+
+Within git bisect:
+$ git bisect start
+$ git bisect bad
+$ git bisect good [some previous commit we know was good]
+$ git bisect run python debug_learning_failure_git_bisect.py [... options]
+"""
+import argparse
+import json
+import yaml
+
+import ray
+from ray import tune
+from ray.rllib.utils import try_import_tf
+from ray.rllib.utils.test_utils import check_learning_achieved
+
+tf = try_import_tf()
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--run", type=str, default=None)
+parser.add_argument("--torch", action="store_true")
+parser.add_argument("--stop-iters", type=int, default=None)
+parser.add_argument("--stop-timesteps", type=int, default=None)
+parser.add_argument("--stop-reward", type=float, default=None)
+parser.add_argument("-f", type=str, default=None)
+parser.add_argument("--config", type=str, default=None)
+parser.add_argument("--env", type=str, default=None)
+
+if __name__ == "__main__":
+ run = None
+
+ args = parser.parse_args()
+
+ # Explicit yaml config file.
+ if args.f:
+ with open(args.f, "r") as fp:
+ experiment_config = yaml.load(fp)
+ experiment_config = experiment_config[next(
+ iter(experiment_config))]
+ config = experiment_config.get("config", {})
+ config["env"] = experiment_config.get("env")
+ run = experiment_config.pop("run")
+ # JSON string on command line.
+ else:
+ config = json.loads(args.config)
+ assert args.env
+ config["env"] = args.env
+
+ # Explicit run.
+ if args.run:
+ run = args.run
+
+ # Explicit --torch framework.
+ if args.torch:
+ config["framework"] = "torch"
+ # Framework not specified in config, try to infer it.
+ if "framework" not in config:
+ config["framework"] = "torch" if args.torch else "tf"
+
+ ray.init()
+
+ stop = {}
+ if args.stop_iters:
+ stop["training_iteration"] = args.stop_iters
+ if args.stop_timesteps:
+ stop["timesteps_total"] = args.stop_timesteps
+ if args.stop_reward:
+ stop["episode_reward_mean"] = args.stop_reward
+
+ results = tune.run(run, stop=stop, config=config)
+
+ check_learning_achieved(results, args.stop_reward)
+
+ ray.shutdown()
| [RLlib] PPO not learning in complex cont. action environments.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
PPO is currently not learning e.g. HalfCheetah-v2 using the `tuned_examples/ppo/halfcheetah-ppo.yaml` config (on neither tf nor torch).
ray-0.9.0dev
tf2.2.0
torch1.5.0
The problem does not occur on ray/rllib 0.8.5.
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
[RLlib] PPO not learning in complex cont. action environments.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
PPO is currently not learning e.g. HalfCheetah-v2 using the `tuned_examples/ppo/halfcheetah-ppo.yaml` config (on neither tf nor torch).
ray-0.9.0dev
tf2.2.0
torch1.5.0
The problem does not occur on ray/rllib 0.8.5.
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| <img width="849" alt="Screen Shot 2020-06-10 at 2 27 42 PM" src="https://user-images.githubusercontent.com/20640975/84320506-a1ce9a00-ab26-11ea-9dd1-1ed216d14682.png">
<img width="880" alt="Screen Shot 2020-06-10 at 2 27 27 PM" src="https://user-images.githubusercontent.com/20640975/84320512-a430f400-ab26-11ea-997b-cd9ae4d82422.png">
In addition, `pong-ppo.yaml` and `pong-impala-vectorized.yaml` are not learning either.
<img width="849" alt="Screen Shot 2020-06-10 at 2 27 42 PM" src="https://user-images.githubusercontent.com/20640975/84320506-a1ce9a00-ab26-11ea-9dd1-1ed216d14682.png">
<img width="880" alt="Screen Shot 2020-06-10 at 2 27 27 PM" src="https://user-images.githubusercontent.com/20640975/84320512-a430f400-ab26-11ea-997b-cd9ae4d82422.png">
In addition, `pong-ppo.yaml` and `pong-impala-vectorized.yaml` are not learning either. | 2020-06-11T09:09:31 |
|
ray-project/ray | 8,933 | ray-project__ray-8933 | [
"8919",
"8919"
] | 19cc1ae781483ff509176a3f3ad3ff09a7084f8b | diff --git a/rllib/train.py b/rllib/train.py
--- a/rllib/train.py
+++ b/rllib/train.py
@@ -149,6 +149,7 @@ def run(args, parser):
args.experiment_name: { # i.e. log to ~/ray_results/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
+ "checkpoint_at_end": args.checkpoint_at_end,
"keep_checkpoints_num": args.keep_checkpoints_num,
"checkpoint_score_attr": args.checkpoint_score_attr,
"local_dir": args.local_dir,
| diff --git a/rllib/tests/test_checkpoint_restore.py b/rllib/tests/test_checkpoint_restore.py
--- a/rllib/tests/test_checkpoint_restore.py
+++ b/rllib/tests/test_checkpoint_restore.py
@@ -124,6 +124,9 @@ def ckpt_restore_test(alg_name, tfe=False):
if abs(a1 - a2) > .1:
raise AssertionError("algo={} [a1={} a2={}]".format(
alg_name, a1, a2))
+ # Stop both Trainers.
+ alg1.stop()
+ alg2.stop()
class TestCheckpointRestore(unittest.TestCase):
| [rllib] rllib train ... --checkpoint-at-end flag is ignored
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Ray 0.8.5
Python 3.7
MacOS Catalina
PyTorch 1.4.0
No checkpoint is produced when the `--checkpoint-at-end` flag is used. Adding `--checkpoint-freq 10` does cause checkpoints to be saved.
### Reproduction (REQUIRED)
```shell
rllib train --run PPO --env CartPole-v0 --stop='{"training_iteration": 25}' --ray-address auto --checkpoint-at-end
```
No checkpoint directory exists under `~/ray_results/default/PPO...`
Add the `--checkpoint-freq 10` flag:
```shell
rllib train --run PPO --env CartPole-v0 --stop='{"training_iteration": 25}' --ray-address auto --checkpoint-at-end --checkpoint-freq 10
```
Now there are `checkpoint_10` and `checkpoint_20` directories, but not a `checkpoint_25` at the end.
Could the choice of stop criteria, `training_iteration`, have something to do with it?
> **Note:** Being persnickety, it bugs me that the final directory path for the checkpoints is `.../checkpoint_20/checkpoint-20` (underscore vs. dash). How about one or the other?
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
[rllib] rllib train ... --checkpoint-at-end flag is ignored
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Ray 0.8.5
Python 3.7
MacOS Catalina
PyTorch 1.4.0
No checkpoint is produced when the `--checkpoint-at-end` flag is used. Adding `--checkpoint-freq 10` does cause checkpoints to be saved.
### Reproduction (REQUIRED)
```shell
rllib train --run PPO --env CartPole-v0 --stop='{"training_iteration": 25}' --ray-address auto --checkpoint-at-end
```
No checkpoint directory exists under `~/ray_results/default/PPO...`
Add the `--checkpoint-freq 10` flag:
```shell
rllib train --run PPO --env CartPole-v0 --stop='{"training_iteration": 25}' --ray-address auto --checkpoint-at-end --checkpoint-freq 10
```
Now there are `checkpoint_10` and `checkpoint_20` directories, but not a `checkpoint_25` at the end.
Could the choice of stop criteria, `training_iteration`, have something to do with it?
> **Note:** Being persnickety, it bugs me that the final directory path for the checkpoints is `.../checkpoint_20/checkpoint-20` (underscore vs. dash). How about one or the other?
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-06-14T13:52:06 |
|
ray-project/ray | 8,953 | ray-project__ray-8953 | [
"4029"
] | 4bc1d7c043cca03a743fbeb98b0434fecafa7001 | diff --git a/python/ray/log_monitor.py b/python/ray/log_monitor.py
--- a/python/ray/log_monitor.py
+++ b/python/ray/log_monitor.py
@@ -5,6 +5,7 @@
import logging
import os
import platform
+import re
import shutil
import time
import traceback
@@ -18,19 +19,24 @@
# entry/init points.
logger = logging.getLogger(__name__)
+# First group is worker id. Second group is job id.
+JOB_LOG_PATTERN = re.compile(".*worker-([0-9a-f]{40})-(\d+)")
+
class LogFileInfo:
def __init__(self,
filename=None,
size_when_last_opened=None,
file_position=None,
- file_handle=None):
+ file_handle=None,
+ job_id=None):
assert (filename is not None and size_when_last_opened is not None
and file_position is not None)
self.filename = filename
self.size_when_last_opened = size_when_last_opened
self.file_position = file_position
self.file_handle = file_handle
+ self.job_id = job_id
self.worker_pid = None
@@ -116,13 +122,20 @@ def update_log_filenames(self):
for file_path in log_file_paths + raylet_err_paths:
if os.path.isfile(
file_path) and file_path not in self.log_filenames:
+ job_match = JOB_LOG_PATTERN.match(file_path)
+ if job_match:
+ job_id = job_match.group(2)
+ else:
+ job_id = None
+
self.log_filenames.add(file_path)
self.closed_file_infos.append(
LogFileInfo(
filename=file_path,
size_when_last_opened=0,
file_position=0,
- file_handle=None))
+ file_handle=None,
+ job_id=job_id))
log_filename = os.path.basename(file_path)
logger.info("Beginning to track file {}".format(log_filename))
@@ -231,6 +244,7 @@ def check_log_files_and_publish_updates(self):
json.dumps({
"ip": self.ip,
"pid": file_info.worker_pid,
+ "job": file_info.job_id,
"lines": lines_to_publish
}))
anything_published = True
diff --git a/python/ray/utils.py b/python/ray/utils.py
--- a/python/ray/utils.py
+++ b/python/ray/utils.py
@@ -403,6 +403,7 @@ def create_and_init_new_worker_log(path, worker_pid):
Args:
path (str): The name/path of the file to be opened.
+ worker_pid (int): The pid of the worker process.
Returns:
A file-like object which can be written to.
diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -953,13 +953,15 @@ def stderr_setter(x):
return stdout_path, stderr_path
-def print_logs(redis_client, threads_stopped):
+def print_logs(redis_client, threads_stopped, job_id):
"""Prints log messages from workers on all of the nodes.
Args:
redis_client: A client to the primary Redis shard.
threads_stopped (threading.Event): A threading event used to signal to
the thread that it should exit.
+ job_id (JobID): The id of the driver's job
+
"""
pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)
pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)
@@ -981,9 +983,20 @@ def print_logs(redis_client, threads_stopped):
threads_stopped.wait(timeout=0.01)
continue
num_consecutive_messages_received += 1
+ if (num_consecutive_messages_received % 100 == 0
+ and num_consecutive_messages_received > 0):
+ logger.warning(
+ "The driver may not be able to keep up with the "
+ "stdout/stderr of the workers. To avoid forwarding logs "
+ "to the driver, use 'ray.init(log_to_driver=False)'.")
data = json.loads(ray.utils.decode(msg["data"]))
+ # Don't show logs from other drivers.
+ if data["job"] and ray.utils.binary_to_hex(
+ job_id.binary()) != data["job"]:
+ continue
+
def color_for(data):
if data["pid"] == "raylet":
return colorama.Fore.YELLOW
@@ -1001,12 +1014,6 @@ def color_for(data):
colorama.Style.DIM, color_for(data), data["pid"],
data["ip"], colorama.Style.RESET_ALL, line))
- if (num_consecutive_messages_received % 100 == 0
- and num_consecutive_messages_received > 0):
- logger.warning(
- "The driver may not be able to keep up with the "
- "stdout/stderr of the workers. To avoid forwarding logs "
- "to the driver, use 'ray.init(log_to_driver=False)'.")
except (OSError, redis.exceptions.ConnectionError) as e:
logger.error("print_logs: {}".format(e))
finally:
@@ -1310,7 +1317,7 @@ def connect(node,
worker.logger_thread = threading.Thread(
target=print_logs,
name="ray_print_logs",
- args=(worker.redis_client, worker.threads_stopped))
+ args=(worker.redis_client, worker.threads_stopped, job_id))
worker.logger_thread.daemon = True
worker.logger_thread.start()
| diff --git a/python/ray/test_utils.py b/python/ray/test_utils.py
--- a/python/ray/test_utils.py
+++ b/python/ray/test_utils.py
@@ -130,7 +130,9 @@ def run_string_as_driver_nonblocking(driver_script):
f.write(driver_script.encode("ascii"))
f.flush()
return subprocess.Popen(
- [sys.executable, f.name], stdout=subprocess.PIPE)
+ [sys.executable, f.name],
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE)
def flat_errors():
diff --git a/python/ray/tests/test_multi_node.py b/python/ray/tests/test_multi_node.py
--- a/python/ray/tests/test_multi_node.py
+++ b/python/ray/tests/test_multi_node.py
@@ -5,13 +5,9 @@
import ray
from ray.test_utils import (
- RayTestTimeoutException,
- run_string_as_driver,
- run_string_as_driver_nonblocking,
- wait_for_children_of_pid,
- wait_for_children_of_pid_to_exit,
- kill_process_by_name,
-)
+ RayTestTimeoutException, run_string_as_driver,
+ run_string_as_driver_nonblocking, wait_for_children_of_pid,
+ wait_for_children_of_pid_to_exit, kill_process_by_name, Semaphore)
def test_error_isolation(call_ray_start):
@@ -634,6 +630,77 @@ def f():
ray.get(f.remote())
+def test_multi_driver_logging(ray_start_regular):
+ address_info = ray_start_regular
+ address = address_info["redis_address"]
+
+ # ray.init(address=address)
+ driver1_wait = Semaphore.options(name="driver1_wait").remote(value=0)
+ driver2_wait = Semaphore.options(name="driver2_wait").remote(value=0)
+ main_wait = Semaphore.options(name="main_wait").remote(value=0)
+
+ # Params are address, semaphore name, output1, output2
+ driver_script_template = """
+import ray
+import sys
+from ray.test_utils import Semaphore
+
[email protected](num_cpus=0)
+def remote_print(s, file=None):
+ print(s, file=file)
+
+ray.init(address="{}")
+
+driver_wait = ray.get_actor("{}")
+main_wait = ray.get_actor("main_wait")
+
+ray.get(main_wait.release.remote())
+ray.get(driver_wait.acquire.remote())
+
+s1 = "{}"
+ray.get(remote_print.remote(s1))
+
+ray.get(main_wait.release.remote())
+ray.get(driver_wait.acquire.remote())
+
+s2 = "{}"
+ray.get(remote_print.remote(s2))
+
+ray.get(main_wait.release.remote())
+ """
+
+ p1 = run_string_as_driver_nonblocking(
+ driver_script_template.format(address, "driver1_wait", "1", "2"))
+ p2 = run_string_as_driver_nonblocking(
+ driver_script_template.format(address, "driver2_wait", "3", "4"))
+
+ ray.get(main_wait.acquire.remote())
+ ray.get(main_wait.acquire.remote())
+ # At this point both of the other drivers are fully initialized.
+
+ ray.get(driver1_wait.release.remote())
+ ray.get(driver2_wait.release.remote())
+
+ # At this point driver1 should receive '1' and driver2 '3'
+ ray.get(main_wait.acquire.remote())
+ ray.get(main_wait.acquire.remote())
+
+ ray.get(driver1_wait.release.remote())
+ ray.get(driver2_wait.release.remote())
+
+ # At this point driver1 should receive '2' and driver2 '4'
+ ray.get(main_wait.acquire.remote())
+ ray.get(main_wait.acquire.remote())
+
+ driver1_out = p1.stdout.read().decode("ascii").split("\n")
+ driver2_out = p2.stdout.read().decode("ascii").split("\n")
+
+ assert driver1_out[0][-1] == "1"
+ assert driver1_out[1][-1] == "2"
+ assert driver2_out[0][-1] == "3"
+ assert driver2_out[1][-1] == "4"
+
+
if __name__ == "__main__":
import pytest
import sys
| Stdout/stderr from all drivers are streamed to all other drivers.
As of https://github.com/ray-project/ray/pull/3892, the stdout/stderr of all drivers is streamed to all other drivers. An individual driver can opt out by calling `ray.init(..., log_to_driver=False)`.
To reproduce this, do the following.
1. Start Ray.
```
ray start --head --redis-port=6379
```
2. Start a driver that prints stuff.
```python
import ray
import sys
import time
ray.init(redis_address='localhost:6379')
@ray.remote
def f():
while True:
print('hi')
sys.stdout.flush()
time.sleep(0.1)
f.remote()
```
3. Start another driver, and see that it receives the stdout from the first driver.
```python
import ray
ray.init(redis_address='localhost:6379')
```
### Some options to address this.
1. Workers can belong to specific drivers (this means logs will be complicated for cross driver applications, e.g., named actors).
2. Workers can redirect there stdout/stderr on a per-task basis.
3. Workers can print extra annotations in their stdout/stderr which the log monitor can parse and use to choose which drivers to publish to.
| I like option 3.
For option 2 what kind of api were you thinking about? Like an additional keyword in ray.remote decoration?
I am already making use of this feature a little and find it handy to include timestamp and function name to each stdout. Not saying these should be added by default but giving that as an example.
Option 2 would not require the user to do anything. Instead, there would one log file per worker/driver combination, and whenever a worker executes a task for a given driver, it logs to the file for that worker/driver combination. This would all be done under the hood.
2 or 3 sound fine to me then. I am not doing cross driver work yet so that isn't an issue for me.
If this isn't going to be fixed soon, can we at least document it somewhere prominently? This was very confusing to me for a while: now I know what's causing it, it's largely a non-issue, as I can look at output from my other logging methods.
Thanks @AdamGleave for bringing this up. I'll take a look at fixing it this week. | 2020-06-15T23:03:05 |
ray-project/ray | 8,964 | ray-project__ray-8964 | [
"8713"
] | 508149b3c3004ee5ef8b02bd797e26ceaac896b2 | diff --git a/python/ray/util/iter.py b/python/ray/util/iter.py
--- a/python/ray/util/iter.py
+++ b/python/ray/util/iter.py
@@ -203,9 +203,9 @@ def for_each(self, fn: Callable[[T], U], max_concurrency=1,
`max_concurrency` should be used to achieve a high degree of
parallelism without the overhead of increasing the number of shards
- (which are actor based). This provides the semantic guarantee that
- `fn(x_i)` will _begin_ executing before `fn(x_{i+1})` (but not
- necessarily finish first)
+ (which are actor based). If `max_concurrency` is not 1, this function
+ provides no semantic guarantees on the output order.
+ Results will be returned as soon as they are ready.
A performance note: When executing concurrently, this function
maintains its own internal buffer. If `num_async` is `n` and
@@ -234,6 +234,7 @@ def for_each(self, fn: Callable[[T], U], max_concurrency=1,
... [0, 2, 4, 8]
"""
+ assert max_concurrency >= 0, "max_concurrency must be non-negative."
return self._with_transform(
lambda local_it: local_it.for_each(fn, max_concurrency, resources),
".for_each()")
@@ -765,23 +766,13 @@ def apply_foreach(it):
if isinstance(item, _NextValueNotReady):
yield item
else:
- finished, remaining = ray.wait(cur, timeout=0)
- if max_concurrency and len(
- remaining) >= max_concurrency:
- ray.wait(cur, num_returns=(len(finished) + 1))
+ if max_concurrency and len(cur) >= max_concurrency:
+ finished, cur = ray.wait(cur)
+ yield from ray.get(finished)
cur.append(remote_fn(item))
-
- while len(cur) > 0:
- to_yield = cur[0]
- finished, remaining = ray.wait(
- [to_yield], timeout=0)
- if finished:
- cur.pop(0)
- yield ray.get(to_yield)
- else:
- break
-
- yield from ray.get(cur)
+ while cur:
+ finished, cur = ray.wait(cur)
+ yield from ray.get(finished)
if hasattr(fn, LocalIterator.ON_FETCH_START_HOOK_NAME):
unwrapped = apply_foreach
| diff --git a/python/ray/test_utils.py b/python/ray/test_utils.py
--- a/python/ray/test_utils.py
+++ b/python/ray/test_utils.py
@@ -248,7 +248,7 @@ def __init__(self, value=1):
self._sema = asyncio.Semaphore(value=value)
async def acquire(self):
- self._sema.acquire()
+ await self._sema.acquire()
async def release(self):
self._sema.release()
diff --git a/python/ray/tests/test_iter.py b/python/ray/tests/test_iter.py
--- a/python/ray/tests/test_iter.py
+++ b/python/ray/tests/test_iter.py
@@ -159,7 +159,7 @@ def test_for_each(ray_start_regular_shared):
assert list(it.gather_sync()) == [0, 4, 2, 6]
-def test_for_each_concur(ray_start_regular_shared):
+def test_for_each_concur_async(ray_start_regular_shared):
main_wait = Semaphore.remote(value=0)
test_wait = Semaphore.remote(value=0)
@@ -169,15 +169,18 @@ def task(x):
ray.get(test_wait.acquire.remote())
return i + 10
- @ray.remote(num_cpus=0.1)
+ @ray.remote(num_cpus=0.01)
def to_list(it):
return list(it)
it = from_items(
[(i, main_wait, test_wait) for i in range(8)], num_shards=2)
- it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.1})
+ it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.01})
+
+ list_promise = to_list.remote(it.gather_async())
for i in range(4):
+ assert i in [0, 1, 2, 3]
ray.get(main_wait.acquire.remote())
# There should be exactly 4 tasks executing at this point.
@@ -189,12 +192,49 @@ def to_list(it):
assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
# Finish everything and make sure the output matches a regular iterator.
- for i in range(3):
+ for i in range(7):
+ ray.get(test_wait.release.remote())
+
+ assert repr(
+ it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
+ result_list = ray.get(list_promise)
+ assert set(result_list) == set(range(10, 18))
+
+
+def test_for_each_concur_sync(ray_start_regular_shared):
+ main_wait = Semaphore.remote(value=0)
+ test_wait = Semaphore.remote(value=0)
+
+ def task(x):
+ i, main_wait, test_wait = x
+ ray.get(main_wait.release.remote())
+ ray.get(test_wait.acquire.remote())
+ return i + 10
+
+ @ray.remote(num_cpus=0.01)
+ def to_list(it):
+ return list(it)
+
+ it = from_items(
+ [(i, main_wait, test_wait) for i in range(8)], num_shards=2)
+ it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.01})
+
+ list_promise = to_list.remote(it.gather_sync())
+
+ for i in range(4):
+ assert i in [0, 1, 2, 3]
+ ray.get(main_wait.acquire.remote())
+
+ # There should be exactly 4 tasks executing at this point.
+ assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
+
+ for i in range(8):
ray.get(test_wait.release.remote())
assert repr(
it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
- assert ray.get(to_list.remote(it.gather_sync())) == list(range(10, 18))
+ result_list = ray.get(list_promise)
+ assert set(result_list) == set(range(10, 18))
def test_combine(ray_start_regular_shared):
| [ParallelIterator] for_each_concur leaks memory
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
`for_each_concur` appears to leak memory. After iterating over items, they appear to remain pinned in memory.
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```
import ray
from ray.util.iter import from_items
import numpy as np
ray.init()
def expand(x):
print("Expanding...")
return np.array([x for _ in range(2**26)])
def reduce(x):
return np.sum(x)/x.shape[0]
it = from_items(list(range(1000)))
it = it.for_each(expand, max_concurrency=2)
for x in it.gather_async():
print(reduce(x))
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| @wuisawesome Would you mind taking over this issue? For P1 tasks, each assignees will be responsible for resolving them within a month. | 2020-06-16T06:42:02 |
ray-project/ray | 9,020 | ray-project__ray-9020 | [
"7519"
] | c79a49488cde8d11919ab678e409374327695a53 | diff --git a/python/ray/autoscaler/updater.py b/python/ray/autoscaler/updater.py
--- a/python/ray/autoscaler/updater.py
+++ b/python/ray/autoscaler/updater.py
@@ -147,6 +147,44 @@ def remote_shell_command_str(self):
self.node_id)
+class SSHOptions:
+ def __init__(self, ssh_key, control_path=None, **kwargs):
+ self.ssh_key = ssh_key
+ self.arg_dict = {
+ # Supresses initial fingerprint verification.
+ "StrictHostKeyChecking": "no",
+ # SSH IP and fingerprint pairs no longer added to known_hosts.
+ # This is to remove a "REMOTE HOST IDENTIFICATION HAS CHANGED"
+ # warning if a new node has the same IP as a previously
+ # deleted node, because the fingerprints will not match in
+ # that case.
+ "UserKnownHostsFile": os.devnull,
+ # Try fewer extraneous key pairs.
+ "IdentitiesOnly": "yes",
+ # Abort if port forwarding fails (instead of just printing to
+ # stderr).
+ "ExitOnForwardFailure": "yes",
+ # Quickly kill the connection if network connection breaks (as
+ # opposed to hanging/blocking).
+ "ServerAliveInterval": 5,
+ "ServerAliveCountMax": 3
+ }
+ if control_path:
+ self.arg_dict.update({
+ "ControlMaster": "auto",
+ "ControlPath": "{}/%C".format(control_path),
+ "ControlPersist": "10s",
+ })
+ self.arg_dict.update(kwargs)
+
+ def to_ssh_options_list(self, *, timeout=60):
+ self.arg_dict["ConnectTimeout"] = "{}s".format(timeout)
+ return ["-i", self.ssh_key] + [
+ x for y in (["-o", "{}={}".format(k, v)]
+ for k, v in self.arg_dict.items()) for x in y
+ ]
+
+
class SSHCommandRunner:
def __init__(self, log_prefix, node_id, provider, auth_config,
cluster_name, process_runner, use_internal_ip):
@@ -166,36 +204,8 @@ def __init__(self, log_prefix, node_id, provider, auth_config,
self.ssh_user = auth_config["ssh_user"]
self.ssh_control_path = ssh_control_path
self.ssh_ip = None
-
- def get_default_ssh_options(self, connect_timeout):
- OPTS = [
- ("ConnectTimeout", "{}s".format(connect_timeout)),
- # Supresses initial fingerprint verification.
- ("StrictHostKeyChecking", "no"),
- # SSH IP and fingerprint pairs no longer added to known_hosts.
- # This is to remove a "REMOTE HOST IDENTIFICATION HAS CHANGED"
- # warning if a new node has the same IP as a previously
- # deleted node, because the fingerprints will not match in
- # that case.
- ("UserKnownHostsFile", os.devnull),
- ("ControlMaster", "auto"),
- ("ControlPath", "{}/%C".format(self.ssh_control_path)),
- ("ControlPersist", "10s"),
- # Try fewer extraneous key pairs.
- ("IdentitiesOnly", "yes"),
- # Abort if port forwarding fails (instead of just printing to
- # stderr).
- ("ExitOnForwardFailure", "yes"),
- # Quickly kill the connection if network connection breaks (as
- # opposed to hanging/blocking).
- ("ServerAliveInterval", 5),
- ("ServerAliveCountMax", 3),
- ]
-
- return ["-i", self.ssh_private_key] + [
- x for y in (["-o", "{}={}".format(k, v)] for k, v in OPTS)
- for x in y
- ]
+ self.base_ssh_options = SSHOptions(self.ssh_private_key,
+ self.ssh_control_path)
def get_node_ip(self):
if self.use_internal_ip:
@@ -241,7 +251,14 @@ def run(self,
exit_on_fail=False,
port_forward=None,
with_output=False,
+ ssh_options_override=None,
**kwargs):
+ ssh_options = ssh_options_override or self.base_ssh_options
+
+ assert isinstance(
+ ssh_options, SSHOptions
+ ), "ssh_options must be of type SSHOptions, got {}".format(
+ type(ssh_options))
self.set_ssh_ip_if_required()
@@ -255,7 +272,7 @@ def run(self,
"{} -> localhost:{}".format(local, remote))
ssh += ["-L", "{}:localhost:{}".format(remote, local)]
- final_cmd = ssh + self.get_default_ssh_options(timeout) + [
+ final_cmd = ssh + ssh_options.to_ssh_options_list(timeout=timeout) + [
"{}@{}".format(self.ssh_user, self.ssh_ip)
]
if cmd:
@@ -286,16 +303,20 @@ def run_rsync_up(self, source, target):
self.set_ssh_ip_if_required()
self.process_runner.check_call([
"rsync", "--rsh",
- " ".join(["ssh"] + self.get_default_ssh_options(120)), "-avz",
- source, "{}@{}:{}".format(self.ssh_user, self.ssh_ip, target)
+ " ".join(["ssh"] +
+ self.base_ssh_options.to_ssh_options_list(timeout=120)),
+ "-avz", source, "{}@{}:{}".format(self.ssh_user, self.ssh_ip,
+ target)
])
def run_rsync_down(self, source, target):
self.set_ssh_ip_if_required()
self.process_runner.check_call([
"rsync", "--rsh",
- " ".join(["ssh"] + self.get_default_ssh_options(120)), "-avz",
- "{}@{}:{}".format(self.ssh_user, self.ssh_ip, source), target
+ " ".join(["ssh"] +
+ self.base_ssh_options.to_ssh_options_list(timeout=120)),
+ "-avz", "{}@{}:{}".format(self.ssh_user, self.ssh_ip,
+ source), target
])
def remote_shell_command_str(self):
@@ -309,6 +330,7 @@ def __init__(self, docker_config, **common_args):
self.docker_name = docker_config["container_name"]
self.docker_config = docker_config
self.home_dir = None
+ self.check_docker_installed()
self.shutdown = False
def run(self,
@@ -318,6 +340,7 @@ def run(self,
port_forward=None,
with_output=False,
run_env=True,
+ ssh_options_override=None,
**kwargs):
if run_env == "auto":
run_env = "host" if cmd.find("docker") == 0 else "docker"
@@ -335,7 +358,23 @@ def run(self,
timeout=timeout,
exit_on_fail=exit_on_fail,
port_forward=None,
- with_output=False)
+ with_output=False,
+ ssh_options_override=ssh_options_override)
+
+ def check_docker_installed(self):
+ try:
+ self.ssh_command_runner.run("command -v docker")
+ return
+ except Exception:
+ install_commands = [
+ "curl -fsSL https://get.docker.com -o get-docker.sh",
+ "sudo sh get-docker.sh", "sudo usermod -aG docker $USER",
+ "sudo systemctl restart docker -f"
+ ]
+ logger.error(
+ "Docker not installed. You can install Docker by adding the "
+ "following commands to 'initialization_commands':\n" +
+ "\n".join(install_commands))
def shutdown_after_next_cmd(self):
self.shutdown = True
@@ -422,6 +461,7 @@ def __init__(self,
self.setup_commands = setup_commands
self.ray_start_commands = ray_start_commands
self.runtime_hash = runtime_hash
+ self.auth_config = auth_config
def run(self):
logger.info(self.log_prefix +
@@ -516,7 +556,10 @@ def do_update(self):
self.log_prefix + "Initialization commands",
show_status=True):
for cmd in self.initialization_commands:
- self.cmd_runner.run(cmd)
+ self.cmd_runner.run(
+ cmd,
+ ssh_options_override=SSHOptions(
+ self.auth_config.get("ssh_private_key")))
with LogTimer(
self.log_prefix + "Setup commands", show_status=True):
| [autoscaler] How to install docker within the setup yaml file?
I want to install docker and run my ray cluster on it. This is what my initialization commands look like:
```
initialization_commands: [
"sudo apt update",
"sudo apt install docker.io -y",
"sudo usermod -a -G docker ubuntu",
"sudo service docker restart",
# Docker should be restarted here, but I do not know how
"docker login -u $DOCKER_USER -p $DOCKER_PW",
]
```
Docker needs to be restarted, or `newgrp docker` has to be run, but I do not know how to do it in this context, because any command that does this like `newgrp` opens up a subshell and breaks the functionality here.
How do I do this?
Here is a stackoverflow question I posted to the same effect: https://stackoverflow.com/questions/60609535/how-to-install-and-use-docker-in-one-shell-script
| Hi, @preeth1. Do you solve the problem? | 2020-06-18T21:35:50 |
|
ray-project/ray | 9,057 | ray-project__ray-9057 | [
"9056"
] | 981f67bfb01725f1e153c881f81082fcba0ac223 | diff --git a/python/ray/tune/config_parser.py b/python/ray/tune/config_parser.py
--- a/python/ray/tune/config_parser.py
+++ b/python/ray/tune/config_parser.py
@@ -73,13 +73,13 @@ def make_parser(parser_creator=None, **kwargs):
help="Whether to checkpoint at the end of the experiment. "
"Default is False.")
parser.add_argument(
- "--no-sync-on-checkpoint",
+ "--sync-on-checkpoint",
action="store_true",
- help="Disable sync-down of trial checkpoint, which is enabled by "
- "default to guarantee recoverability. If set, checkpoint syncing from "
- "worker to driver is asynchronous. Set this only if synchronous "
- "checkpointing is too slow and trial restoration failures can be "
- "tolerated")
+ help="Enable sync-down of trial checkpoint to guarantee "
+ "recoverability. If unset, checkpoint syncing from worker "
+ "to driver is asynchronous, so unset this only if synchronous "
+ "checkpointing is too slow and trial restoration failures "
+ "can be tolerated.")
parser.add_argument(
"--keep-checkpoints-num",
default=None,
@@ -182,7 +182,7 @@ def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
remote_checkpoint_dir=spec.get("remote_checkpoint_dir"),
checkpoint_freq=args.checkpoint_freq,
checkpoint_at_end=args.checkpoint_at_end,
- sync_on_checkpoint=not args.no_sync_on_checkpoint,
+ sync_on_checkpoint=args.sync_on_checkpoint,
keep_checkpoints_num=args.keep_checkpoints_num,
checkpoint_score_attr=args.checkpoint_score_attr,
export_formats=spec.get("export_formats", []),
| [tune] sync_on_checkpoint ignored when list of experiments passed to tune.run
### What is the problem?
When passing a list of predefined Experiments to tune via the `run` function, the `sync_on_checkpoint` flag is ignored.
**Reason for this behaviour:**
The `make_parser` function takes in `no_sync_on_checkpoint`; it's set to False by default.
https://github.com/ray-project/ray/blob/981f67bfb01725f1e153c881f81082fcba0ac223/python/ray/tune/config_parser.py#L75-L77
The create_trial_from_spec function gets a `sync_on_checkpoint` from the Experiment spec, but that key is never used by the parser. So the following line makes the `sync_on_checkpoint` of the returned `Trial` object `True` by default, thus ignoring the value of `sync_on_checkpoint` in the incoming Experiment spec.
https://github.com/ray-project/ray/blob/981f67bfb01725f1e153c881f81082fcba0ac223/python/ray/tune/config_parser.py#L185
*Ray Version: All versions starting from 0.7.7 when the feature was released.*
### Reproduction (REQUIRED)
```python3
import ray
from ray import tune
from ray.tune import Experiment
def func(config):
pass
sync_true_obj = Experiment("test_sync", func, sync_on_checkpoint=True)
sync_false_obj = Experiment("test_sync", func, sync_on_checkpoint=False)
ls = [sync_true_obj, sync_false_obj]
ray.init(local_mode=True)
analysis = tune.run(ls)
```
To check the error, add the following lines just before the `return Trial(` line in `config_parser.py`
```python3
print(args)
print(args.no_sync_on_checkpoint)
```
https://github.com/ray-project/ray/blob/981f67bfb01725f1e153c881f81082fcba0ac223/python/ray/tune/config_parser.py#L173
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-06-20T12:52:33 |
||
ray-project/ray | 9,108 | ray-project__ray-9108 | [
"1221"
] | f77c638d6dc0ba4533b94fa704e46cf478479aec | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -399,6 +399,10 @@ def get_gpu_ids():
assigned_ids = [
global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids
]
+ # Give all GPUs in local_mode.
+ if global_worker.mode == LOCAL_MODE:
+ max_gpus = global_worker.node.get_resource_spec().num_gpus
+ return global_worker.original_gpu_ids[:max_gpus]
return assigned_ids
| diff --git a/python/ray/tests/test_advanced_2.py b/python/ray/tests/test_advanced_2.py
--- a/python/ray/tests/test_advanced_2.py
+++ b/python/ray/tests/test_advanced_2.py
@@ -661,6 +661,26 @@ def g():
ray.get([g.remote() for _ in range(100)])
+def test_local_mode_gpus(save_gpu_ids_shutdown_only):
+ allowed_gpu_ids = [4, 5, 6, 7, 8]
+ os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
+ [str(i) for i in allowed_gpu_ids])
+
+ from importlib import reload
+ reload(ray.worker)
+
+ ray.init(num_gpus=3, local_mode=True)
+
+ @ray.remote
+ def f():
+ gpu_ids = ray.get_gpu_ids()
+ assert len(gpu_ids) == 3
+ for gpu in gpu_ids:
+ assert gpu in allowed_gpu_ids
+
+ ray.get([f.remote() for _ in range(100)])
+
+
def test_blocking_tasks(ray_start_regular):
@ray.remote
def f(i, j):
| `ray.get_gpu_ids()` doesn't work with local_mode
<!--
General questions should be asked on the mailing list [email protected].
Before submitting an issue, please fill out the following form.
-->
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Ubuntu 16.04
- **Ray installed from (source or binary)**: binary
- **Ray version**: 0.2.1
- **Python version**: 2.7.12
- **Exact command to reproduce**:
<!--
You can obtain the Ray version with
python -c "import ray; print(ray.__version__)"
-->
### Describe the problem
`ray.get_gpu_ids()` crashes when `ray.init` was called with `PYTHON_MODE`.
| Thanks for reporting this!
I don't know if this is related, but I have the same issue. Just testing ray on my local pc with 2 gpus in an interactive python REPL.
```
import ray
ray.init(num_gpus=2)
ray.get_gpu_ids()
```
This is what I get:
```
Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 17:14:51)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import ray
>>> ray.init(num_gpus=2)
Process STDOUT and STDERR is being redirected to /tmp/raylogs/.
Waiting for redis server at 127.0.0.1:45001 to respond...
Waiting for redis server at 127.0.0.1:61294 to respond...
Starting local scheduler with the following resources: {'GPU': 2, 'CPU': 16}.
Failed to start the UI, you may need to run 'pip install jupyter'.
{'node_ip_address': '137.43.130.68', 'redis_address': '137.43.130.68:45001', 'object_store_addresses': [ObjectStoreAddress(name='/tmp/plasma_store10462356', manager_name='/tmp/plasma_manager30881352', manager_port=55957)], 'local_scheduler_socket_names': ['/tmp/scheduler26420348'], 'raylet_socket_names': [], 'webui_url': None}
>>> ray.get_gpu_ids()
[]
>>>
```
Hi, `ray.get_gpu_ids()` doesn't return all of the GPUs on the machine. It is intended to be used within a task and returns the GPU IDs that are reserved for that task. Instead, try
```
import ray
ray.init(num_gpus=2)
@ray.remote(num_gpus=1)
def f():
print(ray.get_gpu_ids())
```
That said, you probably don't need to actually call this method because Ray will automatically set the `CUDA_VISIBLE_DEVICES` environment variable.
Hi @robertnishihara thanks for the clarification, it makes things a bit more clear.
If you don't mind me asking, I'm facing another problem. I am trying to run the following example from the tutorials.
[ray_example.zip](https://github.com/ray-project/ray/files/2396322/ray_example.zip)
The goal is to have that simple example parallelize across both my gpus on my local pc, but I keep getting the following error:
```
WARNING: Serializing objects of type <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'> by expanding them as dictionaries of their fields. This behavior may be incorrect in some cases.
WARNING: Serializing objects of type <class 'tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet'> by expanding them as dictionaries of their fields. This behavior may be incorrect in some cases.
2018-09-19 10:05:19.118840: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2018-09-19 10:05:19.356415: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.7085
pciBusID: 0000:17:00.0
totalMemory: 10.92GiB freeMemory: 10.56GiB
2018-09-19 10:05:19.356444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-19 10:05:20.263083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-19 10:05:20.263122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
2018-09-19 10:05:20.263128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
2018-09-19 10:05:20.263598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10215 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:17:00.0, compute capability: 6.1)
Accuracy is 0.8903999924659729.
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0919 10:05:21.059509 4924 local_scheduler.cc:177] Killed worker pid 5344 which hadn't started yet.
```
Any ideas on why is that happening?
It looks like the application is completing successfully since it prints `Accuracy is 0.8903999924659729.`. I suspect the error message is an error that happens accidentally when Ray is shutting down, which we should probably fix.
@robertnishihara
Oh cool!
thank you for your input. Could you please help me clarify if the following Ray notation utilizes both gpus i.e. parallel training?
> import ray
ray.init(num_gpus=2)
@ray.remote(num_gpus=1)
def f(): print(ray.get_gpu_ids())
Or should we instead use:
> import ray
ray.init(num_gpus=2)
@ray.remote(num_gpus=2)
def f(): print(ray.get_gpu_ids())
for parallel training on both gpus?
One last remark is that I tried to modify the `train` method in the above example that I had attached in order to print the accuracy after every step:
from this:
```
def train(self, num_steps):
for step in range(num_steps):
batch_xs, batch_ys = self.mnist.train.next_batch(100)
self.sess.run(
self.train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys}
)
```
to this one:
```
def train(self, num_steps):
for step in range(num_steps):
batch_xs, batch_ys = self.mnist.train.next_batch(100)
self.sess.run(
self.train_step, feed_dict={self.x: batch_xs, self.y_: batch_ys}
)
print("Step: {}, Accuracy is {}:".format(
step, ray.get(self.get_accuracy.remote())
)
)
```
But it doesn't print anything, am I using the wrong notation regarding Ray? | 2020-06-23T18:53:37 |
ray-project/ray | 9,110 | ray-project__ray-9110 | [
"8483"
] | 7904235517b479f2f4b4216ba901882e35863ccc | diff --git a/rllib/contrib/maddpg/maddpg.py b/rllib/contrib/maddpg/maddpg.py
--- a/rllib/contrib/maddpg/maddpg.py
+++ b/rllib/contrib/maddpg/maddpg.py
@@ -22,6 +22,9 @@
# yapf: disable
# __sphinx_doc_begin__
DEFAULT_CONFIG = with_common_config({
+ # === Framework to run the algorithm ===
+ "framework": "tf",
+
# === Settings for each individual policy ===
# ID of the agent controlled by this policy
"agent_id": None,
diff --git a/rllib/contrib/maddpg/maddpg_policy.py b/rllib/contrib/maddpg/maddpg_policy.py
--- a/rllib/contrib/maddpg/maddpg_policy.py
+++ b/rllib/contrib/maddpg/maddpg_policy.py
@@ -74,12 +74,14 @@ def _make_continuous_space(space):
"Space {} is not supported.".format(space))
obs_space_n = [
- _make_continuous_space(space) for _, (_, space, _, _) in
- sorted(config["multiagent"]["policies"].items())
+ _make_continuous_space(space)
+ for _, (_, space, _,
+ _) in config["multiagent"]["policies"].items()
]
act_space_n = [
- _make_continuous_space(space) for _, (_, _, space, _) in
- sorted(config["multiagent"]["policies"].items())
+ _make_continuous_space(space)
+ for _, (_, _, space,
+ _) in config["multiagent"]["policies"].items()
]
# _____ Placeholders
| [rllib][bug] Some policy-names lead to seemingly unrelated errors
### Some policy-names lead to strange errors
The following very simple multi-agent script fails because of the names given to the policies
```python
from gym.spaces import Discrete, Box
import numpy as np
import ray
from ray import tune
from ray.rllib.env.multi_agent_env import MultiAgentEnv
class ErrorGame(MultiAgentEnv):
def __init__(self, env_config):
pass
def reset(self):
obs = self._obs()
return obs
def step(self, action_dict):
rewards = {0: 0, 1: 0}
obs = self._obs()
return obs, rewards, {"__all__": False}, {}
def _obs(self):
return {0: np.array([0.2, 0.2], dtype=np.float32), 1: np.array([0.2], dtype=np.float32)}
if __name__ == "__main__":
obs1 = Box(low=np.array([0] * 2), high=np.array([1] * 2))
obs2 = Box(low=np.array([0] * 1), high=np.array([1] * 1))
act1 = Discrete(1)
act2 = Discrete(1)
# helper_act = Discrete(1)
# -- POLICIES--
name1 = "seeker"
name2 = "helper"
policies = {
name1: (None, obs1, act1, {"agent_id": 0}),
name2: (None, obs2, act2, {"agent_id": 1})
}
def policy_name_mapping(x):
return {0:name1, 1:name2}[x]
config = {
"learning_starts": 100,
"evaluation_interval": 10000,
"multiagent": {
"policies": policies,
"policy_mapping_fn": policy_name_mapping,
},
}
ray.init(
num_cpus=2,
# object_store_memory=200 * 1024 * 1024,
)
tune.run(
"contrib/MADDPG",
stop={
"timesteps_total": 5000,
},
config=dict(config, **{
"env": ErrorGame,
}),
checkpoint_at_end=True,
checkpoint_freq=10000
# restore="checkpoint_50000/checkpoint-50000"
)
```
This will raise the error
` ValueError: Cannot feed value of shape (1, 2) for Tensor 'foo/obs_0:0', which has shape '(?, 1)'`
it took me a long time to find that this error will not come up if i change the names of my policies
If i replace the lines:
```python
name1 = "seeker"
name2 = "helper"
```
just with for example
```python
name1 = "seeker1"
name2 = "seeker2"
```
I can not really find a patter when it works and when not: Here are some working and not working examples:
**working**:
name1 = "seeker1" | name2 = "seeker2"
name1 = "pol" | name2 = "pool"
name1 = "ags" | name2 = "asdgasdg"
**not-working**:
name1 = "seeker" | name2 = "helper"
name1 = "rom" | name2 = "rem"
name1 = "foo" | name2 = "bar"
I can't imagine that's intentional. But if there is some pattern behind it that I don't recognize it would be useful to get a more sophisticated error message
Also, as far as I can judge now, the error seems to occur only when the observation_size of the two policies have different sizes. So my guess is that the policies might get mixed up at some point?
And also i only got this error when using `"contrib/MADDPG"` but did not test very much with other setups.
*Ray version and other system information (Python version, TensorFlow version, OS):*
software | version
--- | ---
ray |0.8.4
python | 3.6.9
tensorflow | 1.14.0
OS | Ubuntu (running in a VM on a Windows OS) Release 18.04
| 2020-06-23T20:43:33 |
||
ray-project/ray | 9,133 | ray-project__ray-9133 | [
"9131"
] | 80bcbe20c70dd284e0d93ea94fe97d1b34b81433 | diff --git a/python/ray/autoscaler/updater.py b/python/ray/autoscaler/updater.py
--- a/python/ray/autoscaler/updater.py
+++ b/python/ray/autoscaler/updater.py
@@ -169,7 +169,14 @@ def __init__(self, log_prefix, node_id, provider, auth_config,
def get_default_ssh_options(self, connect_timeout):
OPTS = [
("ConnectTimeout", "{}s".format(connect_timeout)),
+ # Supresses initial fingerprint verification.
("StrictHostKeyChecking", "no"),
+ # SSH IP and fingerprint pairs no longer added to known_hosts.
+ # This is to remove a "REMOTE HOST IDENTIFICATION HAS CHANGED"
+ # warning if a new node has the same IP as a previously
+ # deleted node, because the fingerprints will not match in
+ # that case.
+ ("UserKnownHostsFile", os.devnull),
("ControlMaster", "auto"),
("ControlPath", "{}/%C".format(self.ssh_control_path)),
("ControlPersist", "10s"),
| Ray should remove ssh fingerprints from known_hosts when tearing down a cluster
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
When Ray kills a node or tears down a cluster, it should remove the ssh fingerprints for the corresponding IP addresses from ~/.ssh/known_hosts. GCP often reuses IP addresses, so if you delete and reprovision a node, it is common for GCP to give you the same IP. Because it's a new VM, the fingerprint is going to be different than the last node's fingerprint. This triggers `WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!`, which ruins the current action. This issue probably hasn't been noticed until now because AWS rarely gives you the same IP. Currently I've been fixing this by manually deleting entries from known_hosts, but it would be nice if this happened automatically (e.g. by executing `ssh-keygen -R $DEST`). This should also happen if Ray notices that the cluster has been deleted through the GCP/AWS console.
| cc @thomasdesr This seems to be that scary error that I've seen sometimes? | 2020-06-24T23:36:39 |
|
ray-project/ray | 9,141 | ray-project__ray-9141 | [
"9128"
] | 536795ef7983b2224742c768e3e62284b5daddb6 | diff --git a/python/ray/tune/trial_runner.py b/python/ray/tune/trial_runner.py
--- a/python/ray/tune/trial_runner.py
+++ b/python/ray/tune/trial_runner.py
@@ -276,7 +276,7 @@ def checkpoint(self, force=False):
with open(tmp_file_name, "w") as f:
json.dump(runner_state, f, indent=2, cls=_TuneFunctionEncoder)
- os.rename(tmp_file_name, self.checkpoint_file)
+ os.replace(tmp_file_name, self.checkpoint_file)
if force:
self._syncer.sync_up()
else:
| [tune][rllib] Windows: FileExistsError when running rllib tune job
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
My script (available below) runs fine on Linux but does not fully run yet on Windows.
I can't tell exactly what went wrong, but the two major errors I see is:
`TypeError: len() of unsized object`
`FileExistsError: [WinError 183] Cannot create a file when that file already exists`
Full error log:
https://gist.github.com/juliusfrost/acbca090259610a176847e7026dd6d30
### Reproduction
Run on Windows OS
Install pytorch
Install the latest `ray` and `rllib` versions
Install `atari-py` if necessary
Download `train_a2c.py` from https://gist.github.com/juliusfrost/61b8be67d33b9bc9ab1faf7ada9d2ae3
Run `python train_a2c.py BreakoutNoFrameskip-v4`
if you don't have a gpu add `--gpus 0`
| #9114 Seems related
cc @mehrdadn
Thanks for the report! I added this to the list. | 2020-06-25T12:30:08 |
|
ray-project/ray | 9,142 | ray-project__ray-9142 | [
"8507"
] | 536795ef7983b2224742c768e3e62284b5daddb6 | diff --git a/rllib/examples/models/custom_loss_model.py b/rllib/examples/models/custom_loss_model.py
--- a/rllib/examples/models/custom_loss_model.py
+++ b/rllib/examples/models/custom_loss_model.py
@@ -121,6 +121,8 @@ def __init__(self, obs_space, action_space, num_outputs, model_config,
nn.Module.__init__(self)
self.input_files = input_files
+ # Create a new input reader per worker.
+ self.reader = JsonReader(self.input_files)
self.fcnet = TorchFC(
self.obs_space,
self.action_space,
@@ -135,13 +137,30 @@ def forward(self, input_dict, state, seq_lens):
@override(ModelV2)
def custom_loss(self, policy_loss, loss_inputs):
- # Create a new input reader per worker.
- reader = JsonReader(self.input_files)
- input_ops = reader.tf_input_ops()
+ """Calculates a custom loss on top of the given policy_loss(es).
+
+ Args:
+ policy_loss (List[TensorType]): The list of already calculated
+ policy losses (as many as there are optimizers).
+ loss_inputs (TensorStruct): Struct of np.ndarrays holding the
+ entire train batch.
+
+ Returns:
+ List[TensorType]: The altered list of policy losses. In case the
+ custom loss should have its own optimizer, make sure the
+ returned list is one larger than the incoming policy_loss list.
+ In case you simply want to mix in the custom loss into the
+ already calculated policy losses, return a list of altered
+ policy losses (as done in this example below).
+ """
+ # Get the next batch from our input files.
+ batch = self.reader.next()
# Define a secondary loss by building a graph copy with weight sharing.
obs = restore_original_dimensions(
- tf.cast(input_ops["obs"], tf.float32), self.obs_space)
+ torch.from_numpy(batch["obs"]).float(),
+ self.obs_space,
+ tensorlib="torch")
logits, _ = self.forward({"obs": obs}, [], None)
# You can also add self-supervised losses easily by referencing tensors
@@ -155,11 +174,15 @@ def custom_loss(self, policy_loss, loss_inputs):
action_dist = TorchCategorical(logits, self.model_config)
self.policy_loss = policy_loss
self.imitation_loss = torch.mean(
- -action_dist.logp(input_ops["actions"]))
- return policy_loss + 10 * self.imitation_loss
+ -action_dist.logp(torch.from_numpy(batch["actions"])))
+
+ # Add the imitation loss to each already calculated policy loss term.
+ # Alternatively (if custom loss has its own optimizer):
+ # return policy_loss + [10 * self.imitation_loss]
+ return [l + 10 * self.imitation_loss for l in policy_loss]
def custom_stats(self):
return {
- "policy_loss": self.policy_loss,
+ "policy_loss": torch.mean(self.policy_loss),
"imitation_loss": self.imitation_loss,
}
diff --git a/rllib/offline/json_reader.py b/rllib/offline/json_reader.py
--- a/rllib/offline/json_reader.py
+++ b/rllib/offline/json_reader.py
@@ -45,7 +45,7 @@ def __init__(self, inputs, ioctx=None):
logger.warning(
"Treating input directory as glob pattern: {}".format(
inputs))
- if urlparse(inputs).scheme:
+ if urlparse(inputs).scheme not in ["d", ""]:
raise ValueError(
"Don't know how to glob over `{}`, ".format(inputs) +
"please specify a list of files to read instead.")
diff --git a/rllib/policy/torch_policy.py b/rllib/policy/torch_policy.py
--- a/rllib/policy/torch_policy.py
+++ b/rllib/policy/torch_policy.py
@@ -240,6 +240,9 @@ def learn_on_batch(self, postprocessed_batch):
train_batch = self._lazy_tensor_dict(postprocessed_batch)
loss_out = force_list(
self._loss(self, self.model, self.dist_class, train_batch))
+ # Call Model's custom-loss with Policy loss outputs and train_batch.
+ if self.model:
+ loss_out = self.model.custom_loss(loss_out, train_batch)
assert len(loss_out) == len(self._optimizers)
# assert not any(torch.isnan(l) for l in loss_out)
| [rllib] Pytorch missing custom loss
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Running `rllib/examples/custom_loss.py` example, the tensorflow implementation displays the custom loss, but Pytorch does not. During the process, I had to update the `rllib/examples/models/custom_loss_model.py` models method name from `custom_stats `to `metrics`.
*Ray version and other system information (Python version, TensorFlow version, OS):*
Ray version: latest wheel
Python: 3.6.8
TF: 2.1
OS: RHEL 7.7
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
Use existing example script in Rllib
If we cannot run your script, we cannot fix your issue.
- [x ] I have verified my script runs in a clean environment and reproduces the issue.
- [x ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| #8193
Sorry, this may have slipped through the cracks. I'll take a look and provide the missing example.
Got it, `ModelV2.custom_loss()` is not supported for PyTorch yet at all (ignored). Will fix this.
Yes, `metrics` is the new name (ModelV2) for the old `custom_stats` (Model(V1)).
I'm looking closer at the current tf policies, and I'm actually struggling to see where `model.custom_loss` has been implemented in tf IMPALA either... Can you highlight the part of the code where this is done for my benefit? In the case of IMPALA, I know it calls the `model.custom_loss` method, as I can see the custom loss metrics I've produced, but what is not clear is that it is actually making it into the gradient calculations... `build_vtrace_loss` seems to just call the class `VTraceLoss`, where I don't see any references to the output of` model.custom_loss`. and dynamic_tf_policy seems to just return the output of this loss function... so I'm struggling to see where it comes into play.
Looks like it is only used once [here](https://github.com/ray-project/ray/blob/master/rllib/policy/tf_policy.py#L215) and from there on it's accessed through `self._loss`.
Thanks for that, not sure how that eluded me… it looks like on further review my issue on the TF side was using 1.x style tf.losses.get_regularization_loss rather than model.losses … once I switched everything is working for TF models.
On May 28, 2020, at 4:50 PM, Jan Blumenkamp <[email protected]> wrote:
>
> Looks like it is only used once here and from there on it's accessed through self._loss.
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub, or unsubscribe.
>
> Got it, `ModelV2.custom_loss()` is not supported for PyTorch yet at all (ignored). Will fix this.
>
> Yes, `metrics` is the new name (ModelV2) for the old `custom_stats` (Model(V1)).
Hi! Is there any update on this? If update is not expected soon, I might give it a shot with tensorflow side. Many thanks.
Hey, @cedros23 . Taking another look right now. | 2020-06-25T13:03:46 |
|
ray-project/ray | 9,227 | ray-project__ray-9227 | [
"8784"
] | f1173d55e051467aa0ec4fccc2f7ff3bbe06add0 | diff --git a/python/ray/serve/metric/client.py b/python/ray/serve/metric/client.py
--- a/python/ray/serve/metric/client.py
+++ b/python/ray/serve/metric/client.py
@@ -1,11 +1,8 @@
import asyncio
-from typing import Dict, Optional, Tuple
+from typing import Dict, Optional, Tuple, List
-from ray.serve.metric.types import (
- MetricType,
- convert_event_type_to_class,
- MetricMetadata,
-)
+from ray.serve.metric.types import (MetricType, convert_event_type_to_class,
+ MetricMetadata, MetricRecord)
from ray.serve.utils import _get_logger
from ray.serve.constants import METRIC_PUSH_INTERVAL_S
@@ -29,8 +26,8 @@ def __init__(
self.exporter = metric_exporter_actor
self.default_labels = default_labels or dict()
- self.registered_metrics: Dict[str, MetricMetadata] = dict()
- self.metric_records = []
+ self.registered_metrics: Dict[int, MetricMetadata] = dict()
+ self.metric_records: List[MetricRecord] = []
assert asyncio.get_event_loop().is_running()
self.push_task = asyncio.get_event_loop().create_task(
@@ -101,10 +98,6 @@ def _new_metric(
description: str,
label_names: Tuple[str] = (),
):
- if name in self.registered_metrics:
- raise ValueError(
- "Metric with name {} is already registered.".format(name))
-
if not isinstance(label_names, tuple):
raise ValueError("label_names need to be a tuple, it is {}".format(
type(label_names)))
@@ -116,11 +109,17 @@ def _new_metric(
label_names=label_names,
default_labels=self.default_labels.copy(),
)
+
+ key = hash(metric_metadata)
+ if key in self.registered_metrics:
+ raise ValueError("Metric named {} and associated metadata "
+ "is already registered.".format(name))
+ self.registered_metrics[key] = metric_metadata
+
metric_class = convert_event_type_to_class(metric_type)
metric_object = metric_class(
- client=self, name=name, label_names=label_names)
+ client=self, key=key, label_names=label_names)
- self.registered_metrics[name] = metric_metadata
return metric_object
async def _push_to_exporter_once(self):
diff --git a/python/ray/serve/metric/exporter.py b/python/ray/serve/metric/exporter.py
--- a/python/ray/serve/metric/exporter.py
+++ b/python/ray/serve/metric/exporter.py
@@ -1,4 +1,4 @@
-from typing import Dict
+from typing import Dict, Any
from collections import Counter, namedtuple
import ray
@@ -46,15 +46,15 @@ def __init__(self, exporter_class: ExporterInterface):
# TODO(simon): Add support for initializer args and kwargs.
self.exporter = exporter_class()
- # Stores the mapping metric_name -> MetricMetadata
+ # Stores the all recorded MetricMetadata (keyed by hash key)
# This field is tolerant to failures since each client will always push
# an updated copy of the metadata for each ingest call.
- self.metric_metadata = dict()
+ self.metric_metadata: Dict[int, MetricMetadata] = dict()
logger.debug("Initialized with metric exporter of type {}".format(
type(self.exporter)))
- def ingest(self, metric_metadata: Dict[str, MetricMetadata],
+ def ingest(self, metric_metadata: Dict[int, MetricMetadata],
batch: MetricBatch):
self.metric_metadata.update(metric_metadata)
self.exporter.export(self.metric_metadata, batch)
@@ -70,9 +70,10 @@ def __init__(self):
# Keep track of latest observation of measures
self.latest_measures: Dict[namedtuple, float] = dict()
- def export(self, metric_metadata, metric_batch):
+ def export(self, metric_metadata: Dict[int, MetricMetadata],
+ metric_batch: MetricBatch):
for record in metric_batch:
- metadata = metric_metadata[record.name]
+ metadata = metric_metadata[record.key]
metric_key = make_metric_namedtuple(metadata, record)
if metadata.type == MetricType.COUNTER:
self.counters[metric_key] += record.value
@@ -88,7 +89,8 @@ def inspect_metrics(self):
for info_tuple, value in metrics_to_collect.items():
# Represent the metric type as a human readable name
info_tuple = info_tuple._replace(type=str(info_tuple.type))
- items.append({"info": info_tuple._asdict(), "value": value})
+ # _asdict returns OrderedDict, we just need to return regular dict
+ items.append({"info": dict(info_tuple._asdict()), "value": value})
return items
@@ -103,7 +105,7 @@ def __init__(self):
}
self.prom_generate_latest = generate_latest
- self.metrics_cache = dict()
+ self.metrics_cache: Dict[str, Any] = dict()
self.default_labels = dict()
self.registry = CollectorRegistry()
@@ -112,10 +114,12 @@ def inspect_metrics(self):
def export(self, metric_metadata, metric_batch):
self._process_metric_metadata(metric_metadata)
- self._process_batch(metric_batch)
+ self._process_batch(metric_metadata, metric_batch)
def _process_metric_metadata(self, metric_metadata):
- for name, metric_metadata in metric_metadata.items():
+ for key, metric_metadata in metric_metadata.items():
+ name = metric_metadata.name
+
if name not in self.metrics_cache:
constructor = self.metric_type_to_prom_type[
metric_metadata.type]
@@ -124,7 +128,7 @@ def _process_metric_metadata(self, metric_metadata):
label_names = tuple(
default_labels.keys()) + metric_metadata.label_names
metric_object = constructor(
- metric_metadata.name,
+ name,
metric_metadata.description,
labelnames=label_names,
registry=self.registry,
@@ -132,14 +136,17 @@ def _process_metric_metadata(self, metric_metadata):
self.metrics_cache[name] = (metric_object,
metric_metadata.type)
- self.default_labels[name] = default_labels
- def _process_batch(self, batch):
- for name, labels, value in batch:
- assert name in self.metrics_cache, (
- "Metrics {} was not registered.".format(name))
+ self.default_labels[key] = metric_metadata.default_labels
+
+ def _process_batch(self, metric_metadata, batch):
+ for key, labels, value in batch:
+ if key not in metric_metadata:
+ continue
+
+ name = metric_metadata[key].name
metric, metric_type = self.metrics_cache[name]
- default_labels = self.default_labels[name]
+ default_labels = self.default_labels[key]
merged_labels = {**default_labels, **labels}
if metric_type == MetricType.COUNTER:
metric.labels(**merged_labels).inc(value)
diff --git a/python/ray/serve/metric/types.py b/python/ray/serve/metric/types.py
--- a/python/ray/serve/metric/types.py
+++ b/python/ray/serve/metric/types.py
@@ -2,27 +2,53 @@
from typing import Tuple, List, Dict, Optional
from collections import namedtuple
+
+class MetricType(enum.IntEnum):
+ COUNTER = 1
+ MEASURE = 2
+
+
# We split the information about a metric into two parts: the MetricMetadata
# and MetricRecord. Metadata is declared at creation time and include names
# and the label names. The label values will be supplied at observation time.
-MetricMetadata = namedtuple(
- "MetricMetadata",
- ["name", "type", "description", "label_names", "default_labels"])
-MetricRecord = namedtuple("MetricRecord", ["name", "labels", "value"])
+class MetricMetadata:
+ def __init__(self, name: str, type: MetricType, description: str,
+ label_names: Tuple[str], default_labels: Dict[str, str]):
+ self.name = name
+ self.type = type
+ self.description = description
+ self.label_names = label_names
+ self.default_labels = default_labels
+
+ def __eq__(self, value: "MetricMetadata"):
+ if not isinstance(value, MetricMetadata):
+ return False
+
+ return (self.name == value.name and self.type == self.type
+ and self.description == value.description
+ and self.label_names == value.label_names
+ and self.default_labels == value.default_labels)
+
+ def __hash__(self):
+ return hash((self.name, self.type, self.description, self.label_names,
+ frozenset(self.default_labels.items())))
+
+
+MetricRecord = namedtuple("MetricRecord", ["key", "labels", "value"])
MetricBatch = List[MetricRecord]
class BaseMetric:
def __init__(self,
client,
- name: str,
+ key: int,
label_names: Tuple[str],
dynamic_labels: Optional[Dict[str, str]] = None):
"""Represent a single metric stream
Args:
client(MetricClient): The client object to push update to.
- name(str): The name of the metric.
+ key(int): The unique hash key of the metric.
label_names(Tuple[str]): The names of the labels that must be set
before an observation.
dynamic_labels(Optional[Dict[str,str]]): A partially preset labels.
@@ -30,7 +56,7 @@ def __init__(self,
``metric.labels(a=b).labels(c=d)``.
"""
self.client = client
- self.name = name
+ self.key = key
self.dynamic_labels = dynamic_labels or dict()
self.label_names = label_names
@@ -56,7 +82,7 @@ def labels(self, **kwargs):
"label names. Allowed label names are {}.".format(
k, self.label_names))
new_dynamic_labels[k] = str(v)
- return type(self)(self.client, self.name, self.label_names,
+ return type(self)(self.client, self.key, self.label_names,
new_dynamic_labels)
@@ -67,7 +93,7 @@ def add(self, increment=1):
"""Increment the counter by some amount. Default is 1"""
self.check_all_labels_fulfilled_or_error()
self.client.metric_records.append(
- MetricRecord(self.name, self.dynamic_labels, increment))
+ MetricRecord(self.key, self.dynamic_labels, increment))
class Measure(BaseMetric):
@@ -75,12 +101,7 @@ def record(self, value):
"""Record the given value for the measure"""
self.check_all_labels_fulfilled_or_error()
self.client.metric_records.append(
- MetricRecord(self.name, self.dynamic_labels, value))
-
-
-class MetricType(enum.IntEnum):
- COUNTER = 1
- MEASURE = 2
+ MetricRecord(self.key, self.dynamic_labels, value))
def convert_event_type_to_class(event_type: MetricType) -> BaseMetric:
| diff --git a/python/ray/serve/tests/test_metric.py b/python/ray/serve/tests/test_metric.py
--- a/python/ray/serve/tests/test_metric.py
+++ b/python/ray/serve/tests/test_metric.py
@@ -46,29 +46,29 @@ async def test_client():
await collector._push_to_exporter_once()
- assert exporter.metadata == {
- "counter": MetricMetadata(
- name="counter",
- type=MetricType.COUNTER,
- description="",
- label_names=("a", "b"),
- default_labels={"default": "label"},
- ),
- "measure": MetricMetadata(
- name="measure",
- type=MetricType.MEASURE,
- description="",
- label_names=(),
- default_labels={"default": "label"},
- )
- }
- assert exporter.batches == [("counter", {
- "a": "1",
- "b": "2"
- }, 1), ("counter", {
- "a": "1",
- "b": "3"
- }, 42), ("measure", {}, 2)]
+ assert MetricMetadata(
+ name="counter",
+ type=MetricType.COUNTER,
+ description="",
+ label_names=("a", "b"),
+ default_labels={"default": "label"},
+ ) in exporter.metadata.values()
+ assert MetricMetadata(
+ name="measure",
+ type=MetricType.MEASURE,
+ description="",
+ label_names=(),
+ default_labels={"default": "label"},
+ ) in exporter.metadata.values()
+
+ metric_values = [item.value for item in exporter.batches]
+ assert set(metric_values) == {1, 42, 2}
+
+ metric_labels = [
+ frozenset(item.labels.items()) for item in exporter.batches
+ ]
+ assert frozenset(dict(a="1", b="2").items()) in metric_labels
+ assert frozenset(dict(a="1", b="3").items()) in metric_labels
async def test_in_memory_exporter(serve_instance):
@@ -140,6 +140,26 @@ async def test_prometheus_exporter(serve_instance):
assert fragment in metric_stored
+async def test_prometheus_conflicting_labels(serve_instance):
+ exporter = MetricExporterActor.remote(PrometheusExporter)
+
+ collector_a = MetricClient(
+ exporter, push_interval=2, default_labels={"default": "a"})
+ collector_b = MetricClient(
+ exporter, push_interval=2, default_labels={"default": "b"})
+
+ for collector in [collector_a, collector_b]:
+ counter = collector.new_counter("num")
+ counter.add()
+ await collector._push_to_exporter_once()
+
+ metric_stored = (await exporter.inspect_metrics.remote()).decode()
+
+ fragments = ['num_total{default="a"}', 'num_total{default="b"}']
+ for fragment in fragments:
+ assert fragment in metric_stored
+
+
async def test_system_metric_endpoints(serve_instance):
def test_error_counter(flask_request):
1 / 0
| [Serve] Prometheus Metric Overrides
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Serve's prometheus exporter is stores a metric name -> default label mapping, this is a bug because there are can different metrics for the same name without different default labels.
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
Code
```python
import time
import pprint
import ray
from ray import serve
from ray.serve.constants import SERVE_MASTER_NAME
from ray.serve.metric import MetricClient, PrometheusExporter
serve.init(metric_exporter=PrometheusExporter)
@ray.remote
class AsyncActor:
async def run(self, key):
actor = ray.get_actor(SERVE_MASTER_NAME)
[metric_exporter] = ray.get(actor.get_metric_exporter.remote())
client = MetricClient(metric_exporter, default_labels={"key": key})
counter = client.new_counter("same_name")
for _ in range(10):
counter.add()
await client._push_to_exporter_once()
a = AsyncActor.remote()
b = AsyncActor.remote()
ray.get([a.run.remote("a"), b.run.remote("b")])
print(serve.stat().decode())
```
Output
```
2020-06-04 12:07:19,281 INFO resource_spec.py:212 -- Starting Ray with 21.97 GiB memory available for workers and up to 10.99 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).
2020-06-04 12:07:19,500 WARNING services.py:923 -- Redis failed to start, retrying now.
2020-06-04 12:07:19,740 INFO services.py:1165 -- View the Ray dashboard at localhost:8265
(pid=61255) 2020-06-04 12:07:21,622 INFO master.py:182 -- Starting metric exporter with name 'SERVE_METRIC_SINK_ACTOR'
(pid=61255) 2020-06-04 12:07:21,636 INFO master.py:130 -- Starting router with name 'SERVE_ROUTER_ACTOR'
(pid=61255) 2020-06-04 12:07:21,644 INFO master.py:152 -- Starting HTTP proxy with name 'SERVE_PROXY_ACTOR' on node 'node:192.168.31.141'
(pid=61267) INFO: Started server process [61267]
(pid=61267) INFO: Waiting for application startup.
(pid=61267) INFO: Application startup complete.
# HELP same_name_total
# TYPE same_name_total counter
same_name_total{key="a"} 20.0
# TYPE same_name_created gauge
same_name_created{key="a"} 1.591297642889063e+09
```
Expected:
```
same_name_total{key="a"} 10.0
...
same_name_total{key="b"} 10.0
```
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-06-30T23:35:07 |
|
ray-project/ray | 9,228 | ray-project__ray-9228 | [
"9251"
] | e0d8b58969728054d1800abc925aa5352882baa8 | diff --git a/python/ray/async_compat.py b/python/ray/async_compat.py
--- a/python/ray/async_compat.py
+++ b/python/ray/async_compat.py
@@ -3,8 +3,6 @@
It will raise SyntaxError when importing from Python 2.
"""
import asyncio
-from collections import namedtuple
-import time
import inspect
try:
@@ -35,84 +33,13 @@ async def wrapper(*args, **kwargs):
return wrapper
-# Class encapsulate the get result from direct actor.
-# Case 1: plasma_fallback_id=None, result=<Object>
-# Case 2: plasma_fallback_id=ObjectID, result=None
-AsyncGetResponse = namedtuple("AsyncGetResponse",
- ["plasma_fallback_id", "result"])
-
-
def get_async(object_id):
- """Asyncio compatible version of ray.get"""
- # Delayed import because raylet import this file and
- # it creates circular imports.
- from ray.experimental.async_api import init as async_api_init, as_future
- from ray.experimental.async_plasma import PlasmaObjectFuture
-
- assert isinstance(object_id, ray.ObjectID), "Batched get is not supported."
-
- # Setup
- async_api_init()
+ """C++ Asyncio version of ray.get"""
loop = asyncio.get_event_loop()
core_worker = ray.worker.global_worker.core_worker
- # Here's the callback used to implement async get logic.
- # What we want:
- # - If direct call, first try to get it from in memory store.
- # If the object if promoted to plasma, retry it from plasma API.
- # - If not direct call, directly use plasma API to get it.
- user_future = loop.create_future()
-
- # We have three future objects here.
- # user_future is directly returned to the user from this function.
- # and it will be eventually fulfilled by the final result.
- # inner_future is the first attempt to retrieve the object. It can be
- # fulfilled by either core_worker.get_async or plasma_api.as_future.
- # When inner_future completes, done_callback will be invoked. This
- # callback set the final object in user_future if the object hasn't
- # been promoted by plasma, otherwise it will retry from plasma.
- # retry_plasma_future is only created when we are getting objects that's
- # promoted to plasma. It will also invoke the done_callback when it's
- # fulfilled.
-
- def done_callback(future):
- result = future.result()
- # Result from async plasma, transparently pass it to user future
- if isinstance(future, PlasmaObjectFuture):
- if isinstance(result, ray.exceptions.RayTaskError):
- ray.worker.last_task_error_raise_time = time.time()
- user_future.set_exception(result.as_instanceof_cause())
- else:
- user_future.set_result(result)
- else:
- # Result from direct call.
- assert isinstance(result, AsyncGetResponse), result
- if result.plasma_fallback_id is None:
- # If this future has result set already, we just need to
- # skip the set result/exception procedure.
- if user_future.done():
- return
-
- if isinstance(result.result, ray.exceptions.RayTaskError):
- ray.worker.last_task_error_raise_time = time.time()
- user_future.set_exception(
- result.result.as_instanceof_cause())
- else:
- user_future.set_result(result.result)
- else:
- # Schedule plasma to async get, use the the same callback.
- retry_plasma_future = as_future(result.plasma_fallback_id)
- retry_plasma_future.add_done_callback(done_callback)
- # A hack to keep reference to the future so it doesn't get GC.
- user_future.retry_plasma_future = retry_plasma_future
-
- inner_future = loop.create_future()
- # We must add the done_callback before sending to in_memory_store_get
- inner_future.add_done_callback(done_callback)
- core_worker.in_memory_store_get_async(object_id, inner_future)
- # A hack to keep reference to inner_future so it doesn't get GC.
- user_future.inner_future = inner_future
+ future = loop.create_future()
+ core_worker.get_async(object_id, future)
# A hack to keep a reference to the object ID for ref counting.
- user_future.object_id = object_id
-
- return user_future
+ future.object_id = object_id
+ return future
diff --git a/python/ray/experimental/async_api.py b/python/ray/experimental/async_api.py
deleted file mode 100644
--- a/python/ray/experimental/async_api.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import asyncio
-
-import ray
-from ray.experimental.async_plasma import PlasmaEventHandler
-from ray.services import logger
-
-handler = None
-
-
-def init():
- """Initialize plasma event handlers for asyncio support."""
- assert ray.is_initialized(), "Please call ray.init before async_api.init"
-
- global handler
- if handler is None:
- worker = ray.worker.global_worker
- loop = asyncio.get_event_loop()
- handler = PlasmaEventHandler(loop, worker)
- worker.core_worker.set_plasma_added_callback(handler)
- logger.debug("AsyncPlasma Connection Created!")
-
-
-def as_future(object_id):
- """Turn an object_id into a Future object.
-
- Args:
- object_id: A Ray object_id.
-
- Returns:
- PlasmaObjectFuture: A future object that waits the object_id.
- """
- if handler is None:
- init()
- return handler.as_future(object_id)
-
-
-def shutdown():
- """Manually shutdown the async API.
-
- Cancels all related tasks and all the socket transportation.
- """
- global handler
- if handler is not None:
- handler.close()
- handler = None
diff --git a/python/ray/experimental/async_plasma.py b/python/ray/experimental/async_plasma.py
deleted file mode 100644
--- a/python/ray/experimental/async_plasma.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import asyncio
-
-import ray
-from ray.services import logger
-from collections import defaultdict
-
-
-class PlasmaObjectFuture(asyncio.Future):
- """This class is a wrapper for a Future on Plasma."""
- pass
-
-
-class PlasmaEventHandler:
- """This class is an event handler for Plasma."""
-
- def __init__(self, loop, worker):
- super().__init__()
- self._loop = loop
- self._worker = worker
- self._waiting_dict = defaultdict(list)
-
- def _complete_future(self, ray_object_id):
- # TODO(ilr): Consider race condition between popping from the
- # waiting_dict and as_future appending to the waiting_dict's list.
- logger.debug(
- "Completing plasma futures for object id {}".format(ray_object_id))
- if ray_object_id not in self._waiting_dict:
- return
- obj = self._worker.get_objects([ray_object_id], timeout=0)[0]
- futures = self._waiting_dict.pop(ray_object_id)
- for fut in futures:
- try:
- fut.set_result(obj)
- except asyncio.InvalidStateError:
- # Avoid issues where process_notifications
- # and check_immediately both get executed
- logger.debug("Failed to set result for future {}."
- "Most likely already set.".format(fut))
-
- def close(self):
- """Clean up this handler."""
- for futures in self._waiting_dict.values():
- for fut in futures:
- fut.cancel()
-
- def check_immediately(self, object_id):
- ready, _ = ray.wait([object_id], timeout=0)
- if ready:
- self._complete_future(object_id)
-
- def as_future(self, object_id, check_ready=True):
- """Turn an object_id into a Future object.
-
- Args:
- object_id: A Ray's object_id.
- check_ready (bool): If true, check if the object_id is ready.
-
- Returns:
- PlasmaObjectFuture: A future object that waits the object_id.
- """
- if not isinstance(object_id, ray.ObjectID):
- raise TypeError("Input should be a Ray ObjectID.")
-
- future = PlasmaObjectFuture(loop=self._loop)
- self._waiting_dict[object_id].append(future)
- if not self.check_immediately(object_id) and len(
- self._waiting_dict[object_id]) == 1:
- # Only subscribe once
- self._worker.core_worker.subscribe_to_plasma_object(object_id)
- return future
| diff --git a/ci/asan_tests/run_asan_tests.sh b/ci/asan_tests/run_asan_tests.sh
--- a/ci/asan_tests/run_asan_tests.sh
+++ b/ci/asan_tests/run_asan_tests.sh
@@ -35,7 +35,7 @@ asan_run() {
cd "${ROOT_DIR}"/../..
# async plasma test
- python -m pytest -v --durations=5 --timeout=300 python/ray/experimental/test/async_test.py
+ python -m pytest -v --durations=5 --timeout=300 python/ray/tests/test_async.py
# Ray tests
bazel test --test_tag_filters=-jenkins_only python/ray/serve/...
diff --git a/python/ray/tests/BUILD b/python/ray/tests/BUILD
--- a/python/ray/tests/BUILD
+++ b/python/ray/tests/BUILD
@@ -472,3 +472,11 @@ py_test(
tags = ["exclusive"],
deps = ["//:ray_lib"],
)
+
+py_test(
+ name = "test_async",
+ size = "medium",
+ srcs = SRCS + ["test_async.py"],
+ tags = ["exclusive"],
+ deps = ["//:ray_lib"],
+)
diff --git a/python/ray/experimental/test/async_test.py b/python/ray/tests/test_async.py
similarity index 67%
rename from python/ray/experimental/test/async_test.py
rename to python/ray/tests/test_async.py
--- a/python/ray/experimental/test/async_test.py
+++ b/python/ray/tests/test_async.py
@@ -1,20 +1,19 @@
import asyncio
+import sys
import time
-import pytest
import numpy as np
+import pytest
+
import ray
-from ray.experimental import async_api
@pytest.fixture
def init():
ray.init(num_cpus=4)
- async_api.init()
asyncio.get_event_loop().set_debug(False)
yield
- async_api.shutdown()
ray.shutdown()
@@ -33,7 +32,7 @@ def f():
time.sleep(1)
return np.zeros(1024 * 1024, dtype=np.uint8)
- future = async_api.as_future(f.remote())
+ future = f.remote().as_future()
result = asyncio.get_event_loop().run_until_complete(future)
assert isinstance(result, np.ndarray)
@@ -41,7 +40,7 @@ def f():
def test_gather(init):
loop = asyncio.get_event_loop()
tasks = gen_tasks()
- futures = [async_api.as_future(obj_id) for obj_id in tasks]
+ futures = [obj_id.as_future() for obj_id in tasks]
results = loop.run_until_complete(asyncio.gather(*futures))
assert all(a[0] == b[0] for a, b in zip(results, ray.get(tasks)))
@@ -49,7 +48,7 @@ def test_gather(init):
def test_wait(init):
loop = asyncio.get_event_loop()
tasks = gen_tasks()
- futures = [async_api.as_future(obj_id) for obj_id in tasks]
+ futures = [obj_id.as_future() for obj_id in tasks]
results, _ = loop.run_until_complete(asyncio.wait(futures))
assert set(results) == set(futures)
@@ -57,7 +56,7 @@ def test_wait(init):
def test_wait_timeout(init):
loop = asyncio.get_event_loop()
tasks = gen_tasks(10)
- futures = [async_api.as_future(obj_id) for obj_id in tasks]
+ futures = [obj_id.as_future() for obj_id in tasks]
fut = asyncio.wait(futures, timeout=5)
results, _ = loop.run_until_complete(fut)
assert list(results)[0] == futures[0]
@@ -75,12 +74,7 @@ async def g(n):
await asyncio.sleep(n * 0.1)
return n, np.zeros(1024 * 1024, dtype=np.uint8)
- tasks = [
- async_api.as_future(f.remote(1)),
- g(2),
- async_api.as_future(f.remote(3)),
- g(4)
- ]
+ tasks = [f.remote(1).as_future(), g(2), f.remote(3).as_future(), g(4)]
results = loop.run_until_complete(asyncio.gather(*tasks))
assert [result[0] for result in results] == [1, 2, 3, 4]
@@ -100,11 +94,31 @@ async def _g(_n):
return asyncio.ensure_future(_g(n))
- tasks = [
- async_api.as_future(f.remote(0.1)),
- g(7),
- async_api.as_future(f.remote(5)),
- g(2)
- ]
+ tasks = [f.remote(0.1).as_future(), g(7), f.remote(5).as_future(), g(2)]
ready, _ = loop.run_until_complete(asyncio.wait(tasks, timeout=4))
assert set(ready) == {tasks[0], tasks[-1]}
+
+
[email protected]
[email protected](
+ "ray_start_regular_shared", [{
+ "object_store_memory": 100 * 1024 * 1024,
+ }],
+ indirect=True)
+async def test_garbage_collection(ray_start_regular_shared):
+ # This is a regression test for
+ # https://github.com/ray-project/ray/issues/9134
+
+ @ray.remote
+ def f():
+ return np.zeros(40 * 1024 * 1024, dtype=np.uint8)
+
+ for _ in range(10):
+ await f.remote()
+ for _ in range(10):
+ put_id = ray.put(np.zeros(40 * 1024 * 1024, dtype=np.uint8))
+ await put_id
+
+
+if __name__ == "__main__":
+ sys.exit(pytest.main(["-v", __file__]))
diff --git a/python/ray/tests/test_asyncio.py b/python/ray/tests/test_asyncio.py
--- a/python/ray/tests/test_asyncio.py
+++ b/python/ray/tests/test_asyncio.py
@@ -1,8 +1,9 @@
# coding: utf-8
import asyncio
+import sys
import threading
+
import pytest
-import sys
import ray
from ray.test_utils import SignalActor
@@ -113,10 +114,6 @@ async def test_asyncio_get(ray_start_regular_shared, event_loop):
asyncio.set_event_loop(loop)
loop.set_debug(True)
- # This is needed for async plasma
- from ray.experimental.async_api import init
- init()
-
# Test Async Plasma
@ray.remote
def task():
| Aysyncio ObjectID becomes None
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Calling `await` on a task's ObjectID inside an async Actor sometimes results in the following error `AttributeError: 'NoneType' object has no attribute 'call_soon_threadsafe'`. This happens when the task runs after the actor method returns. This does not happen if `asyncio.wait()` is called. Sometimes this issue results in a weird segfault.
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
- Change this [line](https://github.com/ray-project/ray/blob/master/python/ray/serve/router.py#L324) to `await worker.handle_request.remote(req)`
- Run `pytest -vs serve/tests/test_api.py::test_shadow_traffic`
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-07-01T00:55:54 |
|
ray-project/ray | 9,297 | ray-project__ray-9297 | [
"9268"
] | 7a2d7964d8f944bd60c1f03d58d6cc190c7a7015 | diff --git a/python/ray/tune/logger.py b/python/ray/tune/logger.py
--- a/python/ray/tune/logger.py
+++ b/python/ray/tune/logger.py
@@ -187,7 +187,7 @@ class TBXLogger(Logger):
"""
# NoneType is not supported on the last TBX release yet.
- VALID_HPARAMS = (str, bool, int, float, list)
+ VALID_HPARAMS = (str, bool, np.bool8, int, np.integer, float, list)
def _init(self):
try:
| diff --git a/python/ray/tune/tests/test_logger.py b/python/ray/tune/tests/test_logger.py
--- a/python/ray/tune/tests/test_logger.py
+++ b/python/ray/tune/tests/test_logger.py
@@ -2,6 +2,7 @@
import unittest
import tempfile
import shutil
+import numpy as np
from ray.tune.logger import JsonLogger, CSVLogger, TBXLogger
@@ -46,7 +47,17 @@ def testJSON(self):
logger.close()
def testTBX(self):
- config = {"a": 2, "b": [1, 2], "c": {"c": {"D": 123}}}
+ config = {
+ "a": 2,
+ "b": [1, 2],
+ "c": {
+ "c": {
+ "D": 123
+ }
+ },
+ "d": np.int64(1),
+ "e": np.bool8(True)
+ }
t = Trial(evaluated_params=config, trial_id="tbx")
logger = TBXLogger(config=config, logdir=self.test_dir, trial=t)
logger.on_result(result(0, 4))
| [tune] Parameters from `tune.choice()` do not get logged to TensorBoard when integers
### What is the problem?
When providing parameters via `tune.choice()` that include integers, the values are not logged to TensorBoard's HPARAMS section.
The issue is that `numpy.random.choice([1, 2, 3])` (for example) returns `numpy.int32`/`numpy.int64` and those types are not included in the `VALID_HPARAMS = (str, bool, int, float, list)` tuple (python/ray/tune/logger.py).
Since TensorBoard has no issues with logging `numpy.int32/64`, one simple solution would be to just include those types in the tuple above. Happy to provide a PR if you think this is the way to go.
*Ray version and other system information (Python version, TensorFlow version, OS):*
ray: 0.8.6
python: 3.7.7
tensorboard: 2.2.2
ubuntu: 20.04
### Reproduction (REQUIRED)
```python
from ray import tune
def trainable(config):
tune.report(score=config["a"])
config_dict = {"a": tune.choice([1, 2, 3])}
tune.run(trainable, config=config_dict, num_samples=1)
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| yes! that'd be great - could you push a PR and ping me? | 2020-07-03T17:40:47 |
ray-project/ray | 9,300 | ray-project__ray-9300 | [
"9166"
] | c0ba337fe06ba6cef3df23dea65f7b59c0291e15 | diff --git a/python/ray/resource_spec.py b/python/ray/resource_spec.py
--- a/python/ray/resource_spec.py
+++ b/python/ray/resource_spec.py
@@ -3,6 +3,8 @@
import logging
import multiprocessing
import os
+import subprocess
+import sys
import ray
import ray.ray_constants as ray_constants
@@ -229,12 +231,23 @@ def resolve(self, is_head, node_ip_address=None):
def _autodetect_num_gpus():
"""Attempt to detect the number of GPUs on this machine.
- TODO(rkn): This currently assumes Nvidia GPUs and Linux.
+ TODO(rkn): This currently assumes NVIDIA GPUs on Linux.
+ TODO(mehrdadn): This currently does not work on macOS.
+ TODO(mehrdadn): Use a better mechanism for Windows.
+
+ Possibly useful: tensorflow.config.list_physical_devices()
Returns:
The number of GPUs if any were detected, otherwise 0.
"""
- proc_gpus_path = "/proc/driver/nvidia/gpus"
- if os.path.isdir(proc_gpus_path):
- return len(os.listdir(proc_gpus_path))
- return 0
+ result = 0
+ if sys.platform.startswith("linux"):
+ proc_gpus_path = "/proc/driver/nvidia/gpus"
+ if os.path.isdir(proc_gpus_path):
+ result = len(os.listdir(proc_gpus_path))
+ elif sys.platform == "win32":
+ props = "AdapterCompatibility"
+ cmdargs = ["WMIC", "PATH", "Win32_VideoController", "GET", props]
+ lines = subprocess.check_output(cmdargs).splitlines()[1:]
+ result = len([l.rstrip() for l in lines if l.startswith(b"NVIDIA")])
+ return result
| [tune][rllib] Windows: GPU not recognized
### What is the problem?
I'm getting `ray.tune.error.TuneError: Insufficient cluster resources to launch trial`.
I specified a GPU in my config but ray does not recognize my GPU (RTX 2080) and throws an error.
I can get passed this by setting `num_gpus: 0` in my config for now.
https://gist.github.com/juliusfrost/fa7ebbb8d1dfc66eea0bbc4babcbe5aa
### Reproduction (REQUIRED)
```
git clone https://github.com/juliusfrost/rllib-tune-atari.git
cd rllib-tune-atari
pip install -r requirements.txt
python train.py --algo a2c
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| #9114 @mehrdadn
@mehrdadn Did you get a chance to look at this? I updated the script to reproduce to remove external dependencies. | 2020-07-04T00:48:38 |
|
ray-project/ray | 9,381 | ray-project__ray-9381 | [
"9364"
] | b97b474ae9ad360fff363291476d33cde7bee7a4 | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -65,6 +65,13 @@ def get_best_config(self, metric, mode="max"):
mode (str): One of [min, max].
"""
rows = self._retrieve_rows(metric=metric, mode=mode)
+ if not rows:
+ # only nans encountered when retrieving rows
+ logger.warning("Not able to retrieve the best config for {} "
+ "according to the specified metric "
+ "(only nans encountered).".format(
+ self._experiment_dir))
+ return None
all_configs = self.get_all_configs()
compare_op = max if mode == "max" else min
best_path = compare_op(rows, key=lambda k: rows[k][metric])
@@ -77,11 +84,20 @@ def get_best_logdir(self, metric, mode="max"):
metric (str): Key for trial info to order on.
mode (str): One of [min, max].
"""
+ assert mode in ["max", "min"]
df = self.dataframe(metric=metric, mode=mode)
- if mode == "max":
- return df.iloc[df[metric].idxmax()].logdir
- elif mode == "min":
- return df.iloc[df[metric].idxmin()].logdir
+ mode_idx = pd.Series.idxmax if mode == "max" else pd.Series.idxmin
+ try:
+ return df.iloc[mode_idx(df[metric])].logdir
+ except KeyError:
+ # all dirs contains only nan values
+ # for the specified metric
+ # -> df is an empty dataframe
+ logger.warning("Not able to retrieve the best logdir for {} "
+ "according to the specified metric "
+ "(only nans encountered).".format(
+ self._experiment_dir))
+ return None
def fetch_trial_dataframes(self):
fail_count = 0
@@ -161,7 +177,13 @@ def _retrieve_rows(self, metric=None, mode=None):
idx = df[metric].idxmin()
else:
idx = -1
- rows[path] = df.iloc[idx].to_dict()
+ try:
+ rows[path] = df.iloc[idx].to_dict()
+ except TypeError:
+ # idx is nan
+ logger.warning(
+ "Warning: Non-numerical value(s) encountered for {}".
+ format(path))
return rows
| diff --git a/python/ray/tune/tests/test_experiment_analysis.py b/python/ray/tune/tests/test_experiment_analysis.py
--- a/python/ray/tune/tests/test_experiment_analysis.py
+++ b/python/ray/tune/tests/test_experiment_analysis.py
@@ -4,6 +4,7 @@
import random
import os
import pandas as pd
+from numpy import nan
import ray
from ray.tune import run, sample_from
@@ -38,6 +39,21 @@ def run_test_exp(self):
"height": sample_from(lambda spec: int(100 * random.random())),
})
+ def nan_test_exp(self):
+ nan_ea = run(
+ lambda x: nan,
+ name="testing_nan",
+ local_dir=self.test_dir,
+ stop={"training_iteration": 1},
+ checkpoint_freq=1,
+ num_samples=self.num_samples,
+ config={
+ "width": sample_from(
+ lambda spec: 10 + int(90 * random.random())),
+ "height": sample_from(lambda spec: int(100 * random.random())),
+ })
+ return nan_ea
+
def testDataframe(self):
df = self.ea.dataframe()
@@ -58,11 +74,15 @@ def testTrialDataframe(self):
def testBestConfig(self):
best_config = self.ea.get_best_config(self.metric)
-
self.assertTrue(isinstance(best_config, dict))
self.assertTrue("width" in best_config)
self.assertTrue("height" in best_config)
+ def testBestConfigNan(self):
+ nan_ea = self.nan_test_exp()
+ best_config = nan_ea.get_best_config(self.metric)
+ self.assertIsNone(best_config)
+
def testBestLogdir(self):
logdir = self.ea.get_best_logdir(self.metric)
self.assertTrue(logdir.startswith(self.test_path))
@@ -70,6 +90,11 @@ def testBestLogdir(self):
self.assertTrue(logdir2.startswith(self.test_path))
self.assertNotEquals(logdir, logdir2)
+ def testBestLogdirNan(self):
+ nan_ea = self.nan_test_exp()
+ logdir = nan_ea.get_best_logdir(self.metric)
+ self.assertIsNone(logdir)
+
def testGetTrialCheckpointsPathsByTrial(self):
best_trial = self.ea.get_best_trial(self.metric)
checkpoints_metrics = self.ea.get_trial_checkpoints_paths(best_trial)
| [tune] Handling exceptions for the analysis module when encountering nans
Hi, after running some experiments with tune I was trying to get the best configuration/log dir using `get_best_config` and `get_best_logdir` from the analysis module. Unfortunately, I had some `nan` values in the metric I was measuring, and both methods returned an error related to Pandas. After checking the code, I found that the line causing the error is in the function `_retrieve_rows`:
https://github.com/ray-project/ray/blob/4da0e542d54c7977aae5bda5242cab15ab3f344b/python/ray/tune/analysis/experiment_analysis.py#L164
In particular, if there are `nan` values then the variable `idx` will be equal to `nan` and that will cause the following: `TypeError: Cannot index by location index with a non-integer key`.
I was wondering if you could add an exception for this case, warning the user that the experiment returned some `nan` values. I had to spend some time understanding what was wrong.
| Thank you for opening this issue! Sorry about this - I think we're not handling nans correctly throughout the system.
We'll get to this next week, and I'll ping you once we have a patch merged (or feel free to push a PR too).
Thanks! | 2020-07-09T15:04:41 |
ray-project/ray | 9,386 | ray-project__ray-9386 | [
"9366"
] | bed1be611e075a1e676c0bc66e26824e728108b2 | diff --git a/rllib/agents/dqn/dqn_torch_model.py b/rllib/agents/dqn/dqn_torch_model.py
--- a/rllib/agents/dqn/dqn_torch_model.py
+++ b/rllib/agents/dqn/dqn_torch_model.py
@@ -53,46 +53,45 @@ def __init__(
self.dueling = dueling
ins = num_outputs
- # Dueling case: Build the shared (advantages and value) fc-network.
advantage_module = nn.Sequential()
- value_module = None
- if self.dueling:
- value_module = nn.Sequential()
- for i, n in enumerate(q_hiddens):
- advantage_module.add_module("dueling_A_{}".format(i),
- nn.Linear(ins, n))
- value_module.add_module("dueling_V_{}".format(i),
+ value_module = nn.Sequential()
+
+ # Dueling case: Build the shared (advantages and value) fc-network.
+ for i, n in enumerate(q_hiddens):
+ advantage_module.add_module("dueling_A_{}".format(i),
nn.Linear(ins, n))
- # Add activations if necessary.
- if dueling_activation == "relu":
- advantage_module.add_module("dueling_A_act_{}".format(i),
- nn.ReLU())
- value_module.add_module("dueling_V_act_{}".format(i),
+ value_module.add_module("dueling_V_{}".format(i),
+ nn.Linear(ins, n))
+ # Add activations if necessary.
+ if dueling_activation == "relu":
+ advantage_module.add_module("dueling_A_act_{}".format(i),
nn.ReLU())
- elif dueling_activation == "tanh":
- advantage_module.add_module("dueling_A_act_{}".format(i),
- nn.Tanh())
- value_module.add_module("dueling_V_act_{}".format(i),
+ value_module.add_module("dueling_V_act_{}".format(i),
+ nn.ReLU())
+ elif dueling_activation == "tanh":
+ advantage_module.add_module("dueling_A_act_{}".format(i),
nn.Tanh())
+ value_module.add_module("dueling_V_act_{}".format(i),
+ nn.Tanh())
- # Add LayerNorm after each Dense.
- if add_layer_norm:
- advantage_module.add_module("LayerNorm_A_{}".format(i),
- nn.LayerNorm(n))
- value_module.add_module("LayerNorm_V_{}".format(i),
+ # Add LayerNorm after each Dense.
+ if add_layer_norm:
+ advantage_module.add_module("LayerNorm_A_{}".format(i),
nn.LayerNorm(n))
- ins = n
- # Actual Advantages layer (nodes=num-actions) and
- # value layer (nodes=1).
+ value_module.add_module("LayerNorm_V_{}".format(i),
+ nn.LayerNorm(n))
+ ins = n
+
+ # Actual Advantages layer (nodes=num-actions).
+ if q_hiddens:
advantage_module.add_module("A", nn.Linear(ins, action_space.n))
- value_module.add_module("V", nn.Linear(ins, 1))
- # Non-dueling:
- # Q-value layer (use main module's outputs as Q-values).
- else:
- pass
self.advantage_module = advantage_module
- self.value_module = value_module
+
+ # Value layer (nodes=1).
+ if self.dueling:
+ value_module.add_module("V", nn.Linear(ins, 1))
+ self.value_module = value_module
def get_advantages_or_q_values(self, model_out):
"""Returns distributional values for Q(s, a) given a state embedding.
diff --git a/rllib/examples/parametric_actions_cartpole.py b/rllib/examples/parametric_actions_cartpole.py
--- a/rllib/examples/parametric_actions_cartpole.py
+++ b/rllib/examples/parametric_actions_cartpole.py
@@ -48,7 +48,7 @@
# TODO(ekl) we need to set these to prevent the masked values
# from being further processed in DistributionalQModel, which
# would mess up the masking. It is possible to support these if we
- # defined a a custom DistributionalQModel that is aware of masking.
+ # defined a custom DistributionalQModel that is aware of masking.
"hiddens": [],
"dueling": False,
}
| [rllib] incorrect model output for DQN with torch and dueling=false
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
The output fo the DQN model is not within the action space.
Something is wrong when constructing the torch model when dueling is off. The output dimension of the model is equal to whatever is passed in "fcnet_hiddens" instead of being of the size of the action space.
*Ray version and other system information (Python version, TensorFlow version, OS):*
- ray==0.9.0.dev0
- python 3.6.10
- mac OS
### Reproduction (REQUIRED)
```python
import ray
from ray import tune
ray.init()
config = {
"env": "CartPole-v1",
"num_workers": 1,
"train_batch_size": 128,
"learning_starts": 128,
"model": {"fcnet_hiddens": [32]},
"dueling": False ,
"framework": "torch"
}
tune.run("DQN", name="MWE", config=config, stop={"training_iteration": 100})
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-07-09T22:12:56 |
||
ray-project/ray | 9,429 | ray-project__ray-9429 | [
"9402"
] | e6225bdfa1d534fd4d70a61b70a3197425e79d1f | diff --git a/rllib/examples/custom_loss.py b/rllib/examples/custom_loss.py
--- a/rllib/examples/custom_loss.py
+++ b/rllib/examples/custom_loss.py
@@ -31,7 +31,7 @@
type=str,
default=os.path.join(
os.path.dirname(os.path.abspath(__file__)),
- "../tests/data/cartpole_small"))
+ "../tests/data/cartpole/small"))
if __name__ == "__main__":
ray.init()
| diff --git a/rllib/agents/marwil/tests/test_marwil.py b/rllib/agents/marwil/tests/test_marwil.py
--- a/rllib/agents/marwil/tests/test_marwil.py
+++ b/rllib/agents/marwil/tests/test_marwil.py
@@ -1,3 +1,5 @@
+import os
+from pathlib import Path
import unittest
import ray
@@ -18,19 +20,40 @@ def setUpClass(cls):
def tearDownClass(cls):
ray.shutdown()
- def test_marwil_compilation(self):
- """Test whether a MARWILTrainer can be built with all frameworks."""
+ def test_marwil_compilation_and_learning_from_offline_file(self):
+ """Test whether a MARWILTrainer can be built with all frameworks.
+
+ And learns from a historic-data file.
+ """
+ rllib_dir = Path(__file__).parent.parent.parent.parent
+ print("rllib dir={}".format(rllib_dir))
+ data_file = os.path.join(rllib_dir, "tests/data/cartpole/large.json")
+ print("data_file={} exists={}".format(
+ data_file, os.path.isfile(data_file)))
+
config = marwil.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
- num_iterations = 2
+ config["evaluation_num_workers"] = 1
+ config["evaluation_interval"] = 1
+ config["evaluation_config"] = {"input": "sampler"}
+ config["input"] = [data_file]
+ num_iterations = 300
# Test for all frameworks.
for _ in framework_iterator(config):
trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0")
for i in range(num_iterations):
- trainer.train()
+ eval_results = trainer.train()["evaluation"]
+ print("iter={} R={}".format(
+ i, eval_results["episode_reward_mean"]))
+ # Learn until some reward is reached on an actual live env.
+ if eval_results["episode_reward_mean"] > 60.0:
+ print("learnt!")
+ break
+
check_compute_single_action(
trainer, include_prev_action_reward=True)
+
trainer.stop()
diff --git a/rllib/tests/data/cartpole/large.json b/rllib/tests/data/cartpole/large.json
new file mode 100644
--- /dev/null
+++ b/rllib/tests/data/cartpole/large.json
@@ -0,0 +1,21 @@
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diff --git a/rllib/tests/data/cartpole_small/output-2019-02-03_20-27-20_worker-0_0.json b/rllib/tests/data/cartpole/small.json
similarity index 100%
rename from rllib/tests/data/cartpole_small/output-2019-02-03_20-27-20_worker-0_0.json
rename to rllib/tests/data/cartpole/small.json
| [rllib] MARWIL tuned cartpole example (and my own experiments) produce nan rewards only.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem? + Reproduction
I have a custom example that produces offline data and picks it up with MARWIL for training. I observed that I get `nan` reward values for my example every time, so I went a step back and used your cartpole example:
https://github.com/ray-project/ray/blob/cd5a207d69cdaf05b47d956c18e89d928585eec7/rllib/tuned_examples/marwil/cartpole-marwil.yaml
I'm following the exact steps there, i.e. first run
```
./train.py --run=PPO --env=CartPole-v0 \
--stop='{"timesteps_total": 50000}' \
--config='{"output": "/tmp/out", "batch_mode": "complete_episodes"}'
```
followed by
```
rllib train -f cartpole-marwil.yaml
```
I did this both on my currently preferred stable version `0.8.5`, as well as on the `0.9.0.dev0` wheel. The result is this:
```
== Status ==
Memory usage on this node: 19.4/32.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/12 CPUs, 0/0 GPUs, 0.0/9.96 GiB heap, 0.0/3.42 GiB objects
Result logdir: /Users/maxpumperla/ray_results/cartpole-marwil
Number of trials: 2 (2 TERMINATED)
+--------------------------------+------------+-------+--------+--------+------------------+--------+----------+
| Trial name | status | loc | beta | iter | total time (s) | ts | reward |
|--------------------------------+------------+-------+--------+--------+------------------+--------+----------|
| MARWIL_CartPole-v0_7af06_00000 | TERMINATED | | 0 | 2206 | 58.5661 | 500007 | nan |
| MARWIL_CartPole-v0_7af06_00001 | TERMINATED | | 1 | 2248 | 58.6117 | 500286 | nan |
+--------------------------------+------------+-------+--------+--------+------------------+--------+----------+
```
Also, I've noticed that your MARWIL unit test is a pure smoke test and doesn't check reward values, but I didn't run that locally. Maybe it produces nan values as well.
In any case I'd appreciate any input here, as we'd love to use MARWIL for our "real" use case, in which we see the same behaviour.
| Hi @maxpumperla , thanks for filing this. Yes, we don't actually have an actual MARWIL learning test, that's true. I'll take a look. I remember that the NaNs are b/c of the offline learning setup, but I'll confirm that this is a non-issue and get back to you. Either way, we shouldn't be displaying NaN and instead evaluate on a live env and report actual rewards.
Ok, confirmed. All NaNs. But makes sense, if input is a file, then there are no samplers, hence no actual env. We should probably have sampling for the evaluation workers, though to indicate actual learning progress. ...
Got it: Alter the rllib/agents/marwil/tests/test_marwil.py file as follows:
```
def test_marwil_compilation(self):
"""Test whether a MARWILTrainer can be built with all frameworks."""
config = marwil.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["evaluation_num_workers"] = 1
config["evaluation_interval"] = 1 # <- this is switched off in MARWIL's default config (probably shouldn't be)
config["evaluation_config"] = {"input": "sampler"} # <- make sure evaluation workers have an actual Env
config["input"] = ["[your file with pre-recorded data]"]
num_iterations = 2000
# Test for all frameworks.
for _ in framework_iterator(config, frameworks="torch"):
trainer = marwil.MARWILTrainer(config=config, env="CartPole-v0")
for i in range(num_iterations):
print(trainer.train()) # <- in the returned dict, look for the "evaluation" key, which holds a sub-dict with the correct rewards from the evaluation workers' envs in it.
trainer.stop()
```
@maxpumperla
Will discuss internally, whether we should change the default settings of MARWIL. Also, I'm wondering, why tune would not print out the evaluation results (it prints out the "normal" workers' results, hence NaN).
I'm closing this issue. Feel free to re-open in case the above solution does not work on your end.
Again, we'll make sure this becomes more user-friendly in a next version. | 2020-07-12T14:34:01 |
ray-project/ray | 9,461 | ray-project__ray-9461 | [
"8821"
] | 222635b63ff2a62c14bd3611f8c79ffba38e390c | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -201,6 +201,11 @@ class ExperimentAnalysis(Analysis):
"""
def __init__(self, experiment_checkpoint_path, trials=None):
+ experiment_checkpoint_path = os.path.expanduser(
+ experiment_checkpoint_path)
+ if not os.path.isfile(experiment_checkpoint_path):
+ raise ValueError(
+ "{} is not a valid file.".format(experiment_checkpoint_path))
with open(experiment_checkpoint_path) as f:
_experiment_state = json.load(f)
self._experiment_state = _experiment_state
| diff --git a/python/ray/tune/tests/test_experiment_analysis_mem.py b/python/ray/tune/tests/test_experiment_analysis_mem.py
--- a/python/ray/tune/tests/test_experiment_analysis_mem.py
+++ b/python/ray/tune/tests/test_experiment_analysis_mem.py
@@ -1,12 +1,16 @@
+import json
import unittest
import shutil
import tempfile
+import os
import random
import pandas as pd
+import pytest
import numpy as np
import ray
-from ray.tune import run, Trainable, sample_from, Analysis, grid_search
+from ray.tune import (run, Trainable, sample_from, Analysis,
+ ExperimentAnalysis, grid_search)
from ray.tune.examples.async_hyperband_example import MyTrainableClass
@@ -37,14 +41,36 @@ def load_checkpoint(self, checkpoint_path):
pass
self.MockTrainable = MockTrainable
+ self.test_dir = tempfile.mkdtemp()
ray.init(local_mode=False, num_cpus=1)
def tearDown(self):
shutil.rmtree(self.test_dir, ignore_errors=True)
ray.shutdown()
+ def testInit(self):
+ experiment_checkpoint_path = os.path.join(self.test_dir,
+ "experiment_state.json")
+ checkpoint_data = {
+ "checkpoints": [{
+ "trainable_name": "MockTrainable",
+ "logdir": "/mock/test/MockTrainable_0_id=3_2020-07-12"
+ }]
+ }
+
+ with open(experiment_checkpoint_path, "w") as f:
+ f.write(json.dumps(checkpoint_data))
+
+ experiment_analysis = ExperimentAnalysis(experiment_checkpoint_path)
+ self.assertEqual(len(experiment_analysis._checkpoints), 1)
+ self.assertTrue(experiment_analysis.trials is None)
+
+ def testInitException(self):
+ experiment_checkpoint_path = os.path.join(self.test_dir, "mock.json")
+ with pytest.raises(ValueError):
+ ExperimentAnalysis(experiment_checkpoint_path)
+
def testCompareTrials(self):
- self.test_dir = tempfile.mkdtemp()
scores = np.asarray(list(self.MockTrainable.scores_dict.values()))
scores_all = scores.flatten("F")
scores_last = scores_all[5:]
@@ -126,7 +152,7 @@ def testDataframe(self):
analysis = Analysis(self.test_dir)
df = analysis.dataframe()
self.assertTrue(isinstance(df, pd.DataFrame))
- self.assertEquals(df.shape[0], self.num_samples * 2)
+ self.assertEqual(df.shape[0], self.num_samples * 2)
def testBestLogdir(self):
analysis = Analysis(self.test_dir)
@@ -134,17 +160,16 @@ def testBestLogdir(self):
self.assertTrue(logdir.startswith(self.test_dir))
logdir2 = analysis.get_best_logdir(self.metric, mode="min")
self.assertTrue(logdir2.startswith(self.test_dir))
- self.assertNotEquals(logdir, logdir2)
+ self.assertNotEqual(logdir, logdir2)
def testBestConfigIsLogdir(self):
analysis = Analysis(self.test_dir)
for metric, mode in [(self.metric, "min"), (self.metric, "max")]:
logdir = analysis.get_best_logdir(metric, mode=mode)
best_config = analysis.get_best_config(metric, mode=mode)
- self.assertEquals(analysis.get_all_configs()[logdir], best_config)
+ self.assertEqual(analysis.get_all_configs()[logdir], best_config)
if __name__ == "__main__":
- import pytest
import sys
sys.exit(pytest.main(["-v", __file__]))
| [tune] ExperimentAnalysis doesn't expand user as docs imply
### What is the problem?
The Ray docs give an example where a path with the "~" operator is included:
https://docs.ray.io/en/master/tune/api_docs/analysis.html#experimentanalysis-tune-experimentanalysis
```
>>> tune.run(my_trainable, name="my_exp", local_dir="~/tune_results")
>>> analysis = ExperimentAnalysis(
>>> experiment_checkpoint_path="~/tune_results/my_exp/state.json")
```
This throws an error for me:
```
from ray.tune import ExperimentAnalysis
analysis = ExperimentAnalysis("~/rayexp/experiment_state-2020-06-06_20-22-19.json")
203 def __init__(self, experiment_checkpoint_path, trials=None):
--> 204 with open(experiment_checkpoint_path) as f:
205 _experiment_state = json.load(f)
206 self._experiment_state = _experiment_state
FileNotFoundError: [Errno 2] No such file or directory: '~/rayexp/experiment_state-2020-06-06_20-22-19.json'
```
Whereas the corresponding expanded version does not:
```
from ray.tune import ExperimentAnalysis
from os.path import expanduser
analysis = ExperimentAnalysis(expanduser("~/rayexp/experiment_state-2020-06-06_20-22-19.json"))
```
*Ray version and other system information (Python version, TensorFlow version, OS):*
Ray 0.8.5
Python 3.8
Ubuntu 18
### Reproduction (REQUIRED)
See description above.
| 2020-07-14T02:04:02 |
|
ray-project/ray | 9,479 | ray-project__ray-9479 | [
"9441"
] | ca4f3b79db968cd8ad31efb1170b8ba2c7bc38ba | diff --git a/python/ray/serve/request_params.py b/python/ray/serve/request_params.py
--- a/python/ray/serve/request_params.py
+++ b/python/ray/serve/request_params.py
@@ -42,7 +42,7 @@ def adjust_relative_slo_ms(self) -> float:
return current_time_ms + slo_ms
def ray_serialize(self):
- return pickle.dumps(self.__dict__, protocol=5)
+ return pickle.dumps(self.__dict__)
@staticmethod
def ray_deserialize(value):
diff --git a/python/ray/serve/router.py b/python/ray/serve/router.py
--- a/python/ray/serve/router.py
+++ b/python/ray/serve/router.py
@@ -3,10 +3,10 @@
from collections import defaultdict, deque
import time
from typing import DefaultDict, List
+import pickle
import blist
-import ray.cloudpickle as pickle
from ray.exceptions import RayTaskError
import ray
@@ -50,7 +50,7 @@ def ray_serialize(self):
# worker without removing async_future.
clone = copy.copy(self).__dict__
clone.pop("async_future")
- return pickle.dumps(clone, protocol=5)
+ return pickle.dumps(clone)
@staticmethod
def ray_deserialize(value):
| diff --git a/python/ray/serve/tests/test_regression.py b/python/ray/serve/tests/test_regression.py
new file mode 100644
--- /dev/null
+++ b/python/ray/serve/tests/test_regression.py
@@ -0,0 +1,38 @@
+import numpy as np
+import requests
+
+from ray import serve
+
+
+def test_np_in_composed_model(serve_instance):
+ # https://github.com/ray-project/ray/issues/9441
+ # AttributeError: 'bytes' object has no attribute 'readonly'
+ # in cloudpickle _from_numpy_buffer
+
+ def sum_model(_request, data=None):
+ return np.sum(data)
+
+ class ComposedModel:
+ def __init__(self):
+ self.model = serve.get_handle("sum_model")
+
+ async def __call__(self, _request):
+ data = np.ones((10, 10))
+ result = await self.model.remote(data=data)
+ return result
+
+ serve.create_backend("sum_model", sum_model)
+ serve.create_endpoint("sum_model", backend="sum_model")
+ serve.create_backend("model", ComposedModel)
+ serve.create_endpoint(
+ "model", backend="model", route="/model", methods=['GET'])
+
+ result = requests.get("http://127.0.0.1:8000/model")
+ assert result.status_code == 200
+ assert result.json() == 100.0
+
+
+if __name__ == "__main__":
+ import sys
+ import pytest
+ sys.exit(pytest.main(["-v", "-s", __file__]))
| [serve] Composed models with numpy arguments
### What is the problem?
I tried modifying the [composing multiple models](https://docs.ray.io/en/master/serve/advanced.html?highlight=pipeline#composing-multiple-models) examples to pass a numpy array to another endpoint. However, I get the following error:
```
2020-07-13 22:21:37,329 ERROR worker.py:987 -- Possible unhandled error from worker: ray::RayServeWorker_sum_model.handle_request() (pid=22037, ip=192.168.1.31)
File "python/ray/_raylet.pyx", line 410, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 424, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 1212, in ray._raylet.CoreWorker.run_async_func_in_event_loop
File "/home/users/daniel/.local/conda/lib/python3.7/concurrent/futures/_base.py", line 428, in result
return self.__get_result()
File "/home/users/daniel/.local/conda/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
File "python/ray/_raylet.pyx", line 422, in deserialize_args
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/serialization.py", line 312, in deserialize_objects
self._deserialize_object(data, metadata, object_id))
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/serialization.py", line 252, in _deserialize_object
return self._deserialize_msgpack_data(data, metadata)
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/serialization.py", line 233, in _deserialize_msgpack_data
python_objects = self._deserialize_pickle5_data(pickle5_data)
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/serialization.py", line 221, in _deserialize_pickle5_data
obj = pickle.loads(in_band)
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/serve/router.py", line 53, in ray_deserialize
kwargs = pickle.loads(value)
File "/home/users/daniel/.cache/pypoetry/virtualenvs/ray-test-mjIDN_hR-py3.7/lib/python3.7/site-packages/ray/cloudpickle/cloudpickle_fast.py", line 448, in _numpy_frombuffer
array.setflags(write=isinstance(buffer, bytearray) or not buffer.readonly)
AttributeError: 'bytes' object has no attribute 'readonly'
```
Ray version: 0.8.6
Python version: 3.7.7
Numpy version: 1.19.0
### Reproduction (REQUIRED)
```
import time
from ray import serve
import requests
import numpy as np
serve.init()
def sum_model(_request, data=None):
return np.sum(data)
class ComposedModel:
def __init__(self):
self.model = serve.get_handle("sum_model")
async def __call__(self, _request):
data = np.ones((10, 10))
result = await self.model.remote(data=data)
return result
serve.create_backend("sum_model", sum_model)
serve.create_endpoint("sum_model", backend="sum_model")
serve.create_backend("model", ComposedModel)
serve.create_endpoint("model", backend="model", route="/model", methods=['GET'])
while True:
result = requests.get('http://127.0.0.1:8000/model')
print(result)
time.sleep(1)
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-07-14T22:31:01 |
|
ray-project/ray | 9,516 | ray-project__ray-9516 | [
"9218"
] | 2f674728a683ec7d466917afc6327308f2483ec1 | diff --git a/rllib/evaluation/rollout_worker.py b/rllib/evaluation/rollout_worker.py
--- a/rllib/evaluation/rollout_worker.py
+++ b/rllib/evaluation/rollout_worker.py
@@ -9,9 +9,6 @@
TYPE_CHECKING, TypeVar
import ray
-from ray.util.debug import log_once, disable_log_once_globally, \
- enable_periodic_logging
-from ray.util.iter import ParallelIteratorWorker
from ray.rllib.env.atari_wrappers import wrap_deepmind, is_atari
from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
@@ -21,17 +18,17 @@
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.evaluation.sampler import AsyncSampler, SyncSampler
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
-from ray.rllib.policy.sample_batch import MultiAgentBatch, DEFAULT_POLICY_ID
-from ray.rllib.policy.policy import Policy
-from ray.rllib.policy.tf_policy import TFPolicy
-from ray.rllib.policy.torch_policy import TorchPolicy
+from ray.rllib.models import ModelCatalog
+from ray.rllib.models.preprocessors import NoPreprocessor, Preprocessor
from ray.rllib.offline import NoopOutput, IOContext, OutputWriter, InputReader
from ray.rllib.offline.off_policy_estimator import OffPolicyEstimator, \
OffPolicyEstimate
from ray.rllib.offline.is_estimator import ImportanceSamplingEstimator
from ray.rllib.offline.wis_estimator import WeightedImportanceSamplingEstimator
-from ray.rllib.models import ModelCatalog
-from ray.rllib.models.preprocessors import NoPreprocessor, Preprocessor
+from ray.rllib.policy.sample_batch import MultiAgentBatch, DEFAULT_POLICY_ID
+from ray.rllib.policy.policy import Policy
+from ray.rllib.policy.tf_policy import TFPolicy
+from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.debug import summarize
@@ -42,6 +39,9 @@
from ray.rllib.utils.types import EnvType, AgentID, PolicyID, EnvConfigDict, \
ModelConfigDict, TrainerConfigDict, SampleBatchType, ModelWeights, \
ModelGradients, MultiAgentPolicyConfigDict
+from ray.util.debug import log_once, disable_log_once_globally, \
+ enable_periodic_logging
+from ray.util.iter import ParallelIteratorWorker
if TYPE_CHECKING:
from ray.rllib.agents.callbacks import DefaultCallbacks
@@ -399,22 +399,30 @@ def make_env(vector_index):
tf1.set_random_seed(seed)
self.policy_map, self.preprocessors = \
self._build_policy_map(policy_dict, policy_config)
- if (ray.is_initialized()
- and ray.worker._mode() != ray.worker.LOCAL_MODE):
- if not ray.get_gpu_ids():
- logger.debug(
- "Creating policy evaluation worker {}".format(
- worker_index) +
- " on CPU (please ignore any CUDA init errors)")
- elif not tf1.test.is_gpu_available():
- raise RuntimeError(
- "GPUs were assigned to this worker by Ray, but "
- "TensorFlow reports GPU acceleration is disabled. "
- "This could be due to a bad CUDA or TF installation.")
else:
self.policy_map, self.preprocessors = self._build_policy_map(
policy_dict, policy_config)
+ if (ray.is_initialized() and
+ ray.worker._mode() != ray.worker.LOCAL_MODE):
+ # Check available number of GPUs
+ if not ray.get_gpu_ids():
+ logger.debug(
+ "Creating policy evaluation worker {}".format(
+ worker_index) +
+ " on CPU (please ignore any CUDA init errors)")
+ elif (policy_config["framework"] in ["tf2", "tf", "tfe"] and
+ not tf.config.list_physical_devices("GPU")) or \
+ (policy_config["framework"] == "torch" and
+ not torch.cuda.is_available()):
+ raise RuntimeError(
+ "GPUs were assigned to this worker by Ray, but "
+ "your DL framework ({}) reports GPU acceleration is "
+ "disabled. This could be due to a bad CUDA- or {} "
+ "installation.".format(
+ policy_config["framework"],
+ policy_config["framework"]))
+
self.multiagent: bool = set(
self.policy_map.keys()) != {DEFAULT_POLICY_ID}
if self.multiagent:
diff --git a/rllib/policy/torch_policy.py b/rllib/policy/torch_policy.py
--- a/rllib/policy/torch_policy.py
+++ b/rllib/policy/torch_policy.py
@@ -4,6 +4,7 @@
import time
from typing import Callable, Dict, List, Optional, Tuple, Union
+import ray
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
@@ -98,8 +99,10 @@ def __init__(self,
"""
self.framework = "torch"
super().__init__(observation_space, action_space, config)
- self.device = (torch.device("cuda")
- if torch.cuda.is_available() else torch.device("cpu"))
+ if torch.cuda.is_available() and ray.get_gpu_ids():
+ self.device = torch.device("cuda")
+ else:
+ self.device = torch.device("cpu")
self.model = model.to(self.device)
self.exploration = self._create_exploration()
self.unwrapped_model = model # used to support DistributedDataParallel
| [rllib] num_gpus=0 on a device with GPU uses GPU for Pytorch
### What is the problem?
Ray will find a GPU and place the model (e.g. FCNet) on the GPU even when `num_gpus=0`.
Stack Trace:
```
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/trainer.py:520: in train
raise e
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/trainer.py:506: in train
result = Trainable.train(self)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/tune/trainable.py:260: in train
result = self._train()
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py:139: in _train
return self._train_exec_impl()
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/trainer_template.py:177: in _train_exec_impl
res = next(self.train_exec_impl)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/util/iter.py:731: in __next__
return next(self.built_iterator)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/util/iter.py:752: in apply_foreach
result = fn(item)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/maml/maml.py:143: in __call__
fetches = self.workers.local_worker().learn_on_batch(samples)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/evaluation/rollout_worker.py:742: in learn_on_batch
.learn_on_batch(samples)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/policy/torch_policy.py:244: in learn_on_batch
self._loss(self, self.model, self.dist_class, train_batch))
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/agents/maml/maml_torch_policy.py:329: in maml_loss
logits, state = model.from_batch(train_batch)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/models/modelv2.py:224: in from_batch
return self.__call__(input_dict, states, train_batch.get("seq_lens"))
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/models/modelv2.py:181: in __call__
res = self.forward(restored, state or [], seq_lens)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/models/torch/fcnet.py:118: in forward
self._features = self._hidden_layers(self._last_flat_in)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/module.py:550: in __call__
result = self.forward(*input, **kwargs)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/container.py:100: in forward
input = module(input)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/module.py:550: in __call__
result = self.forward(*input, **kwargs)
../../miniconda3/envs/ray/lib/python3.7/site-packages/ray/rllib/models/torch/misc.py:110: in forward
return self._model(x)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/module.py:550: in __call__
result = self.forward(*input, **kwargs)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/container.py:100: in forward
input = module(input)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/module.py:550: in __call__
result = self.forward(*input, **kwargs)
../../miniconda3/envs/ray/lib/python3.7/site-packages/torch/nn/modules/linear.py:87: in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Expected object of device type cuda but got device type cpu for argument #2 'mat1' in call to _th_addmm
```
### Reproduction (REQUIRED)
Run this in `ray/rlllib`:
`pytest -v -s agents/maml/tests/test_maml.py` on a machine with a GPU.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| @michaelzhiluo Looking at this now ... | 2020-07-16T09:03:33 |
|
ray-project/ray | 9,517 | ray-project__ray-9517 | [
"9036"
] | 2f674728a683ec7d466917afc6327308f2483ec1 | diff --git a/python/ray/tune/checkpoint_manager.py b/python/ray/tune/checkpoint_manager.py
--- a/python/ray/tune/checkpoint_manager.py
+++ b/python/ray/tune/checkpoint_manager.py
@@ -109,6 +109,10 @@ def on_checkpoint(self, checkpoint):
return
old_checkpoint = self.newest_persistent_checkpoint
+
+ if old_checkpoint.value == checkpoint.value:
+ return
+
self.newest_persistent_checkpoint = checkpoint
# Remove the old checkpoint if it isn't one of the best ones.
| diff --git a/python/ray/tune/tests/test_trial_scheduler_pbt.py b/python/ray/tune/tests/test_trial_scheduler_pbt.py
new file mode 100644
--- /dev/null
+++ b/python/ray/tune/tests/test_trial_scheduler_pbt.py
@@ -0,0 +1,90 @@
+import numpy as np
+import os
+import pickle
+import random
+import unittest
+import sys
+
+from ray import tune
+from ray.tune.schedulers import PopulationBasedTraining
+
+
+class MockTrainable(tune.Trainable):
+ def setup(self, config):
+ self.iter = 0
+ self.a = config["a"]
+ self.b = config["b"]
+ self.c = config["c"]
+
+ def step(self):
+ self.iter += 1
+ return {"mean_accuracy": (self.a - self.iter) * self.b}
+
+ def save_checkpoint(self, tmp_checkpoint_dir):
+ checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.mock")
+ with open(checkpoint_path, "wb") as fp:
+ pickle.dump((self.a, self.b), fp)
+ return tmp_checkpoint_dir
+
+ def load_checkpoint(self, tmp_checkpoint_dir):
+ checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.mock")
+ with open(checkpoint_path, "rb") as fp:
+ self.a, self.b = pickle.load(fp)
+
+
+class MockParam(object):
+ def __init__(self, params):
+ self._params = params
+ self._index = 0
+
+ def __call__(self, *args, **kwargs):
+ val = self._params[self._index % len(self._params)]
+ self._index += 1
+ return val
+
+
+class PopulationBasedTrainingResumeTest(unittest.TestCase):
+ def testPermutationContinuation(self):
+ """
+ Tests continuation of runs after permutation.
+ Sometimes, runs were continued from deleted checkpoints.
+ This deterministic initialisation would fail when the
+ fix was not applied.
+ See issues #9036, #9036
+ """
+ scheduler = PopulationBasedTraining(
+ time_attr="training_iteration",
+ metric="mean_accuracy",
+ mode="max",
+ perturbation_interval=1,
+ log_config=True,
+ hyperparam_mutations={"c": lambda: 1})
+
+ param_a = MockParam([10, 20, 30, 40])
+ param_b = MockParam([1.2, 0.9, 1.1, 0.8])
+
+ random.seed(100)
+ np.random.seed(1000)
+ tune.run(
+ MockTrainable,
+ config={
+ "a": tune.sample_from(lambda _: param_a()),
+ "b": tune.sample_from(lambda _: param_b()),
+ "c": 1
+ },
+ fail_fast=True,
+ num_samples=20,
+ global_checkpoint_period=1,
+ checkpoint_freq=1,
+ checkpoint_at_end=True,
+ keep_checkpoints_num=1,
+ checkpoint_score_attr="min-training_iteration",
+ scheduler=scheduler,
+ name="testPermutationContinuation",
+ stop={"training_iteration": 5})
+
+
+if __name__ == "__main__":
+ import pytest
+
+ sys.exit(pytest.main(["-v", __file__]))
| [tune] Population-based training: broken when using keep_checkpoint_num
When using **population-based** training TUNE stops after some times throwing the following error:
`There are paused trials, but no more pending trials with sufficient resources.`
This is caused by not finding the latest checkpoint:
```
Failure # 1 (occurred at 2020-06-19_11-26-36)
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py", line 294, in start_trial
self._start_trial(trial, checkpoint, train=train)
File "/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py", line 235, in _start_trial
self.restore(trial, checkpoint)
File "/usr/local/lib/python3.6/dist-packages/ray/tune/ray_trial_executor.py", line 673, in restore
data_dict = TrainableUtil.pickle_checkpoint(value)
File "/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py", line 62, in pickle_checkpoint
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
File "/usr/local/lib/python3.6/dist-packages/ray/tune/trainable.py", line 87, in find_checkpoint_dir
raise FileNotFoundError("Path does not exist", checkpoint_path)
FileNotFoundError: [Errno Path does not exist] /content/TRASH_TUNE_PBT_oversampling_mimic_densenet121/TUNE_Model_0_2020-06-19_11-24-215xncry9c/checkpoint_6/
```
The error appears to be somewhat random since it only appears after quite some iterations
The error can be reproduced in this [colab notebook](https://colab.research.google.com/drive/1-o896bEUm7DTvS24Do0btlqbSHre49MH?usp=sharing). **It is not a COLAB related issue since the same problem arises on our own server.**
@richardliaw Is this related to #8772 ?
| Yeah this seems like an issue. We'll take a look into this.
This is the issue (just for reference):
```
from ray.tune.schedulers import PopulationBasedTraining
scheduler = PopulationBasedTraining(
time_attr='training_iteration',
metric='mean_accuracy',
mode='max',
perturbation_interval=1,
log_config=True,
hyperparam_mutations={
"lr": lambda: 10 ** np.random.uniform(-2, -5)
})
train_config = {'lr': 0.01}
analysis = tune.run(
TUNE_Model, # Reference to the model class.
config=train_config,
fail_fast=True, # Stop after first error
num_samples=4, # Num of different hyperparameter configurations
search_alg=None, # Search algotihm like Hyperopt, Nevergrad...
resources_per_trial={"gpu": 1}, # Requested resources per TRIAL. If set to fraction, total_GPUS/res_p_trial can be run in parallel
global_checkpoint_period=np.inf, # Do not save checkpoints based on time interval
checkpoint_freq = 1, # Save checkpoint every time the checkpoint_score_attr improves
checkpoint_at_end = True,
keep_checkpoints_num = 2, # Keep only the best checkpoint
checkpoint_score_attr = 'mean_accuracy', # Metric used to compare checkpoints
verbose=1,
scheduler=scheduler, # Stop bad trials early
name="TRASH_TUNE_PBT_oversampling_mimic_densenet121",
local_dir="/content",
stop={ # Stop a single trial if one of the conditions are met
"mean_accuracy": 0.85,
"training_iteration": 15})
```
@Cryptiex for a temporary workaround, I think you can just do:
```python
def _save(self, tmp_checkpoint_dir):
""" Saves the model checkpoint depending of checkpoint_freq and checkpoint_at_end set in tune.run.
Arguments:
tmp_checkpoint_dir {str} -- [Folder where the checkpoints get saved.]
Returns:
[str] -- [tmp_checkpoint_dir]
"""
checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.pth")
torch.save(self.model.state_dict(), checkpoint_path)
return checkpoint_path # CHANGE THIS
def _restore(self, checkpoint_path): # CHANGE THIS
""" Restore model.
"""
self.model.load_state_dict(torch.load(checkpoint_path))
I think the temporary fix actually is to comment this out:
```
keep_checkpoints_num = 2, # Keep only the best checkpoint
```
Thanks for the fast reply! Commenting out **keep_checkpoints_num** worked for me as a temporary fix. Any idea where this is coming from? Does the PBT scheduler reference an already deleted checkpoint?
Yeah, I think it's something about not properly bookkeeping the checkpoints on disk.
Any updates on this? The temporary fix seems not to work as indicated by #8441 and my own experiences | 2020-07-16T09:16:32 |
ray-project/ray | 9,521 | ray-project__ray-9521 | [
"9295"
] | ac39e23145336fa7d450dd170e3e12ffc3d72d83 | diff --git a/rllib/policy/sample_batch.py b/rllib/policy/sample_batch.py
--- a/rllib/policy/sample_batch.py
+++ b/rllib/policy/sample_batch.py
@@ -438,8 +438,8 @@ def timeslices(self, k: int) -> List["MultiAgentBatch"]:
steps = []
for policy_id, batch in self.policy_batches.items():
for row in batch.rows():
- steps.append((row[SampleBatch.EPS_ID], row["t"], policy_id,
- row))
+ steps.append((row[SampleBatch.EPS_ID], row["t"],
+ row["agent_index"], policy_id, row))
steps.sort()
finished_slices = []
@@ -458,7 +458,7 @@ def finish_slice():
# For each unique env timestep.
for _, group in itertools.groupby(steps, lambda x: x[:2]):
# Accumulate into the current slice.
- for _, _, policy_id, row in group:
+ for _, _, _, policy_id, row in group:
cur_slice[policy_id].add_values(**row)
cur_slice_size += 1
# Slice has reached target number of env steps.
| [RLlib] Mutiagent learning: can't combine replay lockstep and multiple agents controlled by the same policy.
Hello,
While implementing MADDPG for PyTorch I noticed that it is not possible to combine the lockstep replay mode configuration and having multiple agents controlled by the same policy. This is due to the implementation of MultiAgentBatch as a dict of `PolicyID -> SampleBatch`. In the contrib TensorFlow implementation this issue was circumvented using parameter sharing. Another solution which is framework agnostic would be to group the agents.
I think that this combination should be supported without needing to group the agents. However, I suspect that it would require significant code change. Maybe another solution exists?
Also, the documentation should be updated alongside with the code that checks that the config is valid.
Thanks
(Amazing work btw)
| Hmm, I think this should be ok. The batch will indeed be keyed by policy id, but you can look into the "agent_id" field of the batch to see which experiences correspond with which original agent.
Okk I will look into that next week and let you know. Thx | 2020-07-16T16:40:43 |
|
ray-project/ray | 9,525 | ray-project__ray-9525 | [
"9307"
] | 94fcd43593a7b7675ff857cbfd6d189965899a65 | diff --git a/python/ray/tune/progress_reporter.py b/python/ray/tune/progress_reporter.py
--- a/python/ray/tune/progress_reporter.py
+++ b/python/ray/tune/progress_reporter.py
@@ -5,7 +5,7 @@
from ray.tune.result import (EPISODE_REWARD_MEAN, MEAN_ACCURACY, MEAN_LOSS,
TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL)
-from ray.tune.utils import flatten_dict
+from ray.tune.utils import unflattened_lookup
try:
from collections.abc import Mapping
@@ -466,9 +466,9 @@ def _get_trial_info(trial, parameters, metrics):
parameters (list[str]): Names of trial parameters to include.
metrics (list[str]): Names of metrics to include.
"""
- result = flatten_dict(trial.last_result)
- config = flatten_dict(trial.config)
+ result = trial.last_result
+ config = trial.config
trial_info = [str(trial), trial.status, str(trial.location)]
- trial_info += [config.get(param) for param in parameters]
- trial_info += [result.get(metric) for metric in metrics]
+ trial_info += [unflattened_lookup(param, config) for param in parameters]
+ trial_info += [unflattened_lookup(metric, result) for metric in metrics]
return trial_info
diff --git a/python/ray/tune/utils/__init__.py b/python/ray/tune/utils/__init__.py
--- a/python/ray/tune/utils/__init__.py
+++ b/python/ray/tune/utils/__init__.py
@@ -1,6 +1,6 @@
from ray.tune.utils.util import deep_update, flatten_dict, get_pinned_object, \
- merge_dicts, pin_in_object_store, UtilMonitor, validate_save_restore, \
- warn_if_slow
+ merge_dicts, pin_in_object_store, unflattened_lookup, UtilMonitor, \
+ validate_save_restore, warn_if_slow
__all__ = [
"deep_update",
@@ -8,6 +8,7 @@
"get_pinned_object",
"merge_dicts",
"pin_in_object_store",
+ "unflattened_lookup",
"UtilMonitor",
"validate_save_restore",
"warn_if_slow",
diff --git a/python/ray/tune/utils/util.py b/python/ray/tune/utils/util.py
--- a/python/ray/tune/utils/util.py
+++ b/python/ray/tune/utils/util.py
@@ -2,7 +2,7 @@
import logging
import threading
import time
-from collections import defaultdict
+from collections import defaultdict, deque, Mapping, Sequence
from threading import Thread
import numpy as np
@@ -216,6 +216,27 @@ def flatten_dict(dt, delimiter="/"):
return dt
+def unflattened_lookup(flat_key, lookup, delimiter="/", default=None):
+ """
+ Unflatten `flat_key` and iteratively look up in `lookup`. E.g.
+ `flat_key="a/0/b"` will try to return `lookup["a"][0]["b"]`.
+ """
+ keys = deque(flat_key.split(delimiter))
+ base = lookup
+ while keys:
+ key = keys.popleft()
+ try:
+ if isinstance(base, Mapping):
+ base = base[key]
+ elif isinstance(base, Sequence):
+ base = base[int(key)]
+ else:
+ raise KeyError()
+ except KeyError:
+ return default
+ return base
+
+
def _to_pinnable(obj):
"""Converts obj to a form that can be pinned in object store memory.
| diff --git a/python/ray/tune/tests/test_progress_reporter.py b/python/ray/tune/tests/test_progress_reporter.py
--- a/python/ray/tune/tests/test_progress_reporter.py
+++ b/python/ray/tune/tests/test_progress_reporter.py
@@ -22,15 +22,15 @@
EXPECTED_RESULT_2 = """Result logdir: /foo
Number of trials: 5 (1 PENDING, 3 RUNNING, 1 TERMINATED)
-+--------------+------------+-------+-----+-----+
-| Trial name | status | loc | a | b |
-|--------------+------------+-------+-----+-----|
-| 00000 | TERMINATED | here | 0 | 0 |
-| 00001 | PENDING | here | 1 | 2 |
-| 00002 | RUNNING | here | 2 | 4 |
-| 00003 | RUNNING | here | 3 | 6 |
-| 00004 | RUNNING | here | 4 | 8 |
-+--------------+------------+-------+-----+-----+"""
++--------------+------------+-------+-----+-----+---------+---------+
+| Trial name | status | loc | a | b | n/k/0 | n/k/1 |
+|--------------+------------+-------+-----+-----+---------+---------|
+| 00000 | TERMINATED | here | 0 | 0 | 0 | 0 |
+| 00001 | PENDING | here | 1 | 2 | 1 | 2 |
+| 00002 | RUNNING | here | 2 | 4 | 2 | 4 |
+| 00003 | RUNNING | here | 3 | 6 | 3 | 6 |
+| 00004 | RUNNING | here | 4 | 8 | 4 | 8 |
++--------------+------------+-------+-----+-----+---------+---------+"""
EXPECTED_RESULT_3 = """Result logdir: /foo
Number of trials: 5 (1 PENDING, 3 RUNNING, 1 TERMINATED)
@@ -246,21 +246,29 @@ def testProgressStr(self):
t.trial_id = "%05d" % i
t.local_dir = "/foo"
t.location = "here"
- t.config = {"a": i, "b": i * 2}
- t.evaluated_params = t.config
+ t.config = {"a": i, "b": i * 2, "n": {"k": [i, 2 * i]}}
+ t.evaluated_params = {
+ "a": i,
+ "b": i * 2,
+ "n/k/0": i,
+ "n/k/1": 2 * i
+ }
t.last_result = {
"config": {
"a": i,
- "b": i * 2
+ "b": i * 2,
+ "n": {
+ "k": [i, 2 * i]
+ }
},
"metric_1": i / 2,
"metric_2": i / 4
}
t.__str__ = lambda self: self.trial_id
trials.append(t)
- # One metric, all parameters
+ # One metric, two parameters
prog1 = trial_progress_str(
- trials, ["metric_1"], None, fmt="psql", max_rows=3)
+ trials, ["metric_1"], ["a", "b"], fmt="psql", max_rows=3)
print(prog1)
assert prog1 == EXPECTED_RESULT_1
| [Tune] status tables during training aren't showing values for tunable, nested config parameters
### What is the problem?
Ray 0.8.6. Python 3.7.7.
Running this script (actually run with ipython):
```python
from ray import tune
ray.init()
tune.run(
"PPO",
stop={"episode_reward_mean": 400},
config={
"env": "CartPole-v1",
"num_gpus": 0,
"num_workers": 1,
# "lr": tune.grid_search([0.01, 0.001, 0.0001]),
"model": {
'fcnet_hiddens': [tune.grid_search([20, 40, 60, 80]), tune.grid_search([20, 40, 60, 80])]
},
"eager": False,
},
)
```
It appears that Tune easily drives the 16 different combinations for the two elements of `fcnet_hidden` (number of weights for two NN layers). All combinations easily reach the reward of 400, but some do so with lower iterations and/or elapsed times, which is what I want to determine.
The bug is in the status table printed. It has two columns for the tuning parameters, `model/fcnet_hiddens/0` and `model/fcnet_hiddens/1`, but they are empty. I can determine the actual values by looking at the `experiment_tag`, e.g., ` experiment_tag: 8_fcnet_hiddens_0=20,fcnet_hiddens_1=60`
| Taking a look at this this week. I can reproduce the issue locally.
```
== Status ==
Memory usage on this node: 10.5/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 8/8 CPUs, 0/0 GPUs, 0.0/4.49 GiB heap, 0.0/1.51 GiB objects
Number of trials: 16 (4 RUNNING, 12 TERMINATED)
+-----------------------------+------------+--------------------+-------------------------+-------------------------+--------+------------------+--------+----------+
| Trial name | status | loc | model/fcnet_hiddens/0 | model/fcnet_hiddens/1 | iter | total time (s) | ts | reward |
|-----------------------------+------------+--------------------+-------------------------+-------------------------+--------+------------------+--------+----------|
| PPO_CartPole-v1_e7acd_00000 | TERMINATED | | | | 18 | 220.755 | 72000 | 406.54 |
| PPO_CartPole-v1_e7acd_00001 | TERMINATED | | | | 31 | 371.998 | 124000 | 403.33 |
| PPO_CartPole-v1_e7acd_00002 | TERMINATED | | | | 18 | 220.732 | 72000 | 406.52 |
| PPO_CartPole-v1_e7acd_00003 | TERMINATED | | | | 15 | 189.082 | 60000 | 409.03 |
| PPO_CartPole-v1_e7acd_00004 | TERMINATED | | | | 19 | 233.262 | 76000 | 410.45 |
| PPO_CartPole-v1_e7acd_00005 | TERMINATED | | | | 22 | 260.052 | 88000 | 412.14 |
| PPO_CartPole-v1_e7acd_00006 | TERMINATED | | | | 30 | 354.216 | 120000 | 401.67 |
| PPO_CartPole-v1_e7acd_00007 | TERMINATED | | | | 18 | 211.58 | 72000 | 403.37 |
| PPO_CartPole-v1_e7acd_00008 | TERMINATED | | | | 19 | 222.288 | 76000 | 416.59 |
| PPO_CartPole-v1_e7acd_00009 | TERMINATED | | | | 19 | 218.809 | 76000 | 406.62 |
| PPO_CartPole-v1_e7acd_00010 | TERMINATED | | | | 16 | 531.601 | 64000 | 400.38 |
| PPO_CartPole-v1_e7acd_00011 | TERMINATED | | | | 21 | 4509.18 | 84000 | 415.27 |
| PPO_CartPole-v1_e7acd_00012 | RUNNING | 192.168.2.66:49528 | | | 16 | 4449.35 | 64000 | 383.28 |
| PPO_CartPole-v1_e7acd_00013 | RUNNING | 192.168.2.66:49553 | | | 12 | 4406.25 | 48000 | 292.06 |
| PPO_CartPole-v1_e7acd_00014 | RUNNING | 192.168.2.66:49578 | | | 5 | 3502.22 | 20000 | 110.89 |
| PPO_CartPole-v1_e7acd_00015 | RUNNING | | | | | | | |
+-----------------------------+------------+--------------------+-------------------------+-------------------------+--------+------------------+--------+----------+
```
the trial config contains the `model/fcnet_hiddens` key e.g. `{'env': 'CartPole-v1', 'num_gpus': 0, 'num_workers': 1, 'eager': False, 'model/fcnet_hiddens': [20, 20]}` whereas `trial.evaluated_params` contains the flattened keys e.g. `{'model/fcnet_hiddens/0': 20, 'model/fcnet_hiddens/1': 20}` instead.
`progress_reporter` uses `trial.evaluated_params` to generate the header and looks into `trial.config` for the config values
| 2020-07-16T18:14:09 |
ray-project/ray | 9,537 | ray-project__ray-9537 | [
"8913"
] | 78dfed268391fb5ea927a5094d9c171b9da8c052 | diff --git a/python/ray/tune/progress_reporter.py b/python/ray/tune/progress_reporter.py
--- a/python/ray/tune/progress_reporter.py
+++ b/python/ray/tune/progress_reporter.py
@@ -87,7 +87,7 @@ def __init__(self,
max_progress_rows=20,
max_error_rows=20,
max_report_frequency=5):
- self._metric_columns = metric_columns or self.DEFAULT_COLUMNS
+ self._metric_columns = metric_columns or self.DEFAULT_COLUMNS.copy()
self._parameter_columns = parameter_columns or []
self._max_progress_rows = max_progress_rows
self._max_error_rows = max_error_rows
| [Tune] ProgressReporter default_columns is shared across instances
### What is the problem?
When adding a new column to a ProgressReporter that used the `DEFAULT_COLUMNS` during its creation, this column will be added to the actual default dictionary. This can cause several unexpected behaviors.
For instance, when creating several experiments with custom columns for each of them, using the same column multiple times will break (see reproduction).
### Reproduction (REQUIRED)
```python
from ray.tune import CLIReporter
reporter = CLIReporter()
reporter.add_metric_column('custom column')
reporter = CLIReporter()
reporter.add_metric_column('custom column')
```
Thows a `ValueError: Column custom column already exists.`
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
### Solution
A solution is to copy this default parameter each time it is used [here](https://github.com/ray-project/ray/blob/4b31b383f379dad5514f4d93a9776db6b3caa38c/python/ray/tune/progress_reporter.py#L90).
I can send a PR is this is okay for you.
| Sorry; I totally missed your comment at the bottom. A PR would be great! | 2020-07-17T10:29:17 |
|
ray-project/ray | 9,547 | ray-project__ray-9547 | [
"9541"
] | bfa06052828f83ab790e6b6bbfa5b56edb42b45e | diff --git a/python/ray/node.py b/python/ray/node.py
--- a/python/ray/node.py
+++ b/python/ray/node.py
@@ -394,8 +394,10 @@ def _make_inc_temp(self, suffix="", prefix="", directory_name=None):
raise FileExistsError(errno.EEXIST,
"No usable temporary filename found")
- def get_log_file_names(self, name, unique=False):
- """Generate partially randomized filenames for log files.
+ def get_log_file_handles(self, name, unique=False):
+ """Open log files with partially randomized filenames, returning the
+ file handles. If output redirection has been disabled, no files will
+ be opened and `(None, None)` will be returned.
Args:
name (str): descriptive string for this log file.
@@ -403,7 +405,8 @@ def get_log_file_names(self, name, unique=False):
ensure the returned filename is not already used.
Returns:
- A tuple of two file names for redirecting (stdout, stderr).
+ A tuple of two file handles for redirecting (stdout, stderr), or
+ `(None, None)` if output redirection is disabled.
"""
redirect_output = self._ray_params.redirect_output
@@ -414,6 +417,21 @@ def get_log_file_names(self, name, unique=False):
if not redirect_output:
return None, None
+ log_stdout, log_stderr = self._get_log_file_names(name, unique=unique)
+ return open_log(log_stdout), open_log(log_stderr)
+
+ def _get_log_file_names(self, name, unique=False):
+ """Generate partially randomized filenames for log files.
+
+ Args:
+ name (str): descriptive string for this log file.
+ unique (bool): if true, a counter will be attached to `name` to
+ ensure the returned filename is not already used.
+
+ Returns:
+ A tuple of two file names for redirecting (stdout, stderr).
+ """
+
if unique:
log_stdout = self._make_inc_temp(
suffix=".out", prefix=name, directory_name=self._logs_dir)
@@ -501,15 +519,11 @@ def start_reaper_process(self):
def start_redis(self):
"""Start the Redis servers."""
assert self._redis_address is None
- redis_out_name, redis_err_name = self.get_log_file_names(
- "redis", unique=True)
- redis_log_files = [(open_log(redis_out_name),
- open_log(redis_err_name))]
+ redis_log_files = [self.get_log_file_handles("redis", unique=True)]
for i in range(self._ray_params.num_redis_shards):
- shard_out_name, shard_err_name = self.get_log_file_names(
- "redis-shard_{}".format(i), unique=True)
- redis_log_files.append((open_log(shard_out_name),
- open_log(shard_err_name)))
+ redis_log_files.append(
+ self.get_log_file_handles(
+ "redis-shard_{}".format(i), unique=True))
(self._redis_address, redis_shards,
process_infos) = ray.services.start_redis(
@@ -531,10 +545,8 @@ def start_redis(self):
def start_log_monitor(self):
"""Start the log monitor."""
- log_out_name, log_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"log_monitor", unique=True)
- stdout_file, stderr_file = open_log(log_out_name), open_log(
- log_err_name)
process_info = ray.services.start_log_monitor(
self.redis_address,
self._logs_dir,
@@ -549,10 +561,8 @@ def start_log_monitor(self):
def start_reporter(self):
"""Start the reporter."""
- reporter_out_name, reporter_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"reporter", unique=True)
- stdout_file, stderr_file = (open_log(reporter_out_name),
- open_log(reporter_err_name))
process_info = ray.services.start_reporter(
self.redis_address,
self._ray_params.metrics_agent_port,
@@ -574,10 +584,8 @@ def start_dashboard(self, require_dashboard):
if we fail to start the dashboard. Otherwise it will print
a warning if we fail to start the dashboard.
"""
- dashboard_out_name, dashboard_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"dashboard", unique=True)
- stdout_file, stderr_file = (open_log(dashboard_out_name),
- open_log(dashboard_err_name))
self._webui_url, process_info = ray.services.start_dashboard(
require_dashboard,
self._ray_params.dashboard_host,
@@ -598,10 +606,8 @@ def start_dashboard(self, require_dashboard):
def start_plasma_store(self):
"""Start the plasma store."""
- plasma_out_name, plasma_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"plasma_store", unique=True)
- stdout_file, stderr_file = (open_log(plasma_out_name),
- open_log(plasma_err_name))
process_info = ray.services.start_plasma_store(
self.get_resource_spec(),
self._plasma_store_socket_name,
@@ -620,10 +626,8 @@ def start_plasma_store(self):
def start_gcs_server(self):
"""Start the gcs server.
"""
- gcs_out_name, gcs_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"gcs_server", unique=True)
- stdout_file, stderr_file = (open_log(gcs_out_name),
- open_log(gcs_err_name))
process_info = ray.services.start_gcs_server(
self._redis_address,
stdout_file=stdout_file,
@@ -647,10 +651,8 @@ def start_raylet(self, use_valgrind=False, use_profiler=False):
use_profiler (bool): True if we should start the process in the
valgrind profiler.
"""
- raylet_out_name, raylet_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"raylet", unique=True)
- stdout_file, stderr_file = (open_log(raylet_out_name),
- open_log(raylet_err_name))
process_info = ray.services.start_raylet(
self._redis_address,
self._node_ip_address,
@@ -713,7 +715,7 @@ def get_job_redirected_log_file(self,
else:
name = "worker-{}".format(ray.utils.binary_to_hex(worker_id))
- worker_stdout_file, worker_stderr_file = self.get_log_file_names(
+ worker_stdout_file, worker_stderr_file = self._get_log_file_names(
name, unique=False)
return worker_stdout_file, worker_stderr_file
@@ -723,10 +725,8 @@ def start_worker(self):
def start_monitor(self):
"""Start the monitor."""
- monitor_out_name, monitor_err_name = self.get_log_file_names(
+ stdout_file, stderr_file = self.get_log_file_handles(
"monitor", unique=True)
- stdout_file, stderr_file = (open_log(monitor_out_name),
- open_log(monitor_err_name))
process_info = ray.services.start_monitor(
self._redis_address,
stdout_file=stdout_file,
diff --git a/python/ray/utils.py b/python/ray/utils.py
--- a/python/ray/utils.py
+++ b/python/ray/utils.py
@@ -391,6 +391,10 @@ def setup_logger(logging_level, logging_format):
def open_log(path, **kwargs):
+ """
+ Opens the log file at `path`, with the provided kwargs being given to
+ `open`.
+ """
kwargs.setdefault("buffering", 1)
kwargs.setdefault("mode", "a")
kwargs.setdefault("encoding", "utf-8")
| [Core] Turning off log redirection to files breaks all Ray service startups.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Turning off log redirection to files breaks all Ray process startups (redis, GCS server, log monitor, reporter, plasma store, raylet, etc.)
```
File "/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/scripts/scripts.py", line 470, in start
ray_params, head=True, shutdown_at_exit=block, spawn_reaper=block)
File "/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py", line 194, in __init__
self.start_head_processes()
File "/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py", line 746, in start_head_processes
self.start_redis()
File "/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py", line 506, in start_redis
redis_log_files = [(open_log(redis_out_name),
File "/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/utils.py", line 397, in open_log
return open(path, **kwargs)
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
*Ray version and other system information (Python version, TensorFlow version, OS):*
This bug was introduced in [this PR](https://github.com/ray-project/ray/pull/8885), so it shouldn't be affecting any releases.
The issue is that `get_log_file_names` will return [`None, None` if output redirection is turned off](https://github.com/ray-project/ray/blob/f080aa6ce33ed34f686e868d97b85632c8a8f894/python/ray/node.py#L414-L415), while all callers of that function now pass the returned log file names directly to `open_log`, e.g. [here](https://github.com/ray-project/ray/blob/f080aa6ce33ed34f686e868d97b85632c8a8f894/python/ray/node.py#L506-L507).
### Reproduction:
Start Ray with the environment variable `GLOG_logtostderr` set to 1, or with `redirect_output` or `redirect_worker_output` set to `False`. I've confirmed that this bug exists on the latest build off master.
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| I'll probably be submitting a fix soon.
Awesome! Thanks @clarkzinzow !! | 2020-07-17T17:50:29 |
|
ray-project/ray | 9,561 | ray-project__ray-9561 | [
"9553"
] | cf719dd470f61918f0de8550c885360a78497e59 | diff --git a/python/ray/node.py b/python/ray/node.py
--- a/python/ray/node.py
+++ b/python/ray/node.py
@@ -647,7 +647,8 @@ def start_raylet(self, use_valgrind=False, use_profiler=False):
use_profiler (bool): True if we should start the process in the
valgrind profiler.
"""
- raylet_out_name, raylet_err_name = self.get_log_file_names("raylet")
+ raylet_out_name, raylet_err_name = self.get_log_file_names(
+ "raylet", unique=True)
stdout_file, stderr_file = (open_log(raylet_out_name),
open_log(raylet_err_name))
process_info = ray.services.start_raylet(
| [core] Raylets in local cluster do not log to separate files
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):* 0.9dev
When multiple raylets are started locally, it used to be that they would each log to a separate file, e.g., `raylet.1.out`, `raylet.2.out`, etc. Now, they all log to `raylet.out`, which makes it harder to debug.
### Reproduction (REQUIRED)
Any checked in pytest that starts a local cluster. For example, `RAY_BACKEND_LOG_LEVEL=debug pytest -sv python/ray/tests/test_advanced.py::test_wait_cluster`
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| I believe this was caused by #8885, @wuisawesome.
@stephanie-wang That was another regression I noticed, adding a `unique=True` argument to `get_log_file_names` [here](https://github.com/ray-project/ray/blob/5881417ec4a993194ba6d64b156ad6b5ffbca97c/python/ray/node.py#L650) should do the trick:
```
raylet_out_name, raylet_err_name = self.get_log_file_names("raylet", unique=True)
```
> @stephanie-wang That was another regression I noticed, adding a `unique=True` argument to `get_log_file_names` [here](https://github.com/ray-project/ray/blob/5881417ec4a993194ba6d64b156ad6b5ffbca97c/python/ray/node.py#L650) should do the trick:
>
> ```
> raylet_out_name, raylet_err_name = self.get_log_file_names("raylet", unique=True)
> ```
Ah, nice find!
Nice catch, I may have made this same mistake in a few places. @clarkzinzow do you want to submit a PR for this? If not, I'll try to get around to it in a week or so.
@wuisawesome Sure I can submit a PR for that, I'm already in this code for [another PR](https://github.com/ray-project/ray/pull/9547) so it's not much of a context switch. | 2020-07-18T00:54:34 |
|
ray-project/ray | 9,572 | ray-project__ray-9572 | [
"9568"
] | ce3f5427394af288f9699bed3d479c47181b252f | diff --git a/rllib/train.py b/rllib/train.py
--- a/rllib/train.py
+++ b/rllib/train.py
@@ -180,22 +180,23 @@ def run(args, parser):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
- if args.eager:
- exp["config"]["framework"] = "tfe"
- elif args.torch:
+
+ if args.torch:
exp["config"]["framework"] = "torch"
- else:
- exp["config"]["framework"] = "tf"
+ elif args.eager:
+ exp["config"]["framework"] = "tfe"
+
+ if args.trace:
+ if exp["config"]["framework"] not in ["tf2", "tfe"]:
+ raise ValueError("Must enable --eager to enable tracing.")
+ exp["config"]["eager_tracing"] = True
+
if args.v:
exp["config"]["log_level"] = "INFO"
verbose = 2
if args.vv:
exp["config"]["log_level"] = "DEBUG"
verbose = 3
- if args.trace:
- if exp["config"]["framework"] != "tfe":
- raise ValueError("Must enable --eager to enable tracing.")
- exp["config"]["eager_tracing"] = True
if args.ray_num_nodes:
cluster = Cluster()
| [rllib] Train script overwrites framework field in experiment config argument
[rllib]
### What is the problem?
Framework setting passed to training script via config argument is ignored, because training script uses separate arguments for this field (`--torch`, `--eager`). If none of these args is passed to the script the framework is set to `tf`, regardless of options passed in `--config` argument.
### Expected behavior
Script should use default value for `framework` field only if it is not already present in `config` dict (it can be passed as cmd argument or be loaded from file).
### Reproduction (REQUIRED)
Run `rllib train --run DQN --env CartPole-v0 --config "{\"framework\": \"tfe\"}"` - script uses `tf` instead of `tfe`
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-07-19T18:59:50 |
||
ray-project/ray | 9,966 | ray-project__ray-9966 | [
"9967"
] | 84b7240c4bcbd9373ec18db8be483a3e41a4f02a | diff --git a/rllib/models/tf/visionnet.py b/rllib/models/tf/visionnet.py
--- a/rllib/models/tf/visionnet.py
+++ b/rllib/models/tf/visionnet.py
@@ -11,15 +11,17 @@ class VisionNetwork(TFModelV2):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
+ if not model_config.get("conv_filters"):
+ model_config["conv_filters"] = _get_filter_config(obs_space.shape)
+
super(VisionNetwork, self).__init__(obs_space, action_space,
num_outputs, model_config, name)
- activation = get_activation_fn(model_config.get("conv_activation"))
- filters = model_config.get("conv_filters")
- if not filters:
- filters = _get_filter_config(obs_space.shape)
- no_final_linear = model_config.get("no_final_linear")
- vf_share_layers = model_config.get("vf_share_layers")
+ activation = get_activation_fn(
+ self.model_config.get("conv_activation"), framework="tf")
+ filters = self.model_config["conv_filters"]
+ no_final_linear = self.model_config.get("no_final_linear")
+ vf_share_layers = self.model_config.get("vf_share_layers")
inputs = tf.keras.layers.Input(
shape=obs_space.shape, name="observations")
@@ -73,6 +75,16 @@ def __init__(self, obs_space, action_space, num_outputs, model_config,
padding="same",
data_format="channels_last",
name="conv_out")(last_layer)
+
+ if conv_out.shape[1] != 1 or conv_out.shape[2] != 1:
+ raise ValueError(
+ "Given `conv_filters` ({}) do not result in a [B, 1, "
+ "1, {} (`num_outputs`)] shape (but in {})! Please "
+ "adjust your Conv2D stack such that the dims 1 and 2 "
+ "are both 1.".format(
+ self.model_config["conv_filters"],
+ self.num_outputs, list(conv_out.shape)))
+
# num_outputs not known -> Flatten, then set self.num_outputs
# to the resulting number of nodes.
else:
diff --git a/rllib/models/torch/visionnet.py b/rllib/models/torch/visionnet.py
--- a/rllib/models/torch/visionnet.py
+++ b/rllib/models/torch/visionnet.py
@@ -15,17 +15,18 @@ class VisionNetwork(TorchModelV2, nn.Module):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name):
+ if not model_config.get("conv_filters"):
+ model_config["conv_filters"] = _get_filter_config(obs_space.shape)
+
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
activation = get_activation_fn(
- model_config.get("conv_activation"), framework="torch")
- filters = model_config.get("conv_filters")
- if not filters:
- filters = _get_filter_config(obs_space.shape)
- no_final_linear = model_config.get("no_final_linear")
- vf_share_layers = model_config.get("vf_share_layers")
+ self.model_config.get("conv_activation"), framework="torch")
+ filters = self.model_config["conv_filters"]
+ no_final_linear = self.model_config.get("no_final_linear")
+ vf_share_layers = self.model_config.get("vf_share_layers")
# Whether the last layer is the output of a Flattened (rather than
# a n x (1,1) Conv2D).
@@ -152,8 +153,17 @@ def forward(self, input_dict, state, seq_lens):
if not self.last_layer_is_flattened:
if self._logits:
conv_out = self._logits(conv_out)
+ if conv_out.shape[2] != 1 or conv_out.shape[3] != 1:
+ raise ValueError(
+ "Given `conv_filters` ({}) do not result in a [B, {} "
+ "(`num_outputs`), 1, 1] shape (but in {})! Please adjust "
+ "your Conv2D stack such that the last 2 dims are both "
+ "1.".format(
+ self.model_config["conv_filters"], self.num_outputs,
+ list(conv_out.shape)))
logits = conv_out.squeeze(3)
logits = logits.squeeze(2)
+
return logits, state
else:
return conv_out, state
| [RLlib] Add informative error message when bad Conv2D stack is used with fixed `num_outputs` (no flattening at end).
We should add an informative error message to visionnet.py (torch and tf), when a bad Conv2D stack (`conv_filters` in model config) is configured by the user, with a fixed `num_outputs` (no flattening at end). In this case, the output of the last Conv2D layer must always be [B, num_outputs, 1, 1].
```
import gym
import numpy as np
import ray
from ray import tune
from ray.rllib.models import ModelCatalog
from ray.tune.registry import register_env
from ray.rllib.agents.ppo import PPOTrainer, ppo
from ray.tune import CLIReporter
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
from ray.rllib.models.torch.visionnet import VisionNetwork
from ray.rllib.utils.framework import try_import_torch
ray.init()
torch, nn = try_import_torch()
#### ENV
class JSS(gym.Env):
def __init__(self, env_config):
self.problem_size = 16
self.action_space = gym.spaces.Discrete(self.problem_size + 1)
self.observation_space = gym.spaces.Dict({
'real_obs': gym.spaces.Box(low=0.0, high=1.0, shape=(self.problem_size, 8, 1), dtype=np.float),
'action_mask': gym.spaces.Box(low=0, high=1, shape=(self.action_space.n, ), dtype=np.int),
})
self.i = 0
def reset(self):
self.i = 0
legal_action = np.ones((self.action_space.n,), dtype=np.int)
legal_action[0] = 0
output = {
'real_obs': np.zeros((self.problem_size, 8, 1), dtype=np.float),
'action_mask': legal_action
}
return output
def step(self, action: int):
self.i += 1
legal_action = np.ones((self.action_space.n, ), dtype=np.int)
legal_action[self.problem_size] = 0
legal_action[self.i % self.problem_size + 1] = 0
output = {
'real_obs': np.zeros((self.problem_size, 8, 1), dtype=np.float),
'action_mask': legal_action
}
return output
def render(self, mode='human'):
pass
def env_creator(env_config):
return JSS(env_config)
register_env("jss_env", env_creator)
## MODEL
# THIS ONE WORKS
class FCMaskedActionsModel(TorchModelV2, nn.Module):
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name):
nn.Module.__init__(self)
super(FCMaskedActionsModel, self).__init__(obs_space, action_space, action_space.n, model_config, name)
true_obs_space = gym.spaces.MultiBinary(n=obs_space.shape[0] - action_space.n)
self.action_model = FullyConnectedNetwork(
obs_space=true_obs_space, action_space=action_space, num_outputs=action_space.n,
model_config=model_config, name=name + "action_model")
def forward(self, input_dict, state, seq_lens):
# Extract the available actions tensor from the observation.
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
raw_actions, state = self.action_model({
"obs": input_dict["obs"]["real_obs"]
})
inf_mask = torch.clamp(torch.log(action_mask), min=torch.finfo(raw_actions.dtype).min)
actions = raw_actions + inf_mask
return actions, state
def value_function(self):
return self.action_model.value_function()
#THIS ONE HAS A SIZE MISMATCH PROBLEM WITH THE LOGIT LAYER
class ConvMaskedActionsModel(TorchModelV2, nn.Module):
def __init__(self,
obs_space,
action_space,
num_outputs,
model_config,
name):
nn.Module.__init__(self)
super(ConvMaskedActionsModel, self).__init__(obs_space, action_space, action_space.n, model_config, name)
true_obs_space = obs_space.original_space['real_obs']
self.action_model = VisionNetwork(
obs_space=true_obs_space, action_space=action_space, num_outputs=action_space.n,
model_config=model_config, name=name + "action_model_conv")
def forward(self, input_dict, state, seq_lens):
# Extract the available actions tensor from the observation.
action_mask = input_dict["obs"]["action_mask"]
# Compute the predicted action embedding
raw_actions, state = self.action_model({
"obs": input_dict["obs"]["real_obs"]
})
inf_mask = torch.clamp(torch.log(action_mask), min=torch.finfo(raw_actions.dtype).min)
actions = raw_actions + inf_mask
return actions, state
def value_function(self):
return self.action_model.value_function()
ModelCatalog.register_custom_model("fc_masked_model", FCMaskedActionsModel)
ModelCatalog.register_custom_model("conv_masked_model", ConvMaskedActionsModel)
config = ppo.DEFAULT_CONFIG.copy()
config['framework'] = 'torch'
config['env'] = 'jss_env'
config['model'] = {
"conv_filters": [[4, [4, 4], 1], [8, [4, 4], 1]],
"custom_model": "conv_masked_model",
# Nonlinearity for fully connected net (tanh, relu)
"fcnet_activation": "tanh",
}
config['train_batch_size'] = 1024
config['rollout_fragment_length'] = 1000
config['sgd_minibatch_size'] = 256
config['lr'] = 1e-4
config['evaluation_interval'] = None
config['metrics_smoothing_episodes'] = 1000
stop = {
"time_total_s": 60,
}
reporter = CLIReporter(max_progress_rows=15)
reporter.add_metric_column("episode_reward_max")
analysis = tune.run(PPOTrainer, config=config, stop=stop, progress_reporter=reporter)
print("Best config: ", analysis.get_best_config(metric="episode_reward_max"))
ray.shutdown()
```
| 2020-08-06T20:15:58 |
||
ray-project/ray | 10,078 | ray-project__ray-10078 | [
"10077"
] | 739933e5b84291dea54c29fc6acc982a3d42a52f | diff --git a/python/ray/autoscaler/command_runner.py b/python/ray/autoscaler/command_runner.py
--- a/python/ray/autoscaler/command_runner.py
+++ b/python/ray/autoscaler/command_runner.py
@@ -556,6 +556,7 @@ def run(
ssh_options_override_ssh_key=ssh_options_override_ssh_key)
def run_rsync_up(self, source, target):
+ # TODO(ilr) Expose this to before NodeUpdater::sync_file_mounts
protected_path = target
if target.find("/root") == 0:
target = target.replace("/root", "/tmp/root")
diff --git a/python/ray/autoscaler/updater.py b/python/ray/autoscaler/updater.py
--- a/python/ray/autoscaler/updater.py
+++ b/python/ray/autoscaler/updater.py
@@ -145,8 +145,9 @@ def do_sync(remote_path, local_path, allow_non_existing_paths=False):
with LogTimer(self.log_prefix +
"Synced {} to {}".format(local_path, remote_path)):
- self.cmd_runner.run("mkdir -p {}".format(
- os.path.dirname(remote_path)))
+ self.cmd_runner.run(
+ "mkdir -p {}".format(os.path.dirname(remote_path)),
+ run_env="host")
sync_cmd(local_path, remote_path)
if remote_path not in nolog_paths:
@@ -188,7 +189,7 @@ def wait_ready(self, deadline):
"{}Waiting for remote shell...",
self.log_prefix)
- self.cmd_runner.run("uptime")
+ self.cmd_runner.run("uptime", run_env="host")
cli_logger.old_debug(logger, "Uptime succeeded.")
cli_logger.success("Success.")
return True
| Docker + AWS fails with no such container error
Latest dev version of ray
```
(vanilla_ray_venv) richard@richard-desktop:~/improbable/vanillas/ray/python/ray/autoscaler/aws$ ray up aws_gpu_dummy.yaml
2020-08-12 20:12:39,383 INFO config.py:268 -- _configure_iam_role: Role not specified for head node, using arn:aws:iam::179622923911:instance-profile/ray-autoscaler-v1
2020-08-12 20:12:39,612 INFO config.py:346 -- _configure_key_pair: KeyName not specified for nodes, using ray-autoscaler_us-east-1
2020-08-12 20:12:39,745 INFO config.py:407 -- _configure_subnet: SubnetIds not specified for head node, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:39,746 INFO config.py:417 -- _configure_subnet: SubnetId not specified for workers, using [('subnet-f737f791', 'us-east-1a')]
2020-08-12 20:12:40,358 INFO config.py:590 -- _create_security_group: Created new security group ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739 INFO config.py:444 -- _configure_security_group: SecurityGroupIds not specified for head node, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
2020-08-12 20:12:40,739 INFO config.py:454 -- _configure_security_group: SecurityGroupIds not specified for workers, using ray-autoscaler-richard_cluster_gpu_dummy (sg-0061ca6aff182c1bf)
This will create a new cluster [y/N]: y
2020-08-12 20:12:42,619 INFO commands.py:531 -- get_or_create_head_node: Launching new head node...
2020-08-12 20:12:42,620 INFO node_provider.py:326 -- NodeProvider: calling create_instances with subnet-f737f791 (count=1).
2020-08-12 20:12:44,032 INFO node_provider.py:354 -- NodeProvider: Created instance [id=i-0729c7a86355d5ff8, name=pending, info=pending]
2020-08-12 20:12:44,223 INFO commands.py:570 -- get_or_create_head_node: Updating files on head node...
2020-08-12 20:12:44,320 INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,409 INFO command_runner.py:331 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for IP...
2020-08-12 20:12:54,534 INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got IP [LogTimer=10310ms]
2020-08-12 20:12:54,534 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && command -v docker'
Warning: Permanently added '3.226.253.119' (ECDSA) to the list of known hosts.
/usr/bin/docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:04,587 INFO updater.py:71 -- NodeUpdater: i-0729c7a86355d5ff8: Updating to 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9
2020-08-12 20:14:04,587 INFO updater.py:180 -- NodeUpdater: i-0729c7a86355d5ff8: Waiting for remote shell...
2020-08-12 20:14:04,587 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
2020-08-12 20:14:04,950 INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=waiting-for-ssh on ['i-0729c7a86355d5ff8'] [LogTimer=361ms]
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:21,222 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:26,417 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:31,610 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:36,798 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:41,986 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:47,170 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:52,358 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:14:57,554 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:02,750 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:07,938 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:13,126 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:18,307 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:15:23,494 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:01,502 INFO command_runner.py:468 -- NodeUpdater: i-0729c7a86355d5ff8: Running ssh -tt -i /home/richard/.ssh/ray-autoscaler_us-east-1.pem -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o IdentitiesOnly=yes -o ExitOnForwardFailure=yes -o ServerAliveInterval=5 -o ServerAliveCountMax=3 -o ControlMaster=auto -o ControlPath=/tmp/ray_ssh_6ae199a93c/cfde1c79f1/%C -o ControlPersist=10s -o ConnectTimeout=120s [email protected] bash --login -c -i 'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && docker exec -it pytorch_docker /bin/bash -c '"'"'bash --login -c -i '"'"'"'"'"'"'"'"'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && uptime'"'"'"'"'"'"'"'"''"'"' '
Error: No such container: pytorch_docker
Shared connection to 3.226.253.119 closed.
2020-08-12 20:19:06,689 INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Got remote shell [LogTimer=302102ms]
2020-08-12 20:19:06,690 INFO log_timer.py:27 -- NodeUpdater: i-0729c7a86355d5ff8: Applied config 6b5fc8ee8c5dcdf3cfabe0bf90ba4e844f65a7c9 [LogTimer=302103ms]
2020-08-12 20:19:06,690 ERROR updater.py:88 -- NodeUpdater: i-0729c7a86355d5ff8: Error executing: Unable to connect to node
Exception in thread Thread-2:
Traceback (most recent call last):
File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py", line 76, in run
self.do_update()
File "/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py", line 232, in do_update
self.wait_ready(deadline)
File "/home/richard/improbable/vanillas/ray/python/ray/autoscaler/updater.py", line 224, in wait_ready
assert False, "Unable to connect to node"
AssertionError: Unable to connect to node
2020-08-12 20:19:06,962 ERROR commands.py:650 -- get_or_create_head_node: Updating 3.226.253.119 failed
2020-08-12 20:19:07,002 INFO log_timer.py:27 -- AWSNodeProvider: Set tag ray-node-status=update-failed on ['i-0729c7a86355d5ff8'] [LogTimer=312ms]
```
YAML file for repo attached.
```
# An unique identifier for the head node and workers of this cluster.
cluster_name: richard_cluster_gpu_dummy
# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
min_workers: 1
# The maximum number of workers nodes to launch in addition to the head
# node. This takes precedence over min_workers.
max_workers: 5
# The initial number of worker nodes to launch in addition to the head
# node. When the cluster is first brought up (or when it is refreshed with a
# subsequent `ray up`) this number of nodes will be started.
initial_workers: 1
# Whether or not to autoscale aggressively. If this is enabled, if at any point
# we would start more workers, we start at least enough to bring us to
# initial_workers.
autoscaling_mode: default
# This executes all commands on all nodes in the docker efcontainer,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled.
docker:
image: "pytorch/pytorch:latest" # e.g., tensorflow/tensorflow:1.5.0-py3
container_name: "pytorch_docker" # e.g. ray_docker
# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
# if no cached version is present.
pull_before_run: True
run_options: []
# - $([ -d /proc/driver ] && echo -n --runtime-nvidia) # Use the nvidia runtime only if nvidia gpu's are installed
worker_run_options:
- --runtime=nvidia # Extra options to pass into "docker run"
# Example of running a GPU head with CPU workers
# head_image: "tensorflow/tensorflow:1.13.1-py3"
# head_run_options:
# - --runtime=nvidia
# worker_image: "ubuntu:18.04"
# worker_run_options: []
# The autoscaler will scale up the cluster to this target fraction of resource
# usage. For example, if a cluster of 10 nodes is 100% busy and
# target_utilization is 0.8, it would resize the cluster to 13. This fraction
# can be decreased to increase the aggressiveness of upscaling.
# This value must be less than 1.0 for scaling to happen.
target_utilization_fraction: 0.8
# If a node is idle for this many minutes, it will be removed.
idle_timeout_minutes: 5
# Cloud-provider specific configuration.
provider:
type: aws
region: us-east-1
# Availability zone(s), comma-separated, that nodes may be launched in.
# Nodes are currently spread between zones by a round-robin approach,
# however this implementation detail should not be relied upon.
availability_zone: us-east-1a, us-east-1b
cache_stopped_nodes: False
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: ubuntu
# By default Ray creates a new private keypair, but you can also use your own.
# If you do so, make sure to also set "KeyName" in the head and worker node
# configurations below.
# ssh_private_key: /path/to/your/key.pem
# Provider-specific config for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
head_node:
InstanceType: c4.2xlarge
ImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3
# You can provision additional disk space with a conf as follows
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 100
# Additional options in the boto docs.
# Provider-specific config for worker nodes, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
worker_nodes:
InstanceType: p3.2xlarge
ImageId: ami-043f9aeaf108ebc37 # Deep Learning AMI (Ubuntu) Version 24.3
# Run workers on spot by default. Comment this out to use on-demand.
InstanceMarketOptions:
MarketType: spot
# Additional options can be found in the boto docs, e.g.
# SpotOptions:
# MaxPrice: MAX_HOURLY_PRICE
# Additional options in the boto docs.
# Files or directories to copy to the head and worker nodes. The format is a
# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
file_mounts: {
}
# List of commands that will be run before `setup_commands`. If docker is
# enabled, these commands will run outside the container and before docker
# is setup.
initialization_commands: []
# List of shell commands to run to set up nodes.
setup_commands:
# Note: if you're developing Ray, you probably want to create an AMI that
# has your Ray repo pre-cloned. Then, you can replace the pip installs
# below with a git checkout <your_sha> (and possibly a recompile).
- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
# Consider uncommenting these if you also want to run apt-get commands during setup
# - sudo pkill -9 apt-get || true
# - sudo pkill -9 dpkg || true
# - sudo dpkg --configure -a
# Custom commands that will be run on the head node after common setup.
head_setup_commands:
- pip install boto3 # 1.4.8 adds InstanceMarketOptions
# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands:
- pip install boto3 # 1.4.8 adds InstanceMarketOptions
# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
- ray stop
- ulimit -n 65536; ray start --num-cpus=0 --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
- ray stop
- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
```
| 2020-08-13T01:01:46 |
||
ray-project/ray | 10,097 | ray-project__ray-10097 | [
"9558"
] | 0effcda3e4fce48de26dcb7ab64ac236eaa543e0 | diff --git a/python/ray/autoscaler/command_runner.py b/python/ray/autoscaler/command_runner.py
--- a/python/ray/autoscaler/command_runner.py
+++ b/python/ray/autoscaler/command_runner.py
@@ -189,10 +189,16 @@ def run_rsync_up(self, source, target):
logger.warning(self.log_prefix +
"rsync failed: '{}'. Falling back to 'kubectl cp'"
.format(e))
- self.process_runner.check_call(self.kubectl + [
- "cp", source, "{}/{}:{}".format(self.namespace, self.node_id,
- target)
- ])
+ self.run_cp_up(source, target)
+
+ def run_cp_up(self, source, target):
+ if target.startswith("~"):
+ target = "/root" + target[1:]
+
+ self.process_runner.check_call(self.kubectl + [
+ "cp", source, "{}/{}:{}".format(self.namespace, self.node_id,
+ target)
+ ])
def run_rsync_down(self, source, target):
if target.startswith("~"):
@@ -209,10 +215,16 @@ def run_rsync_down(self, source, target):
logger.warning(self.log_prefix +
"rsync failed: '{}'. Falling back to 'kubectl cp'"
.format(e))
- self.process_runner.check_call(self.kubectl + [
- "cp", "{}/{}:{}".format(self.namespace, self.node_id, source),
- target
- ])
+ self.run_cp_down(source, target)
+
+ def run_cp_down(self, source, target):
+ if target.startswith("~"):
+ target = "/root" + target[1:]
+
+ self.process_runner.check_call(self.kubectl + [
+ "cp", "{}/{}:{}".format(self.namespace, self.node_id, source),
+ target
+ ])
def remote_shell_command_str(self):
return "{} exec -it {} bash".format(" ".join(self.kubectl),
diff --git a/python/ray/tune/integration/kubernetes.py b/python/ray/tune/integration/kubernetes.py
new file mode 100644
--- /dev/null
+++ b/python/ray/tune/integration/kubernetes.py
@@ -0,0 +1,169 @@
+import kubernetes
+import subprocess
+
+from ray import services, logger
+from ray.autoscaler.command_runner import KubernetesCommandRunner
+from ray.tune.syncer import NodeSyncer
+from ray.tune.sync_client import SyncClient
+
+
+def NamespacedKubernetesSyncer(namespace, use_rsync=False):
+ """Wrapper to return a ``KubernetesSyncer`` for a Kubernetes namespace.
+
+ Args:
+ namespace (str): Kubernetes namespace.
+ use_rsync (bool): Use ``rsync`` if True or ``kubectl cp``
+ if False. If True, ``rsync`` will need to be
+ installed in the Kubernetes pods for this to work.
+ If False, ``tar`` will need to be installed instead.
+
+ Returns: A ``KubernetesSyncer`` class to be passed to
+ ``tune.run(sync_to_driver)``.
+
+ Example:
+
+ .. code-block:: python
+
+ from ray.tune.integration.kubernetes import NamespacedKubernetesSyncer
+ tune.run(train,
+ sync_to_driver=NamespacedKubernetesSyncer("ray"))
+
+ """
+
+ class _NamespacedKubernetesSyncer(KubernetesSyncer):
+ _namespace = namespace
+ _use_rsync = use_rsync
+
+ return _NamespacedKubernetesSyncer
+
+
+class KubernetesSyncer(NodeSyncer):
+ """KubernetesSyncer used for synchronization between Kubernetes pods.
+
+ This syncer extends the node syncer, but is usually instantiated
+ without a custom sync client. The sync client defaults to
+ ``KubernetesSyncClient`` instead.
+
+ KubernetesSyncer uses the default namespace ``ray``. You should
+ probably use ``NamespacedKubernetesSyncer`` to return a class
+ with a custom namespace instead.
+ """
+
+ _namespace = "ray"
+ _use_rsync = False
+
+ def __init__(self, local_dir, remote_dir, sync_client=None):
+ self.local_ip = services.get_node_ip_address()
+ self.local_node = self._get_kubernetes_node_by_ip(self.local_ip)
+ self.worker_ip = None
+ self.worker_node = None
+
+ sync_client = sync_client or KubernetesSyncClient(
+ namespace=self.__class__._namespace,
+ use_rsync=self.__class__._use_rsync)
+
+ super(NodeSyncer, self).__init__(local_dir, remote_dir, sync_client)
+
+ def set_worker_ip(self, worker_ip):
+ self.worker_ip = worker_ip
+ self.worker_node = self._get_kubernetes_node_by_ip(worker_ip)
+
+ def _get_kubernetes_node_by_ip(self, node_ip):
+ """Return node name by internal or external IP"""
+ kubernetes.config.load_incluster_config()
+ api = kubernetes.client.CoreV1Api()
+ pods = api.list_namespaced_pod(self._namespace)
+ for pod in pods.items:
+ if pod.status.host_ip == node_ip or \
+ pod.status.pod_ip == node_ip:
+ return pod.metadata.name
+
+ logger.error(
+ "Could not find Kubernetes pod name for IP {}".format(node_ip))
+ return None
+
+ @property
+ def _remote_path(self):
+ return (self.worker_node, self._remote_dir)
+
+
+class KubernetesSyncClient(SyncClient):
+ """KubernetesSyncClient to be used by KubernetesSyncer.
+
+ This client takes care of executing the synchronization
+ commands for Kubernetes clients. In its ``sync_down`` and
+ ``sync_up`` commands, it expects tuples for the source
+ and target, respectively, for compatibility with the
+ KubernetesCommandRunner.
+
+ Args:
+ namespace (str): Namespace in which the pods live.
+ use_rsync (bool): Use ``rsync`` if True or ``kubectl cp``
+ if False. If True, ``rsync`` will need to be
+ installed in the Kubernetes pods for this to work.
+ If False, ``tar`` will need to be installed instead.
+ process_runner: How commands should be called.
+ Defaults to ``subprocess``.
+
+ """
+
+ def __init__(self, namespace, use_rsync=False, process_runner=subprocess):
+ self.namespace = namespace
+ self.use_rsync = use_rsync
+ self._process_runner = process_runner
+ self._command_runners = {}
+
+ def _create_command_runner(self, node_id):
+ """Create a command runner for one Kubernetes node"""
+ return KubernetesCommandRunner(
+ log_prefix="KubernetesSyncClient: {}:".format(node_id),
+ namespace=self.namespace,
+ node_id=node_id,
+ auth_config=None,
+ process_runner=self._process_runner)
+
+ def _get_command_runner(self, node_id):
+ """Create command runner if it doesn't exist"""
+ # Todo(krfricke): These cached runners are currently
+ # never cleaned up. They are cheap so this shouldn't
+ # cause much problems, but should be addressed if
+ # the SyncClient is used more extensively in the future.
+ if node_id not in self._command_runners:
+ command_runner = self._create_command_runner(node_id)
+ self._command_runners[node_id] = command_runner
+ return self._command_runners[node_id]
+
+ def sync_up(self, source, target):
+ """Here target is a tuple (target_node, target_dir)"""
+ target_node, target_dir = target
+
+ # Add trailing slashes for rsync
+ source += "/" if not source.endswith("/") else ""
+ target_dir += "/" if not target_dir.endswith("/") else ""
+
+ command_runner = self._get_command_runner(target_node)
+ if self.use_rsync:
+ command_runner.run_rsync_up(source, target_dir)
+ else:
+ command_runner.run_cp_up(source, target_dir)
+ return True
+
+ def sync_down(self, source, target):
+ """Here source is a tuple (source_node, source_dir)"""
+ source_node, source_dir = source
+
+ # Add trailing slashes for rsync
+ source_dir += "/" if not source_dir.endswith("/") else ""
+ target += "/" if not target.endswith("/") else ""
+
+ command_runner = self._get_command_runner(source_node)
+ if self.use_rsync:
+ command_runner.run_rsync_down(source_dir, target)
+ else:
+ command_runner.run_cp_down(source_dir, target)
+ return True
+
+ def delete(self, target):
+ """No delete function because it is only used by
+ the KubernetesSyncer, which doesn't call delete."""
+ return True
diff --git a/python/ray/tune/syncer.py b/python/ray/tune/syncer.py
--- a/python/ray/tune/syncer.py
+++ b/python/ray/tune/syncer.py
@@ -3,6 +3,7 @@
import os
import time
+from inspect import isclass
from shlex import quote
from ray import ray_constants
@@ -285,6 +286,9 @@ def get_node_syncer(local_dir, remote_dir=None, sync_function=None):
key = (local_dir, remote_dir)
if key in _syncers:
return _syncers[key]
+ elif isclass(sync_function) and issubclass(sync_function, Syncer):
+ _syncers[key] = sync_function(local_dir, remote_dir, None)
+ return _syncers[key]
elif not remote_dir or sync_function is False:
sync_client = NOOP
elif sync_function and sync_function is not True:
| diff --git a/python/ray/tune/tests/test_integration_kubernetes.py b/python/ray/tune/tests/test_integration_kubernetes.py
new file mode 100644
--- /dev/null
+++ b/python/ray/tune/tests/test_integration_kubernetes.py
@@ -0,0 +1,147 @@
+import unittest
+
+from ray.autoscaler.command_runner import KUBECTL_RSYNC
+from ray.tune.integration.kubernetes import KubernetesSyncer, \
+ KubernetesSyncClient
+from ray.tune.syncer import NodeSyncer
+
+
+class _MockProcessRunner:
+ def __init__(self):
+ self.history = []
+
+ def check_call(self, command):
+ self.history.append(command)
+ return True
+
+
+class _MockLookup:
+ def __init__(self, node_ips):
+ self.node_to_ip = {}
+ self.ip_to_node = {}
+ for node, ip in node_ips.items():
+ self.node_to_ip[node] = ip
+ self.ip_to_node[ip] = node
+
+ def get_ip(self, node):
+ return self.node_to_ip[node]
+
+ def get_node(self, ip):
+ return self.ip_to_node[ip]
+
+ def __call__(self, ip):
+ return self.ip_to_node[ip]
+
+
+def _create_mock_syncer(namespace, lookup, use_rsync, process_runner, local_ip,
+ local_dir, remote_dir):
+ class _MockSyncer(KubernetesSyncer):
+ _namespace = namespace
+ _use_rsync = use_rsync
+ _get_kubernetes_node_by_ip = lookup
+
+ def __init__(self, local_dir, remote_dir, sync_client):
+ self.local_ip = local_ip
+ self.local_node = self._get_kubernetes_node_by_ip(self.local_ip)
+ self.worker_ip = None
+ self.worker_node = None
+
+ sync_client = sync_client
+ super(NodeSyncer, self).__init__(local_dir, remote_dir,
+ sync_client)
+
+ return _MockSyncer(
+ local_dir,
+ remote_dir,
+ sync_client=KubernetesSyncClient(
+ namespace=namespace,
+ use_rsync=use_rsync,
+ process_runner=process_runner))
+
+
+class KubernetesIntegrationTest(unittest.TestCase):
+ def setUp(self):
+ self.namespace = "test_ray"
+ self.lookup = _MockLookup({
+ "head": "1.0.0.0",
+ "w1": "1.0.0.1",
+ "w2": "1.0.0.2"
+ })
+ self.process_runner = _MockProcessRunner()
+ self.local_dir = "/tmp/local"
+ self.remote_dir = "/tmp/remote"
+
+ def tearDown(self):
+ pass
+
+ def testKubernetesCpUpDown(self):
+ syncer = _create_mock_syncer(
+ self.namespace, self.lookup, False, self.process_runner,
+ self.lookup.get_ip("head"), self.local_dir, self.remote_dir)
+
+ syncer.set_worker_ip(self.lookup.get_ip("w1"))
+
+ # Test sync up. Should add / to the dirs and call kubectl cp
+ syncer.sync_up()
+
+ self.assertEqual(self.process_runner.history[-1], [
+ "kubectl", "-n", self.namespace, "cp", self.local_dir + "/",
+ "{}/{}:{}".format(self.namespace, "w1", self.remote_dir + "/")
+ ])
+
+ # Test sync down.
+ syncer.sync_down()
+ self.assertEqual(self.process_runner.history[-1], [
+ "kubectl", "-n", self.namespace, "cp", "{}/{}:{}".format(
+ self.namespace,
+ "w1",
+ self.remote_dir + "/",
+ ), self.local_dir + "/"
+ ])
+
+ # Sync to same node should be ignored
+ syncer.set_worker_ip(self.lookup.get_ip("head"))
+ syncer.sync_up()
+ self.assertTrue(len(self.process_runner.history) == 2)
+
+ syncer.sync_down()
+ self.assertTrue(len(self.process_runner.history) == 2)
+
+ def testKubernetesRsyncUpDown(self):
+ syncer = _create_mock_syncer(
+ self.namespace, self.lookup, True, self.process_runner,
+ self.lookup.get_ip("head"), self.local_dir, self.remote_dir)
+
+ syncer.set_worker_ip(self.lookup.get_ip("w1"))
+
+ # Test sync up. Should add / to the dirs and call rsync
+ syncer.sync_up()
+ self.assertEqual(self.process_runner.history[-1][0], KUBECTL_RSYNC)
+ self.assertEqual(self.process_runner.history[-1][-2],
+ self.local_dir + "/")
+ self.assertEqual(
+ self.process_runner.history[-1][-1], "{}@{}:{}".format(
+ "w1", self.namespace, self.remote_dir + "/"))
+
+ # Test sync down.
+ syncer.sync_down()
+ self.assertEqual(self.process_runner.history[-1][0], KUBECTL_RSYNC)
+ self.assertEqual(
+ self.process_runner.history[-1][-2], "{}@{}:{}".format(
+ "w1", self.namespace, self.remote_dir + "/"))
+ self.assertEqual(self.process_runner.history[-1][-1],
+ self.local_dir + "/")
+
+ # Sync to same node should be ignored
+ syncer.set_worker_ip(self.lookup.get_ip("head"))
+ syncer.sync_up()
+ self.assertTrue(len(self.process_runner.history) == 2)
+
+ syncer.sync_down()
+ self.assertTrue(len(self.process_runner.history) == 2)
+
+
+if __name__ == "__main__":
+ import pytest
+ import sys
+ sys.exit(pytest.main(["-v", __file__]))
| [tune] Ray Cluster deployed in Kubernetes unable to sync checkpoint directories to the driver node
### What is the problem?
Getting the following error (Stacktrace attached):
```
2020-07-17 21:53:48,101 ERROR trial_runner.py:550 -- Trial TrainExample_fd24b_00001: Error handling checkpoint /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/ray/tune/trial_runner.py", line 546, in _process_trial_save
trial.on_checkpoint(trial.saving_to)
File "/opt/conda/lib/python3.6/site-packages/ray/tune/trial.py", line 448, in on_checkpoint
self, checkpoint.value))
ray.tune.error.TuneError: Trial TrainExample_fd24b_00001: Checkpoint path /root/ray_results/TrainExample/TrainExample_1_randomforestclassifier__n_estimators=5_2020-07-17_21-53-462l3hkjfs/checkpoint_1/ not found after successful sync down.
```
This is output of `ray_random_forest.py`. From investigation it looks like the checkpoint directories ie. **checkpoint_1, checkpoint_2** that were created in other nodes (workers) of the cluster do not get synced back to the driver node (from where I am running the python script).
Rest of the files in the Trial directories seem to be in sync.
This problem is not reproducible by running it on a single-machine with a cluster started up using bare `init()`. My guess is because all the workers point to the same file-system, and thus, the `os.path.exist()` returns True (This is reference to the line of code which checks and throws the above error)
Taking a look at `validate_save_restore()` to validate a Trainable, I have written a bare-bone script which proves that `checkpoint` directories are not synced across workers.
The stacktrace from `checkpoint_validation_failed.py`
```
Traceback (most recent call last):
File "code/checkpoint_validation_failed.py", line 115, in <module>
_main()
File "code/checkpoint_validation_failed.py", line 94, in _main
print(f"VALIDATE TRAINABLE CLASS: {validate_save_restore(TrainExample)}")
File "code/checkpoint_validation_failed.py", line 67, in validate_save_restore
restore_check = ray.get(trainable_2.restore.remote(old_trainable))
File "/opt/conda/lib/python3.6/site-packages/ray/worker.py", line 1526, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(FileNotFoundError): ray::TrainExample.restore() (pid=2704, ip=172.17.0.8)
File "python/ray/_raylet.pyx", line 471, in ray._raylet.execute_task
File "python/ray/_raylet.pyx", line 424, in ray._raylet.execute_task.function_executor
File "/opt/conda/lib/python3.6/site-packages/ray/tune/trainable.py", line 444, in restore
with open(checkpoint_path + ".tune_metadata", "rb") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/root/ray_results/2020-07-17_21-43-17dbvo01tp/checkpoint_3/.tune_metadata'
command terminated with exit code 1
```
Here, Worker X is trying to restore checkpoint created by Worker Y.
*Ray version and other system information (Python version, TensorFlow version, OS):*
Setup:
- Ray cluster running in K8s.
- Ray version
`pip list | grep ray
ray 0.9.0.dev0`
- Docker image:
`
REPOSITORY TAG IMAGE ID CREATED SIZE
rayproject/autoscaler latest 3f8d747cb8af 7 days ago 5.25GB
`
Playing with `sync_to_driver`, `sync_on_checkpoint` parameters did not help.
Thanks!
### Reproduction (REQUIRED)
Step 1. Spin-up a Ray cluster using `ray-cluster.yaml` in a namespace called `ray`. ie
1. `kubectl create -f ray-namespace.yaml`
2. `kubectl apply -f ray-cluster.yaml`
Step 2. Copy the python script to any worker node.
1. `kubectl -n ray get pods`.
2. Copy WORKER_1_POD_NAME
3. Exec into pod `kubectl -n ray exec -it <WORKER_1_POD_NAME> -- bin/bash`
4. `mkdir code`
5. Copy the python script `kubectl -n ray cp checkpoint_validation_failed.py <WORKER_1_POD_NAME>:/code/checkpoint_validation_failed.py`
6. Exit pod
7. Execute script `kubectl -n ray exec <WORKER_1_POD_NAME> -- bin/bash -c "python code/checkpoint_validation_failed.py`
You can do the same steps to run `ray_random_forest.py`.
[ray_issue_related_files.zip](https://github.com/ray-project/ray/files/4940566/ray_issue_related_files.zip)
The above zip contains:
1. ray-cluster.yaml
2. ray-namespace.yaml
3. checkpoint_validation_failed.py
4. ray_random_forest.py
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| Do you have an NFS for your kubernetes cluster? That might save you a lot of trouble.
Hello @richardliaw,
Thank you for a quick reply.
No, I do not have NFS for the cluster. Does it have to be a NFS? How does ray sync files like
1. events.out.tfevents.1595022826.ray-worker-6d9f6b9b8d-g4zvk
2. params.json
3. params.pkl
4. progress.csv
5. result.json
But not `checkpoint_1`, `checkpoint_2` etc.?
The files are actually generated on the driver, not on the node. Do you have access to S3? S3 could work too.
Okay. So what gets synced on `sync_on_checkpoint`?
sync_on_checkpoint (bool) – Force sync-down of trial **checkpoint** to driver. If set to
False, checkpoint syncing from worker to driver is asynchronous and best-effort. This does
not affect persistent storage syncing. Defaults to True.
In the above documentation snippet I am interpreting **checkpoint** as the `checkpoint_N` directories described above.
Would the suggested S3 solution involve implementing DurableTrainable rather than Trainable?
Also I wanted to clarify, is the above issue a bug in Ray Tune and a network/cloud storage a work-around for it?
Checkpoints (directories) are synced on sync_on_checkpoint, yep.
The above is just a limitation of our file-transfer mechanism; we use rsync to move files between VMs, but I think that mechanism breaks down on kubernetes.
The two approaches to workaround this limitation are to use 1. a networked storage or 2. to implement your own file-transfer protocol (check out `sync_to_driver`).
Does that make sense? Let me know if you have any further questions!
Thank you Richard for this clarification.
Upon searching more, I found this: https://serverfault.com/questions/741670/rsync-files-to-a-kubernetes-pod
I see similar solution applied to `KubernetesCommandRunner` in Ray. Maybe we can do the same for sync-checkpoint mechanism to resolve this issue. Or, we just implement it as a custom file-transfer protocol and make it part of the pallet.
On a side note, I have taken your suggestion to use a NFS. It has unblocked me from aggregating all my checkpoints.
Thanks once again.
Great! yeah, KubernetesCommandRunner is what we will want to use. More than happy to accept a PR if you want to take a quick pass!
Otherwise, we'll try to get to this in the next couple of weeks.
I spent sometime last-week to understand if I can fix it, but for me it was not a going to be a quick pass. So I will defer it to the team. Thanks once again Richard. :) | 2020-08-13T10:21:24 |
ray-project/ray | 10,122 | ray-project__ray-10122 | [
"10117"
] | c7adb464e49db7309cf4084d698dae7968b8e2e3 | diff --git a/rllib/offline/off_policy_estimator.py b/rllib/offline/off_policy_estimator.py
--- a/rllib/offline/off_policy_estimator.py
+++ b/rllib/offline/off_policy_estimator.py
@@ -1,10 +1,13 @@
from collections import namedtuple
import logging
+import numpy as np
+
from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
from ray.rllib.policy import Policy
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.offline.io_context import IOContext
+from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.typing import TensorType, SampleBatchType
from typing import List
@@ -53,7 +56,7 @@ def estimate(self, batch: SampleBatchType):
raise NotImplementedError
@DeveloperAPI
- def action_prob(self, batch: SampleBatchType) -> TensorType:
+ def action_prob(self, batch: SampleBatchType) -> np.ndarray:
"""Returns the probs for the batch actions for the current policy."""
num_state_inputs = 0
@@ -61,13 +64,13 @@ def action_prob(self, batch: SampleBatchType) -> TensorType:
if k.startswith("state_in_"):
num_state_inputs += 1
state_keys = ["state_in_{}".format(i) for i in range(num_state_inputs)]
- log_likelihoods = self.policy.compute_log_likelihoods(
+ log_likelihoods: TensorType = self.policy.compute_log_likelihoods(
actions=batch[SampleBatch.ACTIONS],
obs_batch=batch[SampleBatch.CUR_OBS],
state_batches=[batch[k] for k in state_keys],
prev_action_batch=batch.data.get(SampleBatch.PREV_ACTIONS),
prev_reward_batch=batch.data.get(SampleBatch.PREV_REWARDS))
- return log_likelihoods
+ return convert_to_numpy(log_likelihoods)
@DeveloperAPI
def process(self, batch: SampleBatchType):
| [rllib] Off Policy Estimation breaks with GPU on PyTorch (MARWIL) (Offline API)
### What is the problem?
When I use MARWIL with PyTorch and num_gpus: 1, I get an error when computing metrics. This happens because in off policy estimation it uses the torch tensors on gpu instead of numpy arrays. Particularly, I use "input_evaluation": ["is", "wis"] and the error goes away when "input_evaluation": ["simulation"]
```
Traceback (most recent call last):
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\tune\trial_runner.py", line 497, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\tune\ray_trial_executor.py", line 434, in fetch_result
result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\worker.py", line 1553, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::MARWIL.train() (pid=9136, ip=10.0.0.18)
File "python\ray\_raylet.pyx", line 474, in ray._raylet.execute_task
File "python\ray\_raylet.pyx", line 427, in ray._raylet.execute_task.function_executor
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\function_manager.py", line 567, in actor_method_executor
raise e
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\function_manager.py", line 559, in actor_method_executor
method_returns = method(actor, *args, **kwargs)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\rllib\agents\trainer.py", line 522, in train
raise e
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\rllib\agents\trainer.py", line 508, in train
result = Trainable.train(self)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\tune\trainable.py", line 337, in train
result = self.step()
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\rllib\agents\trainer_template.py", line 110, in step
res = next(self.train_exec_impl)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\util\iter.py", line 758, in __next__
return next(self.built_iterator)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\util\iter.py", line 793, in apply_foreach
result = fn(item)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\rllib\execution\metric_ops.py", line 87, in __call__
res = summarize_episodes(episodes, orig_episodes)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\ray\rllib\evaluation\metrics.py", line 173, in summarize_episodes
metrics[k] = np.mean(v_list)
File "<__array_function__ internals>", line 6, in mean
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\numpy\core\fromnumeric.py", line 3335, in mean
out=out, **kwargs)
File "C:\Users\Julius\Anaconda3\envs\ray\lib\site-packages\numpy\core\_methods.py", line 161, in _mean
ret = ret.dtype.type(ret / rcount)
AttributeError: 'torch.dtype' object has no attribute 'type'
```
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| Hmm I think we are missing a call to `.cpu().numpy()` somewhere and are trying to calculate a np.mean() over torch tensors representing a single element. Do you think you could look into this issue? | 2020-08-14T23:29:31 |
|
ray-project/ray | 10,221 | ray-project__ray-10221 | [
"8894"
] | d206fbbc991dff60d152151ef19af5412f6774b1 | diff --git a/python/ray/worker.py b/python/ray/worker.py
--- a/python/ray/worker.py
+++ b/python/ray/worker.py
@@ -1806,7 +1806,21 @@ def decorator(function_or_class):
if max_task_retries is not None:
raise ValueError("The keyword 'max_task_retries' is not "
"allowed for remote functions.")
-
+ if num_return_vals is not None and (not isinstance(
+ num_return_vals, int) or num_return_vals < 0):
+ raise ValueError(
+ "The keyword 'num_return_vals' only accepts 0 or a"
+ " positive integer")
+ if max_retries is not None and (not isinstance(max_retries, int)
+ or max_retries < -1):
+ raise ValueError(
+ "The keyword 'max_retries' only accepts 0, -1 or a"
+ " positive integer")
+ if max_calls is not None and (not isinstance(max_calls, int)
+ or max_calls < 0):
+ raise ValueError(
+ "The keyword 'max_calls' only accepts 0 or a positive"
+ " integer")
return ray.remote_function.RemoteFunction(
Language.PYTHON, function_or_class, None, num_cpus, num_gpus,
memory, object_store_memory, resources, num_return_vals,
@@ -1820,7 +1834,16 @@ def decorator(function_or_class):
if max_calls is not None:
raise TypeError("The keyword 'max_calls' is not "
"allowed for actors.")
-
+ if max_restarts is not None and (not isinstance(max_restarts, int)
+ or max_restarts < -1):
+ raise ValueError(
+ "The keyword 'max_restarts' only accepts -1, 0 or a"
+ " positive integer")
+ if max_task_retries is not None and (not isinstance(
+ max_task_retries, int) or max_task_retries < -1):
+ raise ValueError(
+ "The keyword 'max_task_retries' only accepts -1, 0 or a"
+ " positive integer")
return ray.actor.make_actor(function_or_class, num_cpus, num_gpus,
memory, object_store_memory, resources,
max_restarts, max_task_retries)
| diff --git a/python/ray/tests/test_basic.py b/python/ray/tests/test_basic.py
--- a/python/ray/tests/test_basic.py
+++ b/python/ray/tests/test_basic.py
@@ -111,6 +111,60 @@ def method(self):
assert ray.get([id1, id2, id3, id4]) == [0, 1, "test", 2]
+def test_invalid_arguments(shutdown_only):
+ ray.init(num_cpus=2)
+
+ for opt in [np.random.randint(-100, -1), np.random.uniform(0, 1)]:
+ with pytest.raises(
+ ValueError,
+ match="The keyword 'num_return_vals' only accepts 0 or a"
+ " positive integer"):
+
+ @ray.remote(num_return_vals=opt)
+ def g1():
+ return 1
+
+ for opt in [np.random.randint(-100, -2), np.random.uniform(0, 1)]:
+ with pytest.raises(
+ ValueError,
+ match="The keyword 'max_retries' only accepts 0, -1 or a"
+ " positive integer"):
+
+ @ray.remote(max_retries=opt)
+ def g2():
+ return 1
+
+ for opt in [np.random.randint(-100, -1), np.random.uniform(0, 1)]:
+ with pytest.raises(
+ ValueError,
+ match="The keyword 'max_calls' only accepts 0 or a positive"
+ " integer"):
+
+ @ray.remote(max_calls=opt)
+ def g3():
+ return 1
+
+ for opt in [np.random.randint(-100, -2), np.random.uniform(0, 1)]:
+ with pytest.raises(
+ ValueError,
+ match="The keyword 'max_restarts' only accepts -1, 0 or a"
+ " positive integer"):
+
+ @ray.remote(max_restarts=opt)
+ class A1:
+ x = 1
+
+ for opt in [np.random.randint(-100, -2), np.random.uniform(0, 1)]:
+ with pytest.raises(
+ ValueError,
+ match="The keyword 'max_task_retries' only accepts -1, 0 or a"
+ " positive integer"):
+
+ @ray.remote(max_task_retries=opt)
+ class A2:
+ x = 1
+
+
def test_many_fractional_resources(shutdown_only):
ray.init(num_cpus=2, num_gpus=2, resources={"Custom": 2})
| No proper error message when num_return_vals is negative or not integral
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
*Ubuntu 18.04.4 LTS*
*Python 3.7.7*
*ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl*
* For non integral values of `x`, eg. `x = 3.2` in the code below, ray doesn't give a proper error message. Instead the worker dies. This also happens when `x` is not equal to the true num return values, eg. `x = 5`.
`
ray.exceptions.RayWorkerError: The worker died unexpectedly while executing this task.
`
* For negative values of `x`, eg. `x = -2` the kernel dies altogether.
` Aborted (core dumped)`
Although one would not expect the user to enter such values. But still.
### Reproduction
```
import ray
ray.init()
def f(a):
return a, 2
x = <some_value>
g = ray.remote(num_return_vals = x)(f)
print(ray.get(g.remote(3)))
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| @vinamrabenara thanks for catching this! | 2020-08-20T10:26:48 |
ray-project/ray | 10,323 | ray-project__ray-10323 | [
"10319"
] | b14c56e599ea34dd88838c820151063b1aeba0e8 | diff --git a/python/ray/dashboard/node_stats.py b/python/ray/dashboard/node_stats.py
--- a/python/ray/dashboard/node_stats.py
+++ b/python/ray/dashboard/node_stats.py
@@ -60,14 +60,22 @@ def __init__(self, redis_address, redis_password=None):
def _insert_log_counts(self):
for ip, logs_by_pid in self._logs.items():
hostname = self._ip_to_hostname[ip]
- logs_by_pid = {pid: len(logs) for pid, logs in logs_by_pid.items()}
- self._node_stats[hostname]["log_count"] = logs_by_pid
+ if hostname in self._node_stats:
+ logs_by_pid = {
+ pid: len(logs)
+ for pid, logs in logs_by_pid.items()
+ }
+ self._node_stats[hostname]["log_count"] = logs_by_pid
def _insert_error_counts(self):
for ip, errs_by_pid in self._errors.items():
hostname = self._ip_to_hostname[ip]
- errs_by_pid = {pid: len(errs) for pid, errs in errs_by_pid.items()}
- self._node_stats[hostname]["error_count"] = errs_by_pid
+ if hostname in self._node_stats:
+ errs_by_pid = {
+ pid: len(errs)
+ for pid, errs in errs_by_pid.items()
+ }
+ self._node_stats[hostname]["error_count"] = errs_by_pid
def _purge_outdated_stats(self):
def current(then, now):
| [dashboard] When autoscaler adds/removes worker, dashboard crashes
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
This is originally reported by Pieterjan for 0.8.7 but also appears to be in issue in the latest nightly. The issue would appear to occur across
### Reproduction (REQUIRED)
This is not a script, but given I will author the fix, I think this is sufficient:
- Start a cluster with autoscaling enabled
- Increase workload so that an additional worker or workers are spawned
- When one of those workers is removed, their log entries still exist on the backend
- The following stack trace occurs and crashes the dashboard: `/ray/dashboard/node_stats.py", line 63, in _insert_log_counts
self._node_stats[hostname]["log_count"] = logs_by_pid`
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-08-25T18:27:35 |
||
ray-project/ray | 10,443 | ray-project__ray-10443 | [
"10228"
] | eb025ea8cb27583e8ef6287f5654f23d1ab270ef | diff --git a/rllib/utils/exploration/stochastic_sampling.py b/rllib/utils/exploration/stochastic_sampling.py
--- a/rllib/utils/exploration/stochastic_sampling.py
+++ b/rllib/utils/exploration/stochastic_sampling.py
@@ -72,5 +72,5 @@ def _get_torch_exploration_action(action_dist, explore):
logp = action_dist.sampled_action_logp()
else:
action = action_dist.deterministic_sample()
- logp = torch.zeros((action.size()[0], ), dtype=torch.float32)
+ logp = torch.zeros_like(action_dist.sampled_action_logp())
return action, logp
| [rllib] _get_torch_exploration_action doesn't support tuple action dist
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### System information
* **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Mac OS 10.15.4
* **Ray installed from (source or binary)**: binary (via pip)
* **Ray version**: 0.8.6., but nothing seems to have changed on master
* **Python version**: 3.7
### What is the problem?
When using tuple action distributions (as advised in #6372) and exploration is disabled, the line:
https://github.com/ray-project/ray/blob/a462ae2747afbeb9047e443cd51e67e3fe0b49e6/rllib/utils/exploration/stochastic_sampling.py#L75
from `_get_torch_exploration_action` raises the following exception:
```
AttributeError: 'tuple' object has no attribute 'size'
```
A simple fix that supports any type of distribution would be:
```python
logp = torch.zeros_like(action_dist.sampled_action_logp())
```
I can submit a PR if it helps.
### Reproduction (REQUIRED)
Exact command to reproduce: python `rllib_cartpole.py` for the following file
```python
import gym.envs.classic_control
from gym.spaces import Tuple, Discrete
import ray
from ray import tune
class CustomCartpole(gym.envs.classic_control.CartPoleEnv):
"""Add a dimension to the cartpole action space that is ignored."""
def __init__(self, env_config):
super().__init__()
# if override_actions is false this is just the Cartpole environment
self.override_actions = env_config['override_actions']
if self.override_actions:
# 2 is the environment's normal action space
# 4 is just a dummy number to give it an extra dimension
self.original_action_space = self.action_space
self.action_space = Tuple([Discrete(2), Discrete(4)])
self.tuple_action_space = self.action_space
def step(self, action):
# call the cartpole environment with the original action
if self.override_actions:
self.action_space = self.original_action_space
return super().step(action[0])
else:
return super().step(action)
def main():
ray.init()
tune.run(
"PPO",
stop={"episode_reward_mean": 50},
config={
"env": CustomCartpole,
"env_config": {'override_actions': True},
"num_gpus": 0,
"num_workers": 1,
"eager": False,
"evaluation_interval": 1,
"evaluation_config": {
"explore": False,
},
"framework": "torch",
},
)
if __name__ == '__main__':
main()
```
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| The proposed fix makes sense to me. We could alternatively try to get the batch dimension of the tuple, but I don't see an existing helper method for that, so your proposal is probably simpler.
And yeah, a PR would be great! | 2020-08-31T11:43:42 |
|
ray-project/ray | 10,449 | ray-project__ray-10449 | [
"10371"
] | 715ee8dfc95ab44909f800e8b917e7e44082a6ec | diff --git a/python/ray/actor.py b/python/ray/actor.py
--- a/python/ray/actor.py
+++ b/python/ray/actor.py
@@ -100,7 +100,27 @@ def __call__(self, *args, **kwargs):
def remote(self, *args, **kwargs):
return self._remote(args, kwargs)
- def _remote(self, args=None, kwargs=None, num_returns=None):
+ def options(self, **options):
+ """Convenience method for executing an actor method call with options.
+
+ Same arguments as func._remote(), but returns a wrapped function
+ that a non-underscore .remote() can be called on.
+
+ Examples:
+ # The following two calls are equivalent.
+ >>> actor.my_method._remote(args=[x, y], name="foo", num_returns=2)
+ >>> actor.my_method.options(name="foo", num_returns=2).remote(x, y)
+ """
+
+ func_cls = self
+
+ class FuncWrapper:
+ def remote(self, *args, **kwargs):
+ return func_cls._remote(args=args, kwargs=kwargs, **options)
+
+ return FuncWrapper()
+
+ def _remote(self, args=None, kwargs=None, name="", num_returns=None):
if num_returns is None:
num_returns = self._num_returns
@@ -112,6 +132,7 @@ def invocation(args, kwargs):
self._method_name,
args=args,
kwargs=kwargs,
+ name=name,
num_returns=num_returns)
# Apply the decorator if there is one.
@@ -317,8 +338,10 @@ def _ray_from_modified_class(cls, modified_class, class_id, max_restarts,
max_task_retries, num_cpus, num_gpus, memory,
object_store_memory, resources):
for attribute in [
- "remote", "_remote", "_ray_from_modified_class",
- "_ray_from_function_descriptor"
+ "remote",
+ "_remote",
+ "_ray_from_modified_class",
+ "_ray_from_function_descriptor",
]:
if hasattr(modified_class, attribute):
logger.warning("Creating an actor from class "
@@ -679,6 +702,7 @@ def _actor_method_call(self,
method_name,
args=None,
kwargs=None,
+ name="",
num_returns=None):
"""Method execution stub for an actor handle.
@@ -691,6 +715,7 @@ def _actor_method_call(self,
method_name: The name of the actor method to execute.
args: A list of arguments for the actor method.
kwargs: A dictionary of keyword arguments for the actor method.
+ name (str): The name to give the actor method call task.
num_returns (int): The number of return values for the method.
Returns:
@@ -724,7 +749,7 @@ def _actor_method_call(self,
object_refs = worker.core_worker.submit_actor_task(
self._ray_actor_language, self._ray_actor_id, function_descriptor,
- list_args, num_returns, self._ray_actor_method_cpus)
+ list_args, name, num_returns, self._ray_actor_method_cpus)
if len(object_refs) == 1:
object_refs = object_refs[0]
diff --git a/python/ray/remote_function.py b/python/ray/remote_function.py
--- a/python/ray/remote_function.py
+++ b/python/ray/remote_function.py
@@ -152,7 +152,8 @@ def _remote(self,
resources=None,
max_retries=None,
placement_group=None,
- placement_group_bundle_index=-1):
+ placement_group_bundle_index=-1,
+ name=""):
"""Submit the remote function for execution."""
worker = ray.worker.global_worker
worker.check_connected()
@@ -212,7 +213,7 @@ def invocation(args, kwargs):
"Cross language remote function " \
"cannot be executed locally."
object_refs = worker.core_worker.submit_task(
- self._language, self._function_descriptor, list_args,
+ self._language, self._function_descriptor, list_args, name,
num_returns, resources, max_retries, placement_group.id,
placement_group_bundle_index)
| diff --git a/java/test/src/main/java/io/ray/test/TaskNameTest.java b/java/test/src/main/java/io/ray/test/TaskNameTest.java
new file mode 100644
--- /dev/null
+++ b/java/test/src/main/java/io/ray/test/TaskNameTest.java
@@ -0,0 +1,19 @@
+package io.ray.test;
+
+import io.ray.api.Ray;
+import org.testng.Assert;
+import org.testng.annotations.Test;
+
+/** Task Name Test. */
+public class TaskNameTest extends BaseTest {
+
+ private static int testFoo() {
+ return 0;
+ }
+
+ /** Test setting task name at task submission time. */
+ @Test
+ public void testSetName() {
+ Assert.assertEquals(0, (int) Ray.task(TaskNameTest::testFoo).setName("foo").remote().get());
+ }
+}
diff --git a/python/ray/tests/test_actor.py b/python/ray/tests/test_actor.py
--- a/python/ray/tests/test_actor.py
+++ b/python/ray/tests/test_actor.py
@@ -14,6 +14,10 @@
import ray.test_utils
import ray.cluster_utils
+# NOTE: We have to import setproctitle after ray because we bundle setproctitle
+# with ray.
+import setproctitle
+
def test_caching_actors(shutdown_only):
# Test defining actors before ray.init() has been called.
@@ -673,6 +677,33 @@ def method3(self):
assert ray.get([id3a, id3b, id3c]) == [1, 2, 3]
+def test_options_num_returns(ray_start_regular_shared):
+ @ray.remote
+ class Foo:
+ def method(self):
+ return 1, 2
+
+ f = Foo.remote()
+
+ obj = f.method.remote()
+ assert ray.get(obj) == (1, 2)
+
+ obj1, obj2 = f.method.options(num_returns=2).remote()
+ assert ray.get([obj1, obj2]) == [1, 2]
+
+
+def test_options_name(ray_start_regular_shared):
+ @ray.remote
+ class Foo:
+ def method(self, name):
+ assert setproctitle.getproctitle() == f"ray::{name}"
+
+ f = Foo.remote()
+
+ ray.get(f.method.options(name="foo").remote("foo"))
+ ray.get(f.method.options(name="bar").remote("bar"))
+
+
def test_define_actor(ray_start_regular_shared):
@ray.remote
class Test:
diff --git a/python/ray/tests/test_advanced_3.py b/python/ray/tests/test_advanced_3.py
--- a/python/ray/tests/test_advanced_3.py
+++ b/python/ray/tests/test_advanced_3.py
@@ -35,7 +35,7 @@ def attempt_to_load_balance(remote_function,
[remote_function.remote(*args) for _ in range(total_tasks)])
names = set(locations)
counts = [locations.count(name) for name in names]
- logger.info("Counts are {}.".format(counts))
+ logger.info(f"Counts are {counts}.")
if (len(names) == num_nodes
and all(count >= minimum_count for count in counts)):
break
@@ -346,6 +346,28 @@ def unique_1():
ray.get(unique_1.remote())
+def test_ray_task_name_setproctitle(ray_start_2_cpus):
+ method_task_name = "foo"
+
+ @ray.remote
+ class UniqueName:
+ def __init__(self):
+ assert setproctitle.getproctitle() == "ray::UniqueName.__init__()"
+
+ def f(self):
+ assert setproctitle.getproctitle() == f"ray::{method_task_name}"
+
+ task_name = "bar"
+
+ @ray.remote
+ def unique_1():
+ assert task_name in setproctitle.getproctitle()
+
+ actor = UniqueName.remote()
+ ray.get(actor.f.options(name=method_task_name).remote())
+ ray.get(unique_1.options(name=task_name).remote())
+
+
@pytest.mark.skipif(
os.getenv("TRAVIS") is None,
reason="This test should only be run on Travis.")
@@ -508,7 +530,7 @@ def test_invalid_unicode_in_worker_log(shutdown_only):
# Wait till first worker log file is created.
while True:
- log_file_paths = glob.glob("{}/worker*.out".format(logs_dir))
+ log_file_paths = glob.glob(f"{logs_dir}/worker*.out")
if len(log_file_paths) == 0:
time.sleep(0.2)
else:
@@ -546,13 +568,13 @@ def f(self):
# Make sure no log files are in the "old" directory before the actors
# are killed.
- assert len(glob.glob("{}/old/worker*.out".format(logs_dir))) == 0
+ assert len(glob.glob(f"{logs_dir}/old/worker*.out")) == 0
# Now kill the actors so the files get moved to logs/old/.
[a.__ray_terminate__.remote() for a in actors]
while True:
- log_file_paths = glob.glob("{}/old/worker*.out".format(logs_dir))
+ log_file_paths = glob.glob(f"{logs_dir}/old/worker*.out")
if len(log_file_paths) > 0:
with open(log_file_paths[0], "r") as f:
assert "function f finished\n" in f.readlines()
@@ -641,7 +663,7 @@ def test_gpu_info_parsing():
"""
constraints_dict = resource_spec._constraints_from_gpu_info(info_string)
expected_dict = {
- "{}V100".format(ray_constants.RESOURCE_CONSTRAINT_PREFIX): 1
+ f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}V100": 1,
}
assert constraints_dict == expected_dict
@@ -658,7 +680,7 @@ def test_gpu_info_parsing():
"""
constraints_dict = resource_spec._constraints_from_gpu_info(info_string)
expected_dict = {
- "{}T4".format(ray_constants.RESOURCE_CONSTRAINT_PREFIX): 1
+ f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}T4": 1,
}
assert constraints_dict == expected_dict
diff --git a/src/ray/core_worker/test/core_worker_test.cc b/src/ray/core_worker/test/core_worker_test.cc
--- a/src/ray/core_worker/test/core_worker_test.cc
+++ b/src/ray/core_worker/test/core_worker_test.cc
@@ -226,7 +226,7 @@ bool CoreWorkerTest::WaitForDirectCallActorState(const ActorID &actor_id, bool w
int CoreWorkerTest::GetActorPid(const ActorID &actor_id,
std::unordered_map<std::string, double> &resources) {
std::vector<std::unique_ptr<TaskArg>> args;
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func{Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"GetWorkerPid", "", "", "")};
@@ -308,7 +308,7 @@ void CoreWorkerTest::TestActorTask(std::unordered_map<std::string, double> &reso
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer2, nullptr, std::vector<ObjectID>())));
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func(ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"MergeInputArgsAsOutput", "", "", ""));
@@ -350,7 +350,7 @@ void CoreWorkerTest::TestActorTask(std::unordered_map<std::string, double> &reso
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer2, nullptr, std::vector<ObjectID>())));
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func(ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"MergeInputArgsAsOutput", "", "", ""));
@@ -412,7 +412,7 @@ void CoreWorkerTest::TestActorRestart(
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer1, nullptr, std::vector<ObjectID>())));
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func(ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"MergeInputArgsAsOutput", "", "", ""));
@@ -455,7 +455,7 @@ void CoreWorkerTest::TestActorFailure(
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer1, nullptr, std::vector<ObjectID>())));
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func(ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"MergeInputArgsAsOutput", "", "", ""));
@@ -539,12 +539,12 @@ TEST_F(ZeroNodeTest, TestTaskSpecPerf) {
RAY_LOG(INFO) << "start creating " << num_tasks << " PushTaskRequests";
rpc::Address address;
for (int i = 0; i < num_tasks; i++) {
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
auto num_returns = options.num_returns;
TaskSpecBuilder builder;
- builder.SetCommonTaskSpec(RandomTaskId(), function.GetLanguage(),
+ builder.SetCommonTaskSpec(RandomTaskId(), options.name, function.GetLanguage(),
function.GetFunctionDescriptor(), job_id, RandomTaskId(), 0,
RandomTaskId(), address, num_returns, resources, resources,
PlacementGroupID::Nil());
@@ -587,7 +587,7 @@ TEST_F(SingleNodeTest, TestDirectActorTaskSubmissionPerf) {
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer, nullptr, std::vector<ObjectID>())));
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func(ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"MergeInputArgsAsOutput", "", "", ""));
diff --git a/src/ray/core_worker/test/direct_task_transport_test.cc b/src/ray/core_worker/test/direct_task_transport_test.cc
--- a/src/ray/core_worker/test/direct_task_transport_test.cc
+++ b/src/ray/core_worker/test/direct_task_transport_test.cc
@@ -324,9 +324,10 @@ TaskSpecification BuildTaskSpec(const std::unordered_map<std::string, double> &r
const ray::FunctionDescriptor &function_descriptor) {
TaskSpecBuilder builder;
rpc::Address empty_address;
- builder.SetCommonTaskSpec(TaskID::Nil(), Language::PYTHON, function_descriptor,
- JobID::Nil(), TaskID::Nil(), 0, TaskID::Nil(), empty_address,
- 1, resources, resources, PlacementGroupID::Nil());
+ builder.SetCommonTaskSpec(TaskID::Nil(), "dummy_task", Language::PYTHON,
+ function_descriptor, JobID::Nil(), TaskID::Nil(), 0,
+ TaskID::Nil(), empty_address, 1, resources, resources,
+ PlacementGroupID::Nil());
return builder.Build();
}
diff --git a/src/ray/core_worker/test/mock_worker.cc b/src/ray/core_worker/test/mock_worker.cc
--- a/src/ray/core_worker/test/mock_worker.cc
+++ b/src/ray/core_worker/test/mock_worker.cc
@@ -50,8 +50,8 @@ class MockWorker {
"", // driver_name
"", // stdout_file
"", // stderr_file
- std::bind(&MockWorker::ExecuteTask, this, _1, _2, _3, _4, _5, _6,
- _7), // task_execution_callback
+ std::bind(&MockWorker::ExecuteTask, this, _1, _2, _3, _4, _5, _6, _7,
+ _8), // task_execution_callback
nullptr, // check_signals
nullptr, // gc_collect
nullptr, // spill_objects
@@ -71,7 +71,8 @@ class MockWorker {
void RunTaskExecutionLoop() { CoreWorkerProcess::RunTaskExecutionLoop(); }
private:
- Status ExecuteTask(TaskType task_type, const RayFunction &ray_function,
+ Status ExecuteTask(TaskType task_type, const std::string task_name,
+ const RayFunction &ray_function,
const std::unordered_map<std::string, double> &required_resources,
const std::vector<std::shared_ptr<RayObject>> &args,
const std::vector<ObjectID> &arg_reference_ids,
diff --git a/src/ray/gcs/test/gcs_test_util.h b/src/ray/gcs/test/gcs_test_util.h
--- a/src/ray/gcs/test/gcs_test_util.h
+++ b/src/ray/gcs/test/gcs_test_util.h
@@ -38,9 +38,10 @@ struct Mocker {
auto actor_id = ActorID::Of(job_id, RandomTaskId(), 0);
auto task_id = TaskID::ForActorCreationTask(actor_id);
auto resource = std::unordered_map<std::string, double>();
- builder.SetCommonTaskSpec(task_id, Language::PYTHON, empty_descriptor, job_id,
- TaskID::Nil(), 0, TaskID::Nil(), owner_address, 1, resource,
- resource, PlacementGroupID::Nil());
+ builder.SetCommonTaskSpec(task_id, name + ":" + empty_descriptor->CallString(),
+ Language::PYTHON, empty_descriptor, job_id, TaskID::Nil(),
+ 0, TaskID::Nil(), owner_address, 1, resource, resource,
+ PlacementGroupID::Nil());
builder.SetActorCreationTaskSpec(actor_id, max_restarts, {}, 1, detached, name);
return builder.Build();
}
diff --git a/src/ray/raylet/scheduling/cluster_task_manager_test.cc b/src/ray/raylet/scheduling/cluster_task_manager_test.cc
--- a/src/ray/raylet/scheduling/cluster_task_manager_test.cc
+++ b/src/ray/raylet/scheduling/cluster_task_manager_test.cc
@@ -264,7 +264,7 @@ Task CreateTask(const std::unordered_map<std::string, double> &required_resource
TaskID id = RandomTaskId();
JobID job_id = RandomJobId();
rpc::Address address;
- spec_builder.SetCommonTaskSpec(id, Language::PYTHON,
+ spec_builder.SetCommonTaskSpec(id, "dummy_task", Language::PYTHON,
FunctionDescriptorBuilder::BuildPython("", "", "", ""),
job_id, TaskID::Nil(), 0, TaskID::Nil(), address, 0,
required_resources, {}, PlacementGroupID::Nil());
@@ -489,4 +489,4 @@ int main(int argc, char **argv) {
}
} // namespace raylet
-} // namespace ray
\ No newline at end of file
+} // namespace ray
diff --git a/src/ray/raylet/task_dependency_manager_test.cc b/src/ray/raylet/task_dependency_manager_test.cc
--- a/src/ray/raylet/task_dependency_manager_test.cc
+++ b/src/ray/raylet/task_dependency_manager_test.cc
@@ -65,7 +65,7 @@ static inline Task ExampleTask(const std::vector<ObjectID> &arguments,
uint64_t num_returns) {
TaskSpecBuilder builder;
rpc::Address address;
- builder.SetCommonTaskSpec(RandomTaskId(), Language::PYTHON,
+ builder.SetCommonTaskSpec(RandomTaskId(), "example_task", Language::PYTHON,
FunctionDescriptorBuilder::BuildPython("", "", "", ""),
JobID::Nil(), RandomTaskId(), 0, RandomTaskId(), address,
num_returns, {}, {}, PlacementGroupID::Nil());
diff --git a/streaming/src/test/mock_actor.cc b/streaming/src/test/mock_actor.cc
--- a/streaming/src/test/mock_actor.cc
+++ b/streaming/src/test/mock_actor.cc
@@ -496,8 +496,8 @@ class StreamingWorker {
"", // driver_name
"", // stdout_file
"", // stderr_file
- std::bind(&StreamingWorker::ExecuteTask, this, _1, _2, _3, _4, _5, _6,
- _7), // task_execution_callback
+ std::bind(&StreamingWorker::ExecuteTask, this, _1, _2, _3, _4, _5, _6, _7,
+ _8), // task_execution_callback
nullptr, // check_signals
nullptr, // gc_collect
nullptr, // spill_objects
@@ -521,7 +521,8 @@ class StreamingWorker {
}
private:
- Status ExecuteTask(TaskType task_type, const RayFunction &ray_function,
+ Status ExecuteTask(TaskType task_type, const std::string task_name,
+ const RayFunction &ray_function,
const std::unordered_map<std::string, double> &required_resources,
const std::vector<std::shared_ptr<RayObject>> &args,
const std::vector<ObjectID> &arg_reference_ids,
diff --git a/streaming/src/test/queue_tests_base.h b/streaming/src/test/queue_tests_base.h
--- a/streaming/src/test/queue_tests_base.h
+++ b/streaming/src/test/queue_tests_base.h
@@ -87,7 +87,7 @@ class StreamingQueueTestBase : public ::testing::TestWithParam<uint64_t> {
args.emplace_back(new TaskArgByValue(std::make_shared<RayObject>(
msg.ToBytes(), nullptr, std::vector<ObjectID>(), true)));
std::unordered_map<std::string, double> resources;
- TaskOptions options{0, resources};
+ TaskOptions options{"", 0, resources};
std::vector<ObjectID> return_ids;
RayFunction func{ray::Language::PYTHON,
ray::FunctionDescriptorBuilder::BuildPython("", "", "init", "")};
@@ -103,7 +103,7 @@ class StreamingQueueTestBase : public ::testing::TestWithParam<uint64_t> {
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer, nullptr, std::vector<ObjectID>(), true)));
std::unordered_map<std::string, double> resources;
- TaskOptions options{0, resources};
+ TaskOptions options{"", 0, resources};
std::vector<ObjectID> return_ids;
RayFunction func{ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"", test, "execute_test", "")};
@@ -119,7 +119,7 @@ class StreamingQueueTestBase : public ::testing::TestWithParam<uint64_t> {
args.emplace_back(new TaskArgByValue(
std::make_shared<RayObject>(buffer, nullptr, std::vector<ObjectID>(), true)));
std::unordered_map<std::string, double> resources;
- TaskOptions options{1, resources};
+ TaskOptions options{"", 1, resources};
std::vector<ObjectID> return_ids;
RayFunction func{ray::Language::PYTHON, ray::FunctionDescriptorBuilder::BuildPython(
"", "", "check_current_test_status", "")};
| [Core] Submission-time task names.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Feature Request
Allow the user to specify submission-time task names, such that the task name is used as the process title (for displaying in the dashboard and debugging) and possibly other areas (e.g. core worker stats). Using the task function name as the user-friendly identifier results in poor UX when many tasks are executed using the same remote function, especially when there exists a semantically meaningful name that could be attached to the task at submission time.
### API and Semantics
The user can specify a task name at submission time:
```python
@ray.remote
def foo(x):
return x + 1
foo.options(name="my_special_foo").remote(1)
```
Unlike Ray actor names, this would **not** "detach" the task, and global uniqueness of these task names would **not** be enforced. Given the existing semantics for `actor.options(name="foo").remote()`, I'm imagining that this new task name option would only apply for actor and non-actor tasks, not for actor creation.
When viewing the machine view of the Ray dashboard, the current task (in the workers/PID column) for a given core worker would be given by this task name, if set. The task name could also be included in other metrics/stats payloads, such as the core worker stats.
### Possible Implementation
I think that this semantically meaningful task name should be easy to propagate if there's a dedicated field on the `TaskSpec`. If the user supplies a `name` at task submission time, the `task_name` field is set to that; if not, it's set to the task function's name, the current behavior.
Flow - setting field in task spec + using said field to set proctitle:
1. Add a `task_name` field to the `TaskSpec` [class](https://github.com/ray-project/ray/blob/0178d6318ead643fff6d9ed873c88f71fdac52e7/src/ray/common/task/task_spec.h#L30-L216)/[proto](https://github.com/ray-project/ray/blob/fe6daef85e1f3cd2aef12b39487124a6e2dcc2b9/src/ray/protobuf/common.proto#L147-L189) and to the [`TaskOptions` class](https://github.com/ray-project/ray/blob/0178d6318ead643fff6d9ed873c88f71fdac52e7/src/ray/core_worker/common.h#L54-L64).
2. Properly set it on the task spec in [`CoreWorker::SubmitTask`](https://github.com/ray-project/ray/blob/0aec4cbccb5862a7808f98b90696236da9e2a03e/src/ray/core_worker/core_worker.cc#L1257-L1287) and [`CoreWorker::SubmitActorTask`](https://github.com/ray-project/ray/blob/0aec4cbccb5862a7808f98b90696236da9e2a03e/src/ray/core_worker/core_worker.cc#L1398-L1436) to either the user-provided `task_name` if given or set it to the function's name if not (current behavior).
3. Pull the `task_name` off of the `TaskSpec` class in [`CoreWorker::ExecuteTask`](https://github.com/ray-project/ray/blob/0aec4cbccb5862a7808f98b90696236da9e2a03e/src/ray/core_worker/core_worker.cc#L1635-L1757) and pass to language-specific task execution handler.
4. Use said `task_name` to [set the proctitle in the language-specific task execution handlers](https://github.com/ray-project/ray/blob/0aec4cbccb5862a7808f98b90696236da9e2a03e/python/ray/_raylet.pyx#L389).
### Concerns/Open Questions
1. Should the submission-time task name be appended or prepended to the function name, yielding something like `some_module.some_func:{task_name}` or `{task_name}:some_module.some_func`, instead of replacing the function name? Or should the user be expected to craft such a task name in their code and use _that_ as the `.options(name=task_name)` argument if that's what they want? I'm thinking the latter, but having to do that crafting for actor methods may be annoying.
2. Will this be confusing given the existence of named actors and the already established semantics thereof? Should a similar concept be provided for actors, and if so, how could we delineate between detached actors and named actors?
| Btw, from 1.0 (the next version), you can name an actor that is not detached! | 2020-08-31T17:52:59 |
ray-project/ray | 10,456 | ray-project__ray-10456 | [
"10451"
] | 91535e910201dc403cb2b8644a75e1132bd712fe | diff --git a/python/ray/tune/__init__.py b/python/ray/tune/__init__.py
--- a/python/ray/tune/__init__.py
+++ b/python/ray/tune/__init__.py
@@ -15,6 +15,8 @@
from ray.tune.sample import (function, sample_from, uniform, quniform, choice,
randint, qrandint, randn, qrandn, loguniform,
qloguniform)
+from ray.tune.suggest import create_searcher
+from ray.tune.schedulers import create_scheduler
__all__ = [
"Trainable", "DurableTrainable", "TuneError", "grid_search",
@@ -24,5 +26,5 @@
"loguniform", "qloguniform", "ExperimentAnalysis", "Analysis",
"CLIReporter", "JupyterNotebookReporter", "ProgressReporter", "report",
"get_trial_dir", "get_trial_name", "get_trial_id", "make_checkpoint_dir",
- "save_checkpoint", "checkpoint_dir"
+ "save_checkpoint", "checkpoint_dir", "create_searcher", "create_scheduler"
]
diff --git a/python/ray/tune/schedulers/__init__.py b/python/ray/tune/schedulers/__init__.py
--- a/python/ray/tune/schedulers/__init__.py
+++ b/python/ray/tune/schedulers/__init__.py
@@ -7,6 +7,69 @@
from ray.tune.schedulers.pbt import (PopulationBasedTraining,
PopulationBasedTrainingReplay)
+
+def create_scheduler(
+ scheduler,
+ metric="episode_reward_mean",
+ mode="max",
+ **kwargs,
+):
+ """Instantiate a scheduler based on the given string.
+
+ This is useful for swapping between different schedulers.
+
+ Args:
+ scheduler (str): The scheduler to use.
+ metric (str): The training result objective value attribute. Stopping
+ procedures will use this attribute.
+ mode (str): One of {min, max}. Determines whether objective is
+ minimizing or maximizing the metric attribute.
+ **kwargs: Additional parameters.
+ These keyword arguments will be passed to the initialization
+ function of the chosen class.
+ Returns:
+ ray.tune.schedulers.trial_scheduler.TrialScheduler: The scheduler.
+ Example:
+ >>> scheduler = tune.create_scheduler('pbt')
+ """
+
+ def _import_async_hyperband_scheduler():
+ from ray.tune.schedulers import AsyncHyperBandScheduler
+ return AsyncHyperBandScheduler
+
+ def _import_median_stopping_rule_scheduler():
+ from ray.tune.schedulers import MedianStoppingRule
+ return MedianStoppingRule
+
+ def _import_hyperband_scheduler():
+ from ray.tune.schedulers import HyperBandScheduler
+ return HyperBandScheduler
+
+ def _import_hb_bohb_scheduler():
+ from ray.tune.schedulers import HyperBandForBOHB
+ return HyperBandForBOHB
+
+ def _import_pbt_search():
+ from ray.tune.schedulers import PopulationBasedTraining
+ return PopulationBasedTraining
+
+ SCHEDULER_IMPORT = {
+ "async_hyperband": _import_async_hyperband_scheduler,
+ "median_stopping_rule": _import_median_stopping_rule_scheduler,
+ "hyperband": _import_hyperband_scheduler,
+ "hb_bohb": _import_hb_bohb_scheduler,
+ "pbt": _import_pbt_search,
+ }
+ scheduler = scheduler.lower()
+ if scheduler not in SCHEDULER_IMPORT:
+ raise ValueError(
+ f"Search alg must be one of {list(SCHEDULER_IMPORT)}. "
+ f"Got: {scheduler}")
+
+ SchedulerClass = SCHEDULER_IMPORT[scheduler]()
+ return SchedulerClass(metric=metric, mode=mode, **kwargs)
+
+
__all__ = [
"TrialScheduler", "HyperBandScheduler", "AsyncHyperBandScheduler",
"ASHAScheduler", "MedianStoppingRule", "FIFOScheduler",
diff --git a/python/ray/tune/suggest/__init__.py b/python/ray/tune/suggest/__init__.py
--- a/python/ray/tune/suggest/__init__.py
+++ b/python/ray/tune/suggest/__init__.py
@@ -5,6 +5,94 @@
from ray.tune.suggest.variant_generator import grid_search
from ray.tune.suggest.repeater import Repeater
+
+def create_searcher(
+ search_alg,
+ metric="episode_reward_mean",
+ mode="max",
+ **kwargs,
+):
+ """Instantiate a search algorithm based on the given string.
+
+ This is useful for swapping between different search algorithms.
+
+ Args:
+ search_alg (str): The search algorithm to use.
+ metric (str): The training result objective value attribute. Stopping
+ procedures will use this attribute.
+ mode (str): One of {min, max}. Determines whether objective is
+ minimizing or maximizing the metric attribute.
+ **kwargs: Additional parameters.
+ These keyword arguments will be passed to the initialization
+ function of the chosen class.
+ Returns:
+ ray.tune.suggest.Searcher: The search algorithm.
+ Example:
+ >>> search_alg = tune.create_searcher('ax')
+ """
+
+ def _import_ax_search():
+ from ray.tune.suggest.ax import AxSearch
+ return AxSearch
+
+ def _import_dragonfly_search():
+ from ray.tune.suggest.dragonfly import DragonflySearch
+ return DragonflySearch
+
+ def _import_skopt_search():
+ from ray.tune.suggest.skopt import SkOptSearch
+ return SkOptSearch
+
+ def _import_hyperopt_search():
+ from ray.tune.suggest.hyperopt import HyperOptSearch
+ return HyperOptSearch
+
+ def _import_bayesopt_search():
+ from ray.tune.suggest.bayesopt import BayesOptSearch
+ return BayesOptSearch
+
+ def _import_bohb_search():
+ from ray.tune.suggest.bohb import TuneBOHB
+ return TuneBOHB
+
+ def _import_nevergrad_search():
+ from ray.tune.suggest.nevergrad import NevergradSearch
+ return NevergradSearch
+
+ def _import_optuna_search():
+ from ray.tune.suggest.optuna import OptunaSearch
+ return OptunaSearch
+
+ def _import_zoopt_search():
+ from ray.tune.suggest.zoopt import ZOOptSearch
+ return ZOOptSearch
+
+ def _import_sigopt_search():
+ from ray.tune.suggest.sigopt import SigOptSearch
+ return SigOptSearch
+
+ SEARCH_ALG_IMPORT = {
+ "ax": _import_ax_search,
+ "dragonfly": _import_dragonfly_search,
+ "skopt": _import_skopt_search,
+ "hyperopt": _import_hyperopt_search,
+ "bayesopt": _import_bayesopt_search,
+ "bohb": _import_bohb_search,
+ "nevergrad": _import_nevergrad_search,
+ "optuna": _import_optuna_search,
+ "zoopt": _import_zoopt_search,
+ "sigopt": _import_sigopt_search,
+ }
+ search_alg = search_alg.lower()
+ if search_alg not in SEARCH_ALG_IMPORT:
+ raise ValueError(
+ f"Search alg must be one of {list(SEARCH_ALG_IMPORT)}. "
+ f"Got: {search_alg}")
+
+ SearcherClass = SEARCH_ALG_IMPORT[search_alg]()
+ return SearcherClass(metric=metric, mode=mode, **kwargs)
+
+
__all__ = [
"SearchAlgorithm", "Searcher", "BasicVariantGenerator", "SearchGenerator",
"grid_search", "Repeater", "ConcurrencyLimiter"
| diff --git a/python/ray/tune/tests/test_api.py b/python/ray/tune/tests/test_api.py
--- a/python/ray/tune/tests/test_api.py
+++ b/python/ray/tune/tests/test_api.py
@@ -14,7 +14,8 @@
from ray.tune import (DurableTrainable, Trainable, TuneError, Stopper,
EarlyStopping)
from ray.tune import register_env, register_trainable, run_experiments
-from ray.tune.schedulers import TrialScheduler, FIFOScheduler
+from ray.tune.schedulers import (TrialScheduler, FIFOScheduler,
+ AsyncHyperBandScheduler)
from ray.tune.trial import Trial
from ray.tune.result import (TIMESTEPS_TOTAL, DONE, HOSTNAME, NODE_IP, PID,
EPISODES_TOTAL, TRAINING_ITERATION,
@@ -24,6 +25,8 @@
from ray.tune.experiment import Experiment
from ray.tune.resources import Resources
from ray.tune.suggest import grid_search
+from ray.tune.suggest.hyperopt import HyperOptSearch
+from ray.tune.suggest.ax import AxSearch
from ray.tune.suggest._mock import _MockSuggestionAlgorithm
from ray.tune.utils import (flatten_dict, get_pinned_object,
pin_in_object_store)
@@ -1105,6 +1108,30 @@ def train(config, reporter):
self.assertIn("LOG_STDERR", content)
+class ShimCreationTest(unittest.TestCase):
+ def testCreateScheduler(self):
+ kwargs = {"metric": "metric_foo", "mode": "min"}
+
+ scheduler = "async_hyperband"
+ shim_scheduler = tune.create_scheduler(scheduler, **kwargs)
+ real_scheduler = AsyncHyperBandScheduler(**kwargs)
+ assert type(shim_scheduler) is type(real_scheduler)
+
+ def testCreateSearcher(self):
+ kwargs = {"metric": "metric_foo", "mode": "min"}
+
+ searcher_ax = "ax"
+ shim_searcher_ax = tune.create_searcher(searcher_ax, **kwargs)
+ real_searcher_ax = AxSearch(space=[], **kwargs)
+ assert type(shim_searcher_ax) is type(real_searcher_ax)
+
+ searcher_hyperopt = "hyperopt"
+ shim_searcher_hyperopt = tune.create_searcher(searcher_hyperopt,
+ **kwargs)
+ real_searcher_hyperopt = HyperOptSearch({}, **kwargs)
+ assert type(shim_searcher_hyperopt) is type(real_searcher_hyperopt)
+
+
if __name__ == "__main__":
import pytest
import sys
| [tune] shim instantiation of search algorithms
### Describe your feature request
allow creating a search algorithm for `tune.run` given:
- (maybe; see option 1 below) a string defining the search algorithm to use
- (maybe; see option 2 below) the search space configuration
### API
there will be two ways to use shim instantiation (option 3 from the design doc):
#### Option 1
(notice how the search algorithm definition doesn't require redefining the search configuration when initializing `HyperOptSearch`)
> ```python
> from ray import tune
> from ray.tune.param import Float, Integer, Categorical
> from ray.tune.param import Grid, Uniform, LogUniform, Normal
>
> space = {
> "lr": Float(1e-4, 1e-1).LogUniform(),
> "num_epochs": Integer(5, 10).Uniform(),
> "temperature": Float().Normal(1, 0.1),
> "batch_size": Categorical([32, 64]).Grid()
> }
>
> tune.run(
> trainable,
> space,
> search_alg=HyperOptSearch(metric="mean_loss"))
> ```
#### Option 2
> ```python
> from ray import tune
> from ray.tune.param import Float, Integer, Categorical
> from ray.tune.param import Grid, Uniform, LogUniform, Normal
>
> space = {
> "lr": Float(1e-4, 1e-1).LogUniform(),
> "num_epochs": Integer(5, 10).Uniform(),
> "temperature": Float().Normal(1, 0.1),
> "batch_size": Categorical([32, 64]).Grid()
> }
>
> search_alg = tune.create_searcher("HyperOpt", space,
> metric="mean_loss")
>
> tune.run(
> trainable,
> search_alg=search_alg)
> ```
---
related: #9969, #10401, #10444
CC @richardliaw @krfricke
| ```python
from ray import tune
from ray.tune.param import Float, Integer, Categorical
from ray.tune.param import Grid, Uniform, LogUniform, Normal
space = {
"lr": Float(1e-4, 1e-1).LogUniform(),
"num_epochs": Integer(5, 10).Uniform(),
"temperature": Float().Normal(1, 0.1),
"batch_size": Categorical([32, 64]).Grid()
}
###############################
# Shim API - Option 3
################################
tune.run(
trainable,
space,
search_alg=tune.create_searcher("HyperOpt", metric="mean_loss"))
# This should also work.
# tune.run(
# trainable,
# space,
# search_alg=HyperOptSearch(metric="mean_loss"))
```
untested; we should use an if-statement instead of `getattr`:
```python
def create_searcher(search_alg: str, **kwargs):
from ray.tune import suggest
return getattr(suggest, f'{search_alg.lower()}.{search_alg}Search')(None, **kwargs)
# in HyperOptSearch.__init__ we check if space is None, and if so, set HyperOptSearch.domain to None
# in tune.run, we check if `HyperOptSearch.domain` is None, and if so, we set assign the proper config based on the config kwarg using PR #10444
```
instead of this getattr, can you just create a dictionary?
```python
def import_hyperopt_search():
from ray.tune.suggest import HyperOptSearch
return HyperOptSearch
SEARCH_ALG_IMPORT = {
"hyperopt": import_hyperopt_search,
"dragonfly": import_dragonfly_search,
}
```
This avoids this dependency on naming convention.
```python
def create_searcher(search_alg, **kwargs):
# TODO: docstring
def _import_ax_search():
from ray.tune.suggest.ax import AxSearch
return AxSearch
def _import_dragonfly_search():
from ray.tune.suggest.dragonfly import DragonflySearch
return DragonflySearch
def _import_skopt_search():
from ray.tune.suggest.skopt import SkOptSearch
return SkOptSearch
def _import_hyperopt_search():
from ray.tune.suggest import HyperOptSearch
return HyperOptSearch
def _import_bayesopt_search():
from ray.tune.suggest.bayesopt import BayesOptSearch
return BayesOptSearch
def _import_bohb_search():
from ray.tune.suggest.bohb import TuneBOHB
return TuneBOHB
def _import_nevergrad_search():
from ray.tune.suggest.nevergrad import NevergradSearch
return NevergradSearch
def _import_optuna_search():
from ray.tune.suggest.optuna import OptunaSearch
return OptunaSearch
def _import_zoopt_search():
from ray.tune.suggest.zoopt import ZOOptSearch
return ZOOptSearch
def _import_sigopt_search():
from ray.tune.suggest.sigopt import SigOptSearch
return SigOptSearch
SEARCH_ALG_IMPORT = {
"ax": _import_ax_search,
"dragonfly": _import_dragonfly_search,
"skopt": _import_skopt_search,
"hyperopt": _import_hyperopt_search,
"bayesopt": _import_bayesopt_search,
"bohb": _import_bohb_search,
"nevergrad": _import_nevergrad_search,
"optuna": _import_optuna_search,
"zoopt": _import_zoopt_search,
"sigopt": _import_sigopt_search,
}
return SEARCH_ALG_IMPORT[search_alg](**kwargs)
```
Look good to me! Consider moving some of the default arguments (i.e., metric, mode) also into the wrapper so that it's explicit and captured in the docstring.
could you push a PR @sumanthratna and tag me when you do? Thanks! | 2020-08-31T22:43:50 |
ray-project/ray | 10,464 | ray-project__ray-10464 | [
"10408"
] | 3f98a8bfcb0c47c8a70458f1251761b0f6ec8195 | diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -205,11 +205,15 @@ def _update(self):
self.resource_demand_scheduler.get_nodes_to_launch(
self.provider.non_terminated_nodes(tag_filters={}),
self.pending_launches.breakdown(),
- resource_demand_vector))
+ resource_demand_vector,
+ self.load_metrics.get_resource_utilization()))
# TODO(ekl) also enforce max launch concurrency here?
for node_type, count in to_launch:
self.launch_new_node(count, node_type=node_type)
+ num_pending = self.pending_launches.value
+ nodes = self.workers()
+
# Launch additional nodes of the default type, if still needed.
num_workers = len(nodes) + num_pending
if num_workers < target_workers:
@@ -483,7 +487,8 @@ def log_info_string(self, nodes, target):
tmp += "\n"
if self.resource_demand_scheduler:
tmp += self.resource_demand_scheduler.debug_string(
- nodes, self.pending_launches.breakdown())
+ nodes, self.pending_launches.breakdown(),
+ self.load_metrics.get_resource_utilization())
if _internal_kv_initialized():
_internal_kv_put(DEBUG_AUTOSCALING_STATUS, tmp, overwrite=True)
logger.info(tmp)
diff --git a/python/ray/autoscaler/load_metrics.py b/python/ray/autoscaler/load_metrics.py
--- a/python/ray/autoscaler/load_metrics.py
+++ b/python/ray/autoscaler/load_metrics.py
@@ -16,13 +16,14 @@ class LoadMetrics:
can be removed.
"""
- def __init__(self):
+ def __init__(self, local_ip=None):
self.last_used_time_by_ip = {}
self.last_heartbeat_time_by_ip = {}
self.static_resources_by_ip = {}
self.dynamic_resources_by_ip = {}
self.resource_load_by_ip = {}
- self.local_ip = services.get_node_ip_address()
+ self.local_ip = services.get_node_ip_address(
+ ) if local_ip is None else local_ip
self.waiting_bundles = []
self.infeasible_bundles = []
@@ -98,7 +99,10 @@ def get_node_resources(self):
"""
return self.static_resources_by_ip.values()
- def get_resource_usage(self):
+ def get_resource_utilization(self):
+ return self.dynamic_resources_by_ip
+
+ def _get_resource_usage(self):
num_nodes = len(self.static_resources_by_ip)
nodes_used = 0.0
num_nonidle = 0
@@ -145,7 +149,8 @@ def info_string(self):
["{}: {}".format(k, v) for k, v in sorted(self._info().items())])
def _info(self):
- nodes_used, resources_used, resources_total = self.get_resource_usage()
+ nodes_used, resources_used, resources_total = self._get_resource_usage(
+ )
now = time.time()
idle_times = [now - t for t in self.last_used_time_by_ip.values()]
diff --git a/python/ray/autoscaler/resource_demand_scheduler.py b/python/ray/autoscaler/resource_demand_scheduler.py
--- a/python/ray/autoscaler/resource_demand_scheduler.py
+++ b/python/ray/autoscaler/resource_demand_scheduler.py
@@ -39,11 +39,10 @@ def __init__(self, provider: NodeProvider,
self.node_types = node_types
self.max_workers = max_workers
- # TODO(ekl) take into account existing utilization of node resources. We
- # should subtract these from node resources prior to running bin packing.
def get_nodes_to_launch(self, nodes: List[NodeID],
pending_nodes: Dict[NodeType, int],
- resource_demands: List[ResourceDict]
+ resource_demands: List[ResourceDict],
+ usage_by_ip: Dict[str, ResourceDict]
) -> List[Tuple[NodeType, int]]:
"""Given resource demands, return node types to add to the cluster.
@@ -64,7 +63,8 @@ def get_nodes_to_launch(self, nodes: List[NodeID],
return []
node_resources, node_type_counts = self.calculate_node_resources(
- nodes, pending_nodes)
+ nodes, pending_nodes, usage_by_ip)
+
logger.info("Cluster resources: {}".format(node_resources))
logger.info("Node counts: {}".format(node_type_counts))
@@ -72,34 +72,43 @@ def get_nodes_to_launch(self, nodes: List[NodeID],
logger.info("Resource demands: {}".format(resource_demands))
logger.info("Unfulfilled demands: {}".format(unfulfilled))
- nodes = get_nodes_for(self.node_types, node_type_counts,
- self.max_workers - len(nodes), unfulfilled)
+ nodes = get_nodes_for(
+ self.node_types, node_type_counts,
+ self.max_workers - len(nodes) - sum(pending_nodes.values()),
+ unfulfilled)
logger.info("Node requests: {}".format(nodes))
return nodes
def calculate_node_resources(
- self, nodes: List[NodeID], pending_nodes: Dict[NodeID, int]
+ self, nodes: List[NodeID], pending_nodes: Dict[NodeID, int],
+ usage_by_ip: Dict[str, ResourceDict]
) -> (List[ResourceDict], Dict[NodeType, int]):
"""Returns node resource list and node type counts."""
node_resources = []
node_type_counts = collections.defaultdict(int)
- def add_node(node_type):
+ def add_node(node_type, existing_resource_usages=None):
if node_type not in self.node_types:
raise RuntimeError("Missing entry for node_type {} in "
"available_node_types config: {}".format(
node_type, self.node_types))
# Careful not to include the same dict object multiple times.
- node_resources.append(
- copy.deepcopy(self.node_types[node_type]["resources"]))
+ available = copy.deepcopy(self.node_types[node_type]["resources"])
+ if existing_resource_usages:
+ for resource, used in existing_resource_usages.items():
+ available[resource] -= used
+
+ node_resources.append(available)
node_type_counts[node_type] += 1
for node_id in nodes:
tags = self.provider.node_tags(node_id)
if TAG_RAY_USER_NODE_TYPE in tags:
node_type = tags[TAG_RAY_USER_NODE_TYPE]
- add_node(node_type)
+ ip = self.provider.internal_ip(node_id)
+ resources = usage_by_ip.get(ip, {})
+ add_node(node_type, resources)
for node_type, count in pending_nodes.items():
for _ in range(count):
@@ -108,9 +117,11 @@ def add_node(node_type):
return node_resources, node_type_counts
def debug_string(self, nodes: List[NodeID],
- pending_nodes: Dict[NodeID, int]) -> str:
+ pending_nodes: Dict[NodeID, int],
+ usage_by_ip: Dict[str, ResourceDict]) -> str:
+ print(f"{usage_by_ip}")
node_resources, node_type_counts = self.calculate_node_resources(
- nodes, pending_nodes)
+ nodes, pending_nodes, usage_by_ip)
out = "Worker node types:"
for node_type, count in node_type_counts.items():
| diff --git a/python/ray/tests/test_multi_node_2.py b/python/ray/tests/test_multi_node_2.py
--- a/python/ray/tests/test_multi_node_2.py
+++ b/python/ray/tests/test_multi_node_2.py
@@ -76,7 +76,7 @@ def setup_monitor(address):
def verify_load_metrics(monitor, expected_resource_usage=None, timeout=30):
while True:
monitor.process_messages()
- resource_usage = monitor.load_metrics.get_resource_usage()
+ resource_usage = monitor.load_metrics._get_resource_usage()
if "memory" in resource_usage[1]:
del resource_usage[1]["memory"]
diff --git a/python/ray/tests/test_resource_demand_scheduler.py b/python/ray/tests/test_resource_demand_scheduler.py
--- a/python/ray/tests/test_resource_demand_scheduler.py
+++ b/python/ray/tests/test_resource_demand_scheduler.py
@@ -14,7 +14,7 @@
from ray.autoscaler.commands import get_or_create_head_node
from ray.autoscaler.tags import TAG_RAY_USER_NODE_TYPE, TAG_RAY_NODE_KIND
from ray.autoscaler.resource_demand_scheduler import _utilization_score, \
- get_bin_pack_residual, get_nodes_for
+ get_bin_pack_residual, get_nodes_for, ResourceDemandScheduler
from ray.test_utils import same_elements
from time import sleep
@@ -163,6 +163,23 @@ def test_get_nodes_respects_max_limit():
}] * 10) == [("m4.large", 2)]
+def test_get_nodes_to_launch_limits():
+ provider = MockProvider()
+ scheduler = ResourceDemandScheduler(provider, TYPES_A, 3)
+
+ provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
+
+ nodes = provider.non_terminated_nodes({})
+
+ ips = provider.non_terminated_node_ips({})
+ utilizations = {ip: {"GPU": 8} for ip in ips}
+
+ to_launch = scheduler.get_nodes_to_launch(nodes, {"p2.8xlarge": 1}, [{
+ "GPU": 8
+ }] * 2, utilizations)
+ assert to_launch == []
+
+
class LoadMetricsTest(unittest.TestCase):
def testResourceDemandVector(self):
lm = LoadMetrics()
@@ -264,6 +281,44 @@ def testScaleUpMinSanity(self):
autoscaler.update()
self.waitForNodes(2)
+ def testScaleUpIgnoreUsed(self):
+ config = MULTI_WORKER_CLUSTER.copy()
+ # Commenting out this line causes the test case to fail?!?!
+ config["min_workers"] = 0
+ config["target_utilization_fraction"] = 1.0
+ config_path = self.write_config(config)
+ self.provider = MockProvider()
+ self.provider.create_node({}, {
+ TAG_RAY_NODE_KIND: "head",
+ TAG_RAY_USER_NODE_TYPE: "p2.xlarge"
+ }, 1)
+ head_ip = self.provider.non_terminated_node_ips({})[0]
+ self.provider.finish_starting_nodes()
+ runner = MockProcessRunner()
+ lm = LoadMetrics(local_ip=head_ip)
+ autoscaler = StandardAutoscaler(
+ config_path,
+ lm,
+ max_failures=0,
+ process_runner=runner,
+ update_interval_s=0)
+ autoscaler.update()
+ self.waitForNodes(1)
+ lm.update(head_ip, {"CPU": 4, "GPU": 1}, {}, {})
+ self.waitForNodes(1)
+
+ lm.update(
+ head_ip, {
+ "CPU": 4,
+ "GPU": 1
+ }, {"GPU": 1}, {},
+ waiting_bundles=[{
+ "GPU": 1
+ }])
+ autoscaler.update()
+ self.waitForNodes(2)
+ assert self.provider.mock_nodes[1].node_type == "p2.xlarge"
+
def testRequestBundlesAccountsForHeadNode(self):
config = MULTI_WORKER_CLUSTER.copy()
config["head_node_type"] = "p2.8xlarge"
| [autoscaler] Multi node autoscaler doesn't take into account existing resource usage
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
When we calculate the resources available in the cluster, we should take into account the existing usage from `LoadMetrics`: https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/resource_demand_scheduler.py#L54
Otherwise, we might not scale up even when tasks cannot be scheduled. This should be fixed:
1. Subtract the usage of the node when calculating node resources by looking at LoadMetrics.
2. Add unit tests for this scenario.
| 2020-09-01T07:31:15 |
|
ray-project/ray | 10,499 | ray-project__ray-10499 | [
"10521"
] | ef18893fb50c97c2c0d9d7b8b951455683f89635 | diff --git a/rllib/agents/ddpg/ddpg_tf_policy.py b/rllib/agents/ddpg/ddpg_tf_policy.py
--- a/rllib/agents/ddpg/ddpg_tf_policy.py
+++ b/rllib/agents/ddpg/ddpg_tf_policy.py
@@ -1,4 +1,5 @@
from gym.spaces import Box
+from functools import partial
import logging
import numpy as np
@@ -290,7 +291,8 @@ def gradients_fn(policy, optimizer, loss):
# Clip if necessary.
if policy.config["grad_clip"]:
- clip_func = tf.clip_by_norm
+ clip_func = partial(
+ tf.clip_by_norm, clip_norm=policy.config["grad_clip"])
else:
clip_func = tf.identity
diff --git a/rllib/agents/sac/sac_tf_policy.py b/rllib/agents/sac/sac_tf_policy.py
--- a/rllib/agents/sac/sac_tf_policy.py
+++ b/rllib/agents/sac/sac_tf_policy.py
@@ -1,4 +1,5 @@
from gym.spaces import Box, Discrete
+from functools import partial
import logging
import ray
@@ -322,7 +323,8 @@ def gradients_fn(policy, optimizer, loss):
# Clip if necessary.
if policy.config["grad_clip"]:
- clip_func = tf.clip_by_norm
+ clip_func = partial(
+ tf.clip_by_norm, clip_norm=policy.config["grad_clip"])
else:
clip_func = tf.identity
| [rllib] SAC and DDPG policy can't do `grad_clip`
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
Ray: 0.8.7
Python: 3.7
Tensorflow: 2.2.0
OS: Manjaro x86_64
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
````py
from ray import tune
from ray.rllib.agents.sac import SACTrainer
from ray.rllib.agents.ddpg import DDPGTrainer
tune.run(DDPGTrainer, config={"env": "CartPole-v0","grad_clip":40.0})
# tune.run(SACTrainer, config={"env": "CartPole-v0","grad_clip":40.0})
````
This [PR](https://github.com/ray-project/ray/pull/10499) will fix this issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-02T13:07:23 |
||
ray-project/ray | 10,504 | ray-project__ray-10504 | [
"10313"
] | ef18893fb50c97c2c0d9d7b8b951455683f89635 | diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -1,5 +1,5 @@
-from collections import defaultdict
-from typing import Optional
+from collections import defaultdict, namedtuple
+from typing import Any, Optional
import copy
import logging
import math
@@ -30,6 +30,12 @@
logger = logging.getLogger(__name__)
+# Tuple of modified fields for the given node_id returned by should_update
+# that will be passed into a NodeUpdaterThread.
+UpdateInstructions = namedtuple(
+ "UpdateInstructions",
+ ["node_id", "init_commands", "start_ray_commands", "docker_config"])
+
class StandardAutoscaler:
"""The autoscaling control loop for a Ray cluster.
@@ -236,14 +242,15 @@ def _update(self):
# problems. They should at a minimum be spawned as daemon threads.
# See https://github.com/ray-project/ray/pull/5903 for more info.
T = []
- for node_id, commands, ray_start in (self.should_update(node_id)
- for node_id in nodes):
+ for node_id, commands, ray_start, docker_config in (
+ self.should_update(node_id) for node_id in nodes):
if node_id is not None:
resources = self._node_resources(node_id)
T.append(
threading.Thread(
target=self.spawn_updater,
- args=(node_id, commands, ray_start, resources)))
+ args=(node_id, commands, ray_start, resources,
+ docker_config)))
for t in T:
t.start()
for t in T:
@@ -401,44 +408,56 @@ def recover_if_needed(self, node_id, now):
updater.start()
self.updaters[node_id] = updater
- def _get_node_type_specific_commands(self, node_id: str,
- commands_key: str):
- commands = self.config[commands_key]
+ def _get_node_type_specific_fields(self, node_id: str,
+ fields_key: str) -> Any:
+ fields = self.config[fields_key]
node_tags = self.provider.node_tags(node_id)
if TAG_RAY_USER_NODE_TYPE in node_tags:
node_type = node_tags[TAG_RAY_USER_NODE_TYPE]
if node_type not in self.available_node_types:
raise ValueError(f"Unknown node type tag: {node_type}.")
node_specific_config = self.available_node_types[node_type]
- if commands_key in node_specific_config:
- commands = node_specific_config[commands_key]
- return commands
+ if fields_key in node_specific_config:
+ fields = node_specific_config[fields_key]
+ return fields
+
+ def _get_node_specific_docker_config(self, node_id):
+ docker_config = copy.deepcopy(self.config.get("docker", {}))
+ node_specific_docker = self._get_node_type_specific_fields(
+ node_id, "docker")
+ docker_config.update(node_specific_docker)
+ return docker_config
def should_update(self, node_id):
if not self.can_update(node_id):
- return None, None, None # no update
+ return UpdateInstructions(None, None, None, None) # no update
status = self.provider.node_tags(node_id).get(TAG_RAY_NODE_STATUS)
if status == STATUS_UP_TO_DATE and self.files_up_to_date(node_id):
- return None, None, None # no update
+ return UpdateInstructions(None, None, None, None) # no update
successful_updated = self.num_successful_updates.get(node_id, 0) > 0
if successful_updated and self.config.get("restart_only", False):
init_commands = []
ray_commands = self.config["worker_start_ray_commands"]
elif successful_updated and self.config.get("no_restart", False):
- init_commands = self._get_node_type_specific_commands(
+ init_commands = self._get_node_type_specific_fields(
node_id, "worker_setup_commands")
ray_commands = []
else:
- init_commands = self._get_node_type_specific_commands(
+ init_commands = self._get_node_type_specific_fields(
node_id, "worker_setup_commands")
ray_commands = self.config["worker_start_ray_commands"]
- return (node_id, init_commands, ray_commands)
+ docker_config = self._get_node_specific_docker_config(node_id)
+ return UpdateInstructions(
+ node_id=node_id,
+ init_commands=init_commands,
+ start_ray_commands=ray_commands,
+ docker_config=docker_config)
def spawn_updater(self, node_id, init_commands, ray_start_commands,
- node_resources):
+ node_resources, docker_config):
updater = NodeUpdaterThread(
node_id=node_id,
provider_config=self.config["provider"],
@@ -447,7 +466,7 @@ def spawn_updater(self, node_id, init_commands, ray_start_commands,
cluster_name=self.config["cluster_name"],
file_mounts=self.config["file_mounts"],
initialization_commands=with_head_node_ip(
- self._get_node_type_specific_commands(
+ self._get_node_type_specific_fields(
node_id, "initialization_commands")),
setup_commands=with_head_node_ip(init_commands),
ray_start_commands=with_head_node_ip(ray_start_commands),
@@ -457,7 +476,7 @@ def spawn_updater(self, node_id, init_commands, ray_start_commands,
cluster_synced_files=self.config["cluster_synced_files"],
process_runner=self.process_runner,
use_internal_ip=True,
- docker_config=self.config.get("docker"),
+ docker_config=docker_config,
node_resources=node_resources)
updater.start()
self.updaters[node_id] = updater
diff --git a/python/ray/autoscaler/command_runner.py b/python/ray/autoscaler/command_runner.py
--- a/python/ray/autoscaler/command_runner.py
+++ b/python/ray/autoscaler/command_runner.py
@@ -705,9 +705,8 @@ def _docker_expand_user(self, string, any_char=False):
def run_init(self, *, as_head, file_mounts):
image = self.docker_config.get("image")
- if image is None:
- image = self.docker_config.get(
- f"{'head' if as_head else 'worker'}_image")
+ image = self.docker_config.get(
+ f"{'head' if as_head else 'worker'}_image", image)
self._check_docker_installed()
if self.docker_config.get("pull_before_run", True):
| diff --git a/python/ray/tests/test_resource_demand_scheduler.py b/python/ray/tests/test_resource_demand_scheduler.py
--- a/python/ray/tests/test_resource_demand_scheduler.py
+++ b/python/ray/tests/test_resource_demand_scheduler.py
@@ -449,6 +449,86 @@ def testCommandPassing(self):
runner.assert_not_has_call(self.provider.mock_nodes[2].internal_ip,
"init_cmd")
+ def testDockerWorkers(self):
+ config = MULTI_WORKER_CLUSTER.copy()
+ config["available_node_types"]["p2.8xlarge"]["docker"] = {
+ "worker_image": "p2.8x_image:latest",
+ "worker_run_options": ["p2.8x-run-options"]
+ }
+ config["available_node_types"]["p2.xlarge"]["docker"] = {
+ "worker_image": "p2x_image:nightly"
+ }
+ config["docker"]["worker_run_options"] = ["standard-run-options"]
+ config["docker"]["image"] = "default-image:nightly"
+ config["docker"]["worker_image"] = "default-image:nightly"
+ # Commenting out this line causes the test case to fail?!?!
+ config["min_workers"] = 0
+ config["max_workers"] = 10
+ config_path = self.write_config(config)
+ self.provider = MockProvider()
+ runner = MockProcessRunner()
+ autoscaler = StandardAutoscaler(
+ config_path,
+ LoadMetrics(),
+ max_failures=0,
+ process_runner=runner,
+ update_interval_s=0)
+ assert len(self.provider.non_terminated_nodes({})) == 0
+ autoscaler.update()
+ self.waitForNodes(0)
+ autoscaler.request_resources([{"CPU": 1}])
+ autoscaler.update()
+ self.waitForNodes(1)
+ assert self.provider.mock_nodes[0].node_type == "m4.large"
+ autoscaler.request_resources([{"GPU": 8}])
+ autoscaler.update()
+ self.waitForNodes(2)
+ assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
+ autoscaler.request_resources([{"GPU": 1}] * 9)
+ autoscaler.update()
+ self.waitForNodes(3)
+ assert self.provider.mock_nodes[2].node_type == "p2.xlarge"
+ autoscaler.update()
+ # Fill up m4, p2.8, p2 and request 2 more CPUs
+ autoscaler.request_resources([{
+ "CPU": 2
+ }, {
+ "CPU": 16
+ }, {
+ "CPU": 32
+ }, {
+ "CPU": 2
+ }])
+ autoscaler.update()
+ self.waitForNodes(4)
+ assert self.provider.mock_nodes[3].node_type == "m4.16xlarge"
+ autoscaler.update()
+ sleep(0.1)
+ runner.assert_has_call(self.provider.mock_nodes[1].internal_ip,
+ "p2.8x-run-options")
+ runner.assert_has_call(self.provider.mock_nodes[1].internal_ip,
+ "p2.8x_image:latest")
+ runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
+ "default-image:nightly")
+ runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
+ "standard-run-options")
+
+ runner.assert_has_call(self.provider.mock_nodes[2].internal_ip,
+ "p2x_image:nightly")
+ runner.assert_has_call(self.provider.mock_nodes[2].internal_ip,
+ "standard-run-options")
+ runner.assert_not_has_call(self.provider.mock_nodes[2].internal_ip,
+ "p2.8x-run-options")
+
+ runner.assert_has_call(self.provider.mock_nodes[3].internal_ip,
+ "default-image:nightly")
+ runner.assert_has_call(self.provider.mock_nodes[3].internal_ip,
+ "standard-run-options")
+ runner.assert_not_has_call(self.provider.mock_nodes[3].internal_ip,
+ "p2.8x-run-options")
+ runner.assert_not_has_call(self.provider.mock_nodes[3].internal_ip,
+ "p2x_image:nightly")
+
def testUpdateConfig(self):
config = MULTI_WORKER_CLUSTER.copy()
config_path = self.write_config(config)
| [Autoscaler] Node specific docker options
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
We should allow node specific docker options for the autoscaler (similar to provider options, commands, resources, etc.)
| cc @ijrsvt This is probably post-ray1.0
@yiranwang52 FYI | 2020-09-02T17:24:41 |
ray-project/ray | 10,513 | ray-project__ray-10513 | [
"10404"
] | 3eed3eca09bfbbb3955e1c7757d480d1e123aceb | diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -13,10 +13,11 @@
from ray.experimental.internal_kv import _internal_kv_put, \
_internal_kv_initialized
from ray.autoscaler.node_provider import get_node_provider
-from ray.autoscaler.tags import (
- TAG_RAY_LAUNCH_CONFIG, TAG_RAY_RUNTIME_CONFIG,
- TAG_RAY_FILE_MOUNTS_CONTENTS, TAG_RAY_NODE_STATUS, TAG_RAY_NODE_KIND,
- TAG_RAY_USER_NODE_TYPE, STATUS_UP_TO_DATE, NODE_KIND_WORKER)
+from ray.autoscaler.tags import (TAG_RAY_LAUNCH_CONFIG, TAG_RAY_RUNTIME_CONFIG,
+ TAG_RAY_FILE_MOUNTS_CONTENTS,
+ TAG_RAY_NODE_STATUS, TAG_RAY_NODE_KIND,
+ TAG_RAY_USER_NODE_TYPE, STATUS_UP_TO_DATE,
+ NODE_KIND_WORKER, NODE_KIND_UNMANAGED)
from ray.autoscaler.updater import NodeUpdaterThread
from ray.autoscaler.node_launcher import NodeLauncher
from ray.autoscaler.resource_demand_scheduler import ResourceDemandScheduler
@@ -146,10 +147,12 @@ def _update(self):
nodes = self.workers()
# Check pending nodes immediately after fetching the number of running
# nodes to minimize chance number of pending nodes changing after
- # additional nodes are launched.
+ # additional nodes (managed and unmanaged) are launched.
num_pending = self.pending_launches.value
- self.load_metrics.prune_active_ips(
- [self.provider.internal_ip(node_id) for node_id in nodes])
+ self.load_metrics.prune_active_ips([
+ self.provider.internal_ip(node_id)
+ for node_id in self.all_workers()
+ ])
target_workers = self.target_num_workers()
if len(nodes) >= target_workers:
@@ -165,8 +168,9 @@ def _update(self):
nodes_to_terminate = []
for node_id in nodes:
node_ip = self.provider.internal_ip(node_id)
- if node_ip in last_used and last_used[node_ip] < horizon and \
- len(nodes) - len(nodes_to_terminate) > target_workers:
+ if (node_ip in last_used and last_used[node_ip] < horizon) and \
+ (len(nodes) - len(nodes_to_terminate)
+ > target_workers):
logger.info("StandardAutoscaler: "
"{}: Terminating idle node".format(node_id))
nodes_to_terminate.append(node_id)
@@ -182,11 +186,12 @@ def _update(self):
# Terminate nodes if there are too many
nodes_to_terminate = []
- while len(nodes) > self.config["max_workers"]:
+ while (len(nodes) -
+ len(nodes_to_terminate)) > self.config["max_workers"] and nodes:
+ to_terminate = nodes.pop()
logger.info("StandardAutoscaler: "
- "{}: Terminating unneeded node".format(nodes[-1]))
- nodes_to_terminate.append(nodes[-1])
- nodes = nodes[:-1]
+ "{}: Terminating unneeded node".format(to_terminate))
+ nodes_to_terminate.append(to_terminate)
if nodes_to_terminate:
self.provider.terminate_nodes(nodes_to_terminate)
@@ -511,10 +516,17 @@ def launch_new_node(self, count: int, node_type: Optional[str]) -> None:
config = copy.deepcopy(self.config)
self.launch_queue.put((config, count, node_type))
+ def all_workers(self):
+ return self.workers() + self.unmanaged_workers()
+
def workers(self):
return self.provider.non_terminated_nodes(
tag_filters={TAG_RAY_NODE_KIND: NODE_KIND_WORKER})
+ def unmanaged_workers(self):
+ return self.provider.non_terminated_nodes(
+ tag_filters={TAG_RAY_NODE_KIND: NODE_KIND_UNMANAGED})
+
def log_info_string(self, nodes, target):
tmp = "Cluster status: "
tmp += self.info_string(nodes, target)
diff --git a/python/ray/autoscaler/resource_demand_scheduler.py b/python/ray/autoscaler/resource_demand_scheduler.py
--- a/python/ray/autoscaler/resource_demand_scheduler.py
+++ b/python/ray/autoscaler/resource_demand_scheduler.py
@@ -14,7 +14,7 @@
from typing import List, Dict, Tuple
from ray.autoscaler.node_provider import NodeProvider
-from ray.autoscaler.tags import TAG_RAY_USER_NODE_TYPE
+from ray.autoscaler.tags import TAG_RAY_USER_NODE_TYPE, NODE_KIND_UNMANAGED
logger = logging.getLogger(__name__)
@@ -90,9 +90,14 @@ def calculate_node_resources(
def add_node(node_type, existing_resource_usages=None):
if node_type not in self.node_types:
- raise RuntimeError("Missing entry for node_type {} in "
- "available_node_types config: {}".format(
- node_type, self.node_types))
+ logger.warn(
+ f"Missing entry for node_type {node_type} in "
+ f"cluster config: {self.node_types} under entry "
+ f"available_node_types. This node's resources will be "
+ f"ignored. If you are using an unmanaged node, manually "
+ f"set the user_node_type tag to \"{NODE_KIND_UNMANAGED}\""
+ f"in your cloud provider's management console.")
+ return None
# Careful not to include the same dict object multiple times.
available = copy.deepcopy(self.node_types[node_type]["resources"])
if existing_resource_usages:
diff --git a/python/ray/autoscaler/tags.py b/python/ray/autoscaler/tags.py
--- a/python/ray/autoscaler/tags.py
+++ b/python/ray/autoscaler/tags.py
@@ -8,6 +8,7 @@
TAG_RAY_NODE_KIND = "ray-node-type"
NODE_KIND_HEAD = "head"
NODE_KIND_WORKER = "worker"
+NODE_KIND_UNMANAGED = "unmanaged"
# Tag for user defined node types (e.g., m4xl_spot). This is used for multi
# node type clusters.
| diff --git a/python/ray/tests/test_autoscaler.py b/python/ray/tests/test_autoscaler.py
--- a/python/ray/tests/test_autoscaler.py
+++ b/python/ray/tests/test_autoscaler.py
@@ -597,6 +597,50 @@ def testAggressiveAutoscaling(self):
autoscaler.update()
self.waitForNodes(11)
+ def testUnmanagedNodes(self):
+ config = SMALL_CLUSTER.copy()
+ config["min_workers"] = 0
+ config["max_workers"] = 20
+ config["initial_workers"] = 0
+ config["idle_timeout_minutes"] = 0
+ config["autoscaling_mode"] = "aggressive"
+ config["target_utilization_fraction"] = 0.8
+ config_path = self.write_config(config)
+
+ self.provider = MockProvider()
+ self.provider.create_node({}, {TAG_RAY_NODE_KIND: "head"}, 1)
+ head_ip = self.provider.non_terminated_node_ips(
+ tag_filters={TAG_RAY_NODE_KIND: "head"}, )[0]
+
+ self.provider.create_node({}, {TAG_RAY_NODE_KIND: "unmanaged"}, 1)
+ unmanaged_ip = self.provider.non_terminated_node_ips(
+ tag_filters={TAG_RAY_NODE_KIND: "unmanaged"}, )[0]
+
+ runner = MockProcessRunner()
+
+ lm = LoadMetrics()
+ lm.local_ip = head_ip
+
+ autoscaler = StandardAutoscaler(
+ config_path,
+ lm,
+ max_launch_batch=5,
+ max_concurrent_launches=5,
+ max_failures=0,
+ process_runner=runner,
+ update_interval_s=0)
+
+ autoscaler.update()
+ self.waitForNodes(2)
+ # This node has num_cpus=0
+ lm.update(unmanaged_ip, {"CPU": 0}, {"CPU": 0}, {})
+ autoscaler.update()
+ self.waitForNodes(2)
+ # 1 CPU task cannot be scheduled.
+ lm.update(unmanaged_ip, {"CPU": 0}, {"CPU": 0}, {"CPU": 1})
+ autoscaler.update()
+ self.waitForNodes(3)
+
def testDelayedLaunch(self):
config_path = self.write_config(SMALL_CLUSTER)
self.provider = MockProvider()
| [Autoscaler] Only recognizes workers it launches
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
0.8.6+
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
* Start a ray cluster with gpu workers (non-resource demand autoscaler), head should not have gpu's.
* Manually add a node (not using autoscaler), with a custom resource {"custom": 1}
```
@ray.remote(num_gpus=1)
def gpu_task():
pass
@ray.remote(resources={"custom":1})
def launch_gpu():
# This blocks and never starts a gpu worker.
ray.get(gpu_task.remote())
ray.get(launch_gpu.remote())
```
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| The root cause of this is that `StandardAutoscaler::workers()` explicitely filters
```
def workers(self):
return self.provider.non_terminated_nodes(
tag_filters={TAG_RAY_NODE_KIND: NODE_KIND_WORKER})
```
@wuisawesome Can you set the priority of this task?
> The root cause of this is that `StandardAutoscaler::workers()` explicitely filters
>
> ```
> def workers(self):
> return self.provider.non_terminated_nodes(
> tag_filters={TAG_RAY_NODE_KIND: NODE_KIND_WORKER})
> ```
This is indeed the root cause. When this would be fixed, the autoscaler will however still try to update the manual node because the `ray-launch-config` and `ray-runtime-config` labels are not set on the manual node either.
Additionally, the script you posted looks very similar to the thing I'm trying to do, but is still a little different.
In my case the `gpu_task` task is not started from within an actor but simply from within python code that is running on the manually started worker. I don't know if that makes a difference or not... | 2020-09-02T21:54:34 |
ray-project/ray | 10,519 | ray-project__ray-10519 | [
"10378"
] | f38dba09b2cd07a27d1e9659176c8a60972bfd44 | diff --git a/python/ray/util/dask/__init__.py b/python/ray/util/dask/__init__.py
--- a/python/ray/util/dask/__init__.py
+++ b/python/ray/util/dask/__init__.py
@@ -1,3 +1,14 @@
from .scheduler import ray_dask_get, ray_dask_get_sync
+from .callbacks import (
+ RayDaskCallback,
+ local_ray_callbacks,
+ unpack_ray_callbacks,
+)
-__all__ = ["ray_dask_get", "ray_dask_get_sync"]
+__all__ = [
+ "ray_dask_get",
+ "ray_dask_get_sync",
+ "RayDaskCallback",
+ "local_ray_callbacks",
+ "unpack_ray_callbacks",
+]
diff --git a/python/ray/util/dask/callbacks.py b/python/ray/util/dask/callbacks.py
new file mode 100644
--- /dev/null
+++ b/python/ray/util/dask/callbacks.py
@@ -0,0 +1,211 @@
+import contextlib
+from collections import namedtuple
+
+from dask.callbacks import Callback
+
+# The names of the Ray-specific callbacks. These are the kwarg names that
+# RayDaskCallback will accept on construction, and is considered the
+# source-of-truth for what Ray-specific callbacks exist.
+CBS = (
+ "ray_presubmit",
+ "ray_postsubmit",
+ "ray_pretask",
+ "ray_posttask",
+ "ray_postsubmit_all",
+ "ray_finish",
+)
+# The Ray-specific callback method names for RayDaskCallback.
+CB_FIELDS = tuple("_" + field for field in CBS)
+# The Ray-specific callbacks that we do _not_ wish to drop from RayCallbacks
+# if not given on a RayDaskCallback instance (will be filled with None
+# instead).
+CBS_DONT_DROP = {"ray_pretask", "ray_posttask"}
+
+# The Ray-specific callbacks for a single RayDaskCallback.
+RayCallback = namedtuple("RayCallback", " ".join(CBS))
+
+# The Ray-specific callbacks for one or more RayDaskCallbacks.
+RayCallbacks = namedtuple("RayCallbacks",
+ " ".join([field + "_cbs" for field in CBS]))
+
+
+class RayDaskCallback(Callback):
+ """
+ Extends Dask's `Callback` class with Ray-specific hooks. When instantiating
+ or subclassing this class, both the normal Dask hooks (e.g. pretask,
+ posttask, etc.) and the Ray-specific hooks can be provided.
+
+ See `dask.callbacks.Callback` for usage.
+
+ Caveats: Any Dask-Ray scheduler must bring the Ray-specific callbacks into
+ context using the `local_ray_callbacks` context manager, since the built-in
+ `local_callbacks` context manager provided by Dask isn't aware of this
+ class.
+ """
+
+ # Set of active Ray-specific callbacks.
+ ray_active = set()
+
+ def __init__(self, **kwargs):
+ """
+ Ray-specific callbacks:
+ - def _ray_presubmit(task, key, deps):
+ Run before submitting a Ray task. If this callback returns a
+ non-`None` value, a Ray task will _not_ be created and this
+ value will be used as the would-be task's result value.
+
+ Args:
+ task (tuple): A Dask task, where the first tuple item is
+ the task function, and the remaining tuple items are
+ the task arguments (either the actual argument values,
+ or Dask keys into the deps dictionary whose
+ corresponding values are the argument values).
+ key (str): The Dask graph key for the given task.
+ deps (dict): The dependencies of this task.
+
+ Returns:
+ Either None, in which case a Ray task will be submitted, or
+ a non-None value, in which case a Ray task will not be
+ submitted and this return value will be used as the
+ would-be task result value.
+
+ - def _ray_postsubmit(task, key, deps, object_ref):
+ Run after submitting a Ray task.
+
+ Args:
+ task (tuple): A Dask task, where the first tuple item is
+ the task function, and the remaining tuple items are
+ the task arguments (either the actual argument values,
+ or Dask keys into the deps dictionary whose
+ corresponding values are the argument values).
+ key (str): The Dask graph key for the given task.
+ deps (dict): The dependencies of this task.
+ object_ref (ray.ObjectRef): The object reference for the
+ return value of the Ray task.
+
+ - def _ray_pretask(key, object_refs):
+ Run before executing a Dask task within a Ray task. This
+ executes after the task has been submitted, within a Ray
+ worker. The return value of this task will be passed to the
+ _ray_posttask callback, if provided.
+
+ Args:
+ key (str): The Dask graph key for the Dask task.
+ object_refs (List[ray.ObjectRef]): The object references
+ for the arguments of the Ray task.
+
+ Returns:
+ A value that will be passed to the corresponding
+ _ray_posttask callback, if said callback is defined.
+
+ - def _ray_posttask(key, result, pre_state):
+ Run after executing a Dask task within a Ray task. This
+ executes within a Ray worker. This callback receives the return
+ value of the _ray_pretask callback, if provided.
+
+ Args:
+ key (str): The Dask graph key for the Dask task.
+ result (object): The task result value.
+ pre_state (object): The return value of the corresponding
+ _ray_pretask callback, if said callback is defined.
+
+ - def _ray_postsubmit_all(object_refs, dsk):
+ Run after all Ray tasks have been submitted.
+
+ Args:
+ object_refs (List[ray.ObjectRef]): The object references
+ for the output (leaf) Ray tasks of the task graph.
+ dsk (dict): The Dask graph.
+
+ - def _ray_finish(result):
+ Run after all Ray tasks have finished executing and the final
+ result has been returned.
+
+ Args:
+ result (object): The final result (output) of the Dask
+ computation, before any repackaging is done by
+ Dask collection-specific post-compute callbacks.
+ """
+ for cb in CBS:
+ cb_func = kwargs.pop(cb, None)
+ if cb_func is not None:
+ setattr(self, "_" + cb, cb_func)
+
+ super().__init__(**kwargs)
+
+ @property
+ def _ray_callback(self):
+ return RayCallback(
+ *[getattr(self, field, None) for field in CB_FIELDS])
+
+ def __enter__(self):
+ self._ray_cm = add_ray_callbacks(self)
+ self._ray_cm.__enter__()
+ super().__enter__()
+ return self
+
+ def __exit__(self, *args):
+ super().__exit__(*args)
+ self._ray_cm.__exit__(*args)
+
+ def register(self):
+ type(self).ray_active.add(self._ray_callback)
+ super().register()
+
+ def unregister(self):
+ type(self).ray_active.remove(self._ray_callback)
+ super().unregister()
+
+
+class add_ray_callbacks:
+ def __init__(self, *callbacks):
+ self.callbacks = [normalize_ray_callback(c) for c in callbacks]
+ RayDaskCallback.ray_active.update(self.callbacks)
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, *args):
+ for c in self.callbacks:
+ RayDaskCallback.ray_active.discard(c)
+
+
+def normalize_ray_callback(cb):
+ if isinstance(cb, RayDaskCallback):
+ return cb._ray_callback
+ elif isinstance(cb, RayCallback):
+ return cb
+ else:
+ raise TypeError(
+ "Callbacks must be either 'RayDaskCallback' or 'RayCallback' "
+ "namedtuple")
+
+
+def unpack_ray_callbacks(cbs):
+ """Take an iterable of callbacks, return a list of each callback."""
+ if cbs:
+ # Only drop callback methods that aren't in CBS_DONT_DROP.
+ return RayCallbacks(*(
+ [cb for cb in cbs_ if cb or CBS[idx] in CBS_DONT_DROP] or None
+ for idx, cbs_ in enumerate(zip(*cbs))))
+ else:
+ return RayCallbacks(*([()] * len(CBS)))
+
+
[email protected]
+def local_ray_callbacks(callbacks=None):
+ """
+ Allows Dask-Ray callbacks to work with nested schedulers.
+
+ Callbacks will only be used by the first started scheduler they encounter.
+ This means that only the outermost scheduler will use global callbacks.
+ """
+ global_callbacks = callbacks is None
+ if global_callbacks:
+ callbacks, RayDaskCallback.ray_active = (RayDaskCallback.ray_active,
+ set())
+ try:
+ yield callbacks or ()
+ finally:
+ if global_callbacks:
+ RayDaskCallback.ray_active = callbacks
diff --git a/python/ray/util/dask/scheduler.py b/python/ray/util/dask/scheduler.py
--- a/python/ray/util/dask/scheduler.py
+++ b/python/ray/util/dask/scheduler.py
@@ -10,6 +10,7 @@
from dask.system import CPU_COUNT
from dask.threaded import pack_exception, _thread_get_id
+from .callbacks import local_ray_callbacks, unpack_ray_callbacks
from .common import unpack_object_refs
main_thread = threading.current_thread()
@@ -30,12 +31,14 @@ def ray_dask_get(dsk, keys, **kwargs):
>>> dask.compute(obj, scheduler=ray_dask_get)
- You can override the number of threads to use when submitting the
- Ray tasks, or the threadpool used to submit Ray tasks:
+ You can override the currently active global Dask-Ray callbacks (e.g.
+ supplied via a context manager), the number of threads to use when
+ submitting the Ray tasks, or the threadpool used to submit Ray tasks:
>>> dask.compute(
obj,
scheduler=ray_dask_get,
+ ray_callbacks=some_ray_dask_callbacks,
num_workers=8,
pool=some_cool_pool,
)
@@ -44,6 +47,7 @@ def ray_dask_get(dsk, keys, **kwargs):
dsk (Dict): Dask graph, represented as a task DAG dictionary.
keys (List[str]): List of Dask graph keys whose values we wish to
compute and return.
+ ray_callbacks (Optional[list[callable]]): Dask-Ray callbacks.
num_workers (Optional[int]): The number of worker threads to use in
the Ray task submission traversal of the Dask graph.
pool (Optional[ThreadPool]): A multiprocessing threadpool to use to
@@ -73,23 +77,48 @@ def ray_dask_get(dsk, keys, **kwargs):
atexit.register(pool.close)
pools[thread][num_workers] = pool
- # NOTE: We hijack Dask's `get_async` function, injecting a different task
- # executor.
- object_refs = get_async(
- _apply_async_wrapper(pool.apply_async, _rayify_task_wrapper),
- len(pool._pool),
- dsk,
- keys,
- get_id=_thread_get_id,
- pack_exception=pack_exception,
- **kwargs,
- )
- # NOTE: We explicitly delete the Dask graph here so object references
- # are garbage-collected before this function returns, i.e. before all Ray
- # tasks are done. Otherwise, no intermediate objects will be cleaned up
- # until all Ray tasks are done.
- del dsk
- result = ray_get_unpack(object_refs)
+ ray_callbacks = kwargs.pop("ray_callbacks", None)
+
+ with local_ray_callbacks(ray_callbacks) as ray_callbacks:
+ # Unpack the Ray-specific callbacks.
+ (
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ ray_postsubmit_all_cbs,
+ ray_finish_cbs,
+ ) = unpack_ray_callbacks(ray_callbacks)
+ # NOTE: We hijack Dask's `get_async` function, injecting a different
+ # task executor.
+ object_refs = get_async(
+ _apply_async_wrapper(
+ pool.apply_async,
+ _rayify_task_wrapper,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ ),
+ len(pool._pool),
+ dsk,
+ keys,
+ get_id=_thread_get_id,
+ pack_exception=pack_exception,
+ **kwargs,
+ )
+ if ray_postsubmit_all_cbs is not None:
+ for cb in ray_postsubmit_all_cbs:
+ cb(object_refs, dsk)
+ # NOTE: We explicitly delete the Dask graph here so object references
+ # are garbage-collected before this function returns, i.e. before all
+ # Ray tasks are done. Otherwise, no intermediate objects will be
+ # cleaned up until all Ray tasks are done.
+ del dsk
+ result = ray_get_unpack(object_refs)
+ if ray_finish_cbs is not None:
+ for cb in ray_finish_cbs:
+ cb(result)
# cleanup pools associated with dead threads.
with pools_lock:
@@ -138,6 +167,10 @@ def _rayify_task_wrapper(
loads,
get_id,
pack_exception,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
):
"""
The core Ray-Dask task execution wrapper, to be given to the thread pool's
@@ -152,6 +185,10 @@ def _rayify_task_wrapper(
loads (callable): A task_info deserializing function.
get_id (callable): An ID generating function.
pack_exception (callable): An exception serializing function.
+ ray_presubmit_cbs (callable): Pre-task submission callbacks.
+ ray_postsubmit_cbs (callable): Post-task submission callbacks.
+ ray_pretask_cbs (callable): Pre-task execution callbacks.
+ ray_posttask_cbs (callable): Post-task execution callbacks.
Returns:
A 3-tuple of the task's key, a literal or a Ray object reference for a
@@ -159,7 +196,15 @@ def _rayify_task_wrapper(
"""
try:
task, deps = loads(task_info)
- result = _rayify_task(task, key, deps)
+ result = _rayify_task(
+ task,
+ key,
+ deps,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ )
id = get_id()
result = dumps((result, id))
failed = False
@@ -169,15 +214,27 @@ def _rayify_task_wrapper(
return key, result, failed
-def _rayify_task(task, key, deps):
+def _rayify_task(
+ task,
+ key,
+ deps,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+):
"""
Rayifies the given task, submitting it as a Ray task to the Ray cluster.
Args:
- task: A Dask graph value, being either a literal, dependency key, Dask
- task, or a list thereof.
- key: The Dask graph key for the given task.
- deps: The dependencies of this task.
+ task (tuple): A Dask graph value, being either a literal, dependency
+ key, Dask task, or a list thereof.
+ key (str): The Dask graph key for the given task.
+ deps (dict): The dependencies of this task.
+ ray_presubmit_cbs (callable): Pre-task submission callbacks.
+ ray_postsubmit_cbs (callable): Post-task submission callbacks.
+ ray_pretask_cbs (callable): Pre-task execution callbacks.
+ ray_posttask_cbs (callable): Post-task execution callbacks.
Returns:
A literal, a Ray object reference representing a submitted task, or a
@@ -186,18 +243,46 @@ def _rayify_task(task, key, deps):
if isinstance(task, list):
# Recursively rayify this list. This will still bottom out at the first
# actual task encountered, inlining any tasks in that task's arguments.
- return [_rayify_task(t, deps) for t in task]
+ return [
+ _rayify_task(
+ t,
+ key,
+ deps,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ ) for t in task
+ ]
elif istask(task):
# Unpacks and repacks Ray object references and submits the task to the
# Ray cluster for execution.
+ if ray_presubmit_cbs is not None:
+ alternate_returns = [
+ cb(task, key, deps) for cb in ray_presubmit_cbs
+ ]
+ for alternate_return in alternate_returns:
+ # We don't submit a Ray task if a presubmit callback returns
+ # a non-`None` value, instead we return said value.
+ # NOTE: This returns the first non-None presubmit callback
+ # return value.
+ if alternate_return is not None:
+ return alternate_return
+
func, args = task[0], task[1:]
# If the function's arguments contain nested object references, we must
# unpack said object references into a flat set of arguments so that
# Ray properly tracks the object dependencies between Ray tasks.
object_refs, repack = unpack_object_refs(args, deps)
# Submit the task using a wrapper function.
- return dask_task_wrapper.options(name=f"dask:{key!s}").remote(
- func, repack, *object_refs)
+ object_ref = dask_task_wrapper.options(name=f"dask:{key!s}").remote(
+ func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *object_refs)
+
+ if ray_postsubmit_cbs is not None:
+ for cb in ray_postsubmit_cbs:
+ cb(task, key, deps, object_ref)
+
+ return object_ref
elif not ishashable(task):
return task
elif task in deps:
@@ -207,7 +292,8 @@ def _rayify_task(task, key, deps):
@ray.remote
-def dask_task_wrapper(func, repack, *args):
+def dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs,
+ *args):
"""
A Ray remote function acting as a Dask task wrapper. This function will
repackage the given flat `args` into its original data structures using
@@ -219,6 +305,9 @@ def dask_task_wrapper(func, repack, *args):
func (callable): The Dask task function to execute.
repack (callable): A function that repackages the provided args into
the original (possibly nested) Python objects.
+ key (str): The Dask key for this task.
+ ray_pretask_cbs (callable): Pre-task execution callbacks.
+ ray_posttask_cbs (callable): Post-task execution callback.
*args (ObjectRef): Ray object references representing the Dask task's
arguments.
@@ -227,11 +316,21 @@ def dask_task_wrapper(func, repack, *args):
dask_task_wrapper.remote() invocation will return a Ray object
reference representing the Ray task's result.
"""
+ if ray_pretask_cbs is not None:
+ pre_states = [
+ cb(key, args) if cb is not None else None for cb in ray_pretask_cbs
+ ]
repacked_args, repacked_deps = repack(args)
# Recursively execute Dask-inlined tasks.
actual_args = [_execute_task(a, repacked_deps) for a in repacked_args]
# Execute the actual underlying Dask task.
- return func(*actual_args)
+ result = func(*actual_args)
+ if ray_posttask_cbs is not None:
+ for cb, pre_state in zip(ray_posttask_cbs, pre_states):
+ if cb is not None:
+ cb(key, result, pre_state)
+
+ return result
def ray_get_unpack(object_refs):
@@ -273,6 +372,15 @@ def ray_dask_get_sync(dsk, keys, **kwargs):
>>> dask.compute(obj, scheduler=ray_dask_get_sync)
+ You can override the currently active global Dask-Ray callbacks (e.g.
+ supplied via a context manager):
+
+ >>> dask.compute(
+ obj,
+ scheduler=ray_dask_get_sync,
+ ray_callbacks=some_ray_dask_callbacks,
+ )
+
Args:
dsk (Dict): Dask graph, represented as a task DAG dictionary.
keys (List[str]): List of Dask graph keys whose values we wish to
@@ -281,18 +389,46 @@ def ray_dask_get_sync(dsk, keys, **kwargs):
Returns:
Computed values corresponding to the provided keys.
"""
- # NOTE: We hijack Dask's `get_async` function, injecting a different task
- # executor.
- object_refs = get_async(
- _apply_async_wrapper(apply_sync, _rayify_task_wrapper),
- 1,
- dsk,
- keys,
- **kwargs,
- )
- # NOTE: We explicitly delete the Dask graph here so object references
- # are garbage-collected before this function returns, i.e. before all Ray
- # tasks are done. Otherwise, no intermediate objects will be cleaned up
- # until all Ray tasks are done.
- del dsk
- return ray_get_unpack(object_refs)
+
+ ray_callbacks = kwargs.pop("ray_callbacks", None)
+
+ with local_ray_callbacks(ray_callbacks) as ray_callbacks:
+ # Unpack the Ray-specific callbacks.
+ (
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ ray_postsubmit_all_cbs,
+ ray_finish_cbs,
+ ) = unpack_ray_callbacks(ray_callbacks)
+ # NOTE: We hijack Dask's `get_async` function, injecting a different
+ # task executor.
+ object_refs = get_async(
+ _apply_async_wrapper(
+ apply_sync,
+ _rayify_task_wrapper,
+ ray_presubmit_cbs,
+ ray_postsubmit_cbs,
+ ray_pretask_cbs,
+ ray_posttask_cbs,
+ ),
+ 1,
+ dsk,
+ keys,
+ **kwargs,
+ )
+ if ray_postsubmit_all_cbs is not None:
+ for cb in ray_postsubmit_all_cbs:
+ cb(object_refs, dsk)
+ # NOTE: We explicitly delete the Dask graph here so object references
+ # are garbage-collected before this function returns, i.e. before all
+ # Ray tasks are done. Otherwise, no intermediate objects will be
+ # cleaned up until all Ray tasks are done.
+ del dsk
+ result = ray_get_unpack(object_refs)
+ if ray_finish_cbs is not None:
+ for cb in ray_finish_cbs:
+ cb(result)
+
+ return result
| diff --git a/python/ray/tests/BUILD b/python/ray/tests/BUILD
--- a/python/ray/tests/BUILD
+++ b/python/ray/tests/BUILD
@@ -79,6 +79,7 @@ py_test_module_list(
"test_command_runner.py",
"test_coordinator_server.py",
"test_dask_scheduler.py",
+ "test_dask_callback.py",
"test_debug_tools.py",
"test_global_state.py",
"test_job.py",
diff --git a/python/ray/tests/test_dask_callback.py b/python/ray/tests/test_dask_callback.py
new file mode 100644
--- /dev/null
+++ b/python/ray/tests/test_dask_callback.py
@@ -0,0 +1,234 @@
+import dask
+import pytest
+
+import ray
+from ray.util.dask import ray_dask_get, RayDaskCallback
+
+
[email protected]
+def add(x, y):
+ return x + y
+
+
+def test_callback_active():
+ """Test that callbacks are active within context"""
+ assert not RayDaskCallback.ray_active
+
+ with RayDaskCallback():
+ assert RayDaskCallback.ray_active
+
+ assert not RayDaskCallback.ray_active
+
+
+def test_presubmit_shortcircuit(ray_start_regular_shared):
+ """
+ Test that presubmit return short-circuits task submission, and that task's
+ result is set to the presubmit return value.
+ """
+
+ class PresubmitShortcircuitCallback(RayDaskCallback):
+ def _ray_presubmit(self, task, key, deps):
+ return 0
+
+ def _ray_postsubmit(self, task, key, deps, object_ref):
+ pytest.fail("_ray_postsubmit shouldn't be called when "
+ "_ray_presubmit returns a value")
+
+ with PresubmitShortcircuitCallback():
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert result == 0
+
+
+def test_pretask_posttask_shared_state(ray_start_regular_shared):
+ """
+ Test that pretask return value is passed to corresponding posttask
+ callback.
+ """
+
+ class PretaskPosttaskCallback(RayDaskCallback):
+ def _ray_pretask(self, key, object_refs):
+ return key
+
+ def _ray_posttask(self, key, result, pre_state):
+ assert pre_state == key
+
+ with PretaskPosttaskCallback():
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert result == 5
+
+
+def test_postsubmit(ray_start_regular_shared):
+ """
+ Test that postsubmit is called after each task.
+ """
+
+ class PostsubmitCallback(RayDaskCallback):
+ def __init__(self, postsubmit_actor):
+ self.postsubmit_actor = postsubmit_actor
+
+ def _ray_postsubmit(self, task, key, deps, object_ref):
+ self.postsubmit_actor.postsubmit.remote(task, key, deps,
+ object_ref)
+
+ @ray.remote
+ class PostsubmitActor:
+ def __init__(self):
+ self.postsubmit_counter = 0
+
+ def postsubmit(self, task, key, deps, object_ref):
+ self.postsubmit_counter += 1
+
+ def get_postsubmit_counter(self):
+ return self.postsubmit_counter
+
+ postsubmit_actor = PostsubmitActor.remote()
+ with PostsubmitCallback(postsubmit_actor):
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert ray.get(postsubmit_actor.get_postsubmit_counter.remote()) == 1
+ assert result == 5
+
+
+def test_postsubmit_all(ray_start_regular_shared):
+ """
+ Test that postsubmit_all is called once.
+ """
+
+ class PostsubmitAllCallback(RayDaskCallback):
+ def __init__(self, postsubmit_all_actor):
+ self.postsubmit_all_actor = postsubmit_all_actor
+
+ def _ray_postsubmit_all(self, object_refs, dsk):
+ self.postsubmit_all_actor.postsubmit_all.remote(object_refs, dsk)
+
+ @ray.remote
+ class PostsubmitAllActor:
+ def __init__(self):
+ self.postsubmit_all_called = False
+
+ def postsubmit_all(self, object_refs, dsk):
+ self.postsubmit_all_called = True
+
+ def get_postsubmit_all_called(self):
+ return self.postsubmit_all_called
+
+ postsubmit_all_actor = PostsubmitAllActor.remote()
+ with PostsubmitAllCallback(postsubmit_all_actor):
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert ray.get(postsubmit_all_actor.get_postsubmit_all_called.remote())
+ assert result == 5
+
+
+def test_finish(ray_start_regular_shared):
+ """
+ Test that finish callback is called once.
+ """
+
+ class FinishCallback(RayDaskCallback):
+ def __init__(self, finish_actor):
+ self.finish_actor = finish_actor
+
+ def _ray_finish(self, result):
+ self.finish_actor.finish.remote(result)
+
+ @ray.remote
+ class FinishActor:
+ def __init__(self):
+ self.finish_called = False
+
+ def finish(self, result):
+ self.finish_called = True
+
+ def get_finish_called(self):
+ return self.finish_called
+
+ finish_actor = FinishActor.remote()
+ with FinishCallback(finish_actor):
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert ray.get(finish_actor.get_finish_called.remote())
+ assert result == 5
+
+
+def test_multiple_callbacks(ray_start_regular_shared):
+ """
+ Test that multiple callbacks are supported.
+ """
+
+ class PostsubmitCallback(RayDaskCallback):
+ def __init__(self, postsubmit_actor):
+ self.postsubmit_actor = postsubmit_actor
+
+ def _ray_postsubmit(self, task, key, deps, object_ref):
+ self.postsubmit_actor.postsubmit.remote(task, key, deps,
+ object_ref)
+
+ @ray.remote
+ class PostsubmitActor:
+ def __init__(self):
+ self.postsubmit_counter = 0
+
+ def postsubmit(self, task, key, deps, object_ref):
+ self.postsubmit_counter += 1
+
+ def get_postsubmit_counter(self):
+ return self.postsubmit_counter
+
+ postsubmit_actor = PostsubmitActor.remote()
+ cb1 = PostsubmitCallback(postsubmit_actor)
+ cb2 = PostsubmitCallback(postsubmit_actor)
+ cb3 = PostsubmitCallback(postsubmit_actor)
+ with cb1, cb2, cb3:
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert ray.get(postsubmit_actor.get_postsubmit_counter.remote()) == 3
+ assert result == 5
+
+
+def test_pretask_posttask_shared_state_multi(ray_start_regular_shared):
+ """
+ Test that pretask return values are passed to the correct corresponding
+ posttask callbacks when multiple callbacks are given.
+ """
+
+ class PretaskPosttaskCallback(RayDaskCallback):
+ def __init__(self, suffix):
+ self.suffix = suffix
+
+ def _ray_pretask(self, key, object_refs):
+ return key + self.suffix
+
+ def _ray_posttask(self, key, result, pre_state):
+ assert pre_state == key + self.suffix
+
+ class PretaskOnlyCallback(RayDaskCallback):
+ def _ray_pretask(self, key, object_refs):
+ return "baz"
+
+ class PosttaskOnlyCallback(RayDaskCallback):
+ def _ray_posttask(self, key, result, pre_state):
+ assert pre_state is None
+
+ cb1 = PretaskPosttaskCallback("foo")
+ cb2 = PretaskOnlyCallback()
+ cb3 = PosttaskOnlyCallback()
+ cb4 = PretaskPosttaskCallback("bar")
+ with cb1, cb2, cb3, cb4:
+ z = add(2, 3)
+ result = z.compute(scheduler=ray_dask_get)
+
+ assert result == 5
+
+
+if __name__ == "__main__":
+ import sys
+ sys.exit(pytest.main(["-v", __file__]))
| [dask-on-ray] Add support for scheduler plugin callbacks.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Feature Request
Dask provides a [scheduler plugin callback abstraction](https://docs.dask.org/en/latest/diagnostics-local.html#custom-callbacks) that allows users to hook into the scheduler and each task's execution lifecycle, running user-defined callbacks at different stages. Having this plugin infrastructure would be very useful for implementing Dask-level progress reporting, diagnostics, caching, etc.
### API and Semantics
The same as Dask's [existing scheduler plugin callback abstraction](https://docs.dask.org/en/latest/diagnostics-local.html#custom-callbacks): a class that can be instantiated with the callbacks given as constructor args, or can be subclassed, with the callbacks implemented as instance methods on the derived class.
#### Desired Hooks
1. The 5 existing Dask callback hooks, used to hook into the Rayification lifecycle.
2. Ray presubmit - Run before submitting a Ray task.
3. Ray postsubmit - Run after submitting a Ray task.
4. Ray pretask - Run before executing a Dask task within a Ray task.
5. Ray posttask - Run after executing a Dask task within a Ray task.
6. Ray postsubmit all - Run after all Ray tasks for the Dask graph have been submitted.
7. Ray finish - Run after all Ray tasks have finished executing and a result has been returned.
Each of these hooks will have the appropriate signature, e.g. Ray presubmit will be given the Dask task, the Dask key, and the task deps, while Ray pretask will be given the Dask key, the Dask task function, and the object reference arguments.
| 2020-09-03T01:51:10 |
|
ray-project/ray | 10,531 | ray-project__ray-10531 | [
"9245"
] | 2a7f56e42926d3b481eb27edf7bab0dbe5ab73b9 | diff --git a/python/ray/tune/schedulers/hb_bohb.py b/python/ray/tune/schedulers/hb_bohb.py
--- a/python/ray/tune/schedulers/hb_bohb.py
+++ b/python/ray/tune/schedulers/hb_bohb.py
@@ -93,7 +93,7 @@ def _unpause_trial(self, trial_runner, trial):
trial_runner.trial_executor.unpause_trial(trial)
trial_runner._search_alg.searcher.on_unpause(trial.trial_id)
- def choose_trial_to_run(self, trial_runner):
+ def choose_trial_to_run(self, trial_runner, allow_recurse=True):
"""Fair scheduling within iteration by completion percentage.
List of trials not used since all trials are tracked as state
@@ -117,8 +117,17 @@ def choose_trial_to_run(self, trial_runner):
for bracket in hyperband:
if bracket and any(trial.status == Trial.PAUSED
for trial in bracket.current_trials()):
- # This will change the trial state and let the
- # trial runner retry.
+ # This will change the trial state
self._process_bracket(trial_runner, bracket)
+
+ # If there are pending trials now, suggest one.
+ # This is because there might be both PENDING and
+ # PAUSED trials now, and PAUSED trials will raise
+ # an error before the trial runner tries again.
+ if allow_recurse and any(
+ trial.status == Trial.PENDING
+ for trial in bracket.current_trials()):
+ return self.choose_trial_to_run(
+ trial_runner, allow_recurse=False)
# MAIN CHANGE HERE!
return None
diff --git a/python/ray/tune/schedulers/hyperband.py b/python/ray/tune/schedulers/hyperband.py
--- a/python/ray/tune/schedulers/hyperband.py
+++ b/python/ray/tune/schedulers/hyperband.py
@@ -282,7 +282,8 @@ def debug_string(self):
for i, band in enumerate(self._hyperbands):
out += "\nRound #{}:".format(i)
for bracket in band:
- out += "\n {}".format(bracket)
+ if bracket:
+ out += "\n {}".format(bracket)
return out
def state(self):
| diff --git a/python/ray/tune/tests/test_trial_scheduler.py b/python/ray/tune/tests/test_trial_scheduler.py
--- a/python/ray/tune/tests/test_trial_scheduler.py
+++ b/python/ray/tune/tests/test_trial_scheduler.py
@@ -689,6 +689,30 @@ def result(score, ts):
self.assertTrue("hyperband_info" in spy_result)
self.assertEquals(spy_result["hyperband_info"]["budget"], 1)
+ def testPauseResumeChooseTrial(self):
+ def result(score, ts):
+ return {"episode_reward_mean": score, TRAINING_ITERATION: ts}
+
+ sched = HyperBandForBOHB(max_t=10, reduction_factor=3, mode="min")
+ runner = _MockTrialRunner(sched)
+ runner._search_alg = MagicMock()
+ runner._search_alg.searcher = MagicMock()
+ trials = [Trial("__fake") for i in range(3)]
+ for t in trials:
+ runner.add_trial(t)
+ runner._launch_trial(t)
+
+ all_results = [result(1, 5), result(2, 1), result(3, 5)]
+ for trial, trial_result in zip(trials, all_results):
+ decision = sched.on_trial_result(runner, trial, trial_result)
+ self.assertEqual(decision, TrialScheduler.PAUSE)
+ runner._pause_trial(trial)
+
+ run_trial = sched.choose_trial_to_run(runner)
+ self.assertEqual(run_trial, trials[1])
+ self.assertSequenceEqual([t.status for t in trials],
+ [Trial.PAUSED, Trial.PENDING, Trial.PAUSED])
+
class _MockTrial(Trial):
def __init__(self, i, config):
| [tune] BOHB with DQN and MountainCar-v0 fails
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
I am using BOHB to optimize the hyperparameters for the DQN algorithm in order to solve the MountainCar-v0 problem.
I alway run into the following issue (even if I use different values for num_samples):
```python
== Status ==
Memory usage on this node: 7.0/15.6 GiB
Using HyperBand: num_stopped=832 total_brackets=3
Round #0:
None
Bracket(Max Size (n)=2, Milestone (r)=1458, completed=100.0%): {RUNNING: 1, TERMINATED: 833}
Bracket(Max Size (n)=324, Milestone (r)=8, completed=47.3%): {PAUSED: 166}
Resources requested: 4/32 CPUs, 0/0 GPUs, 0.0/8.69 GiB heap, 0.0/2.98 GiB objects
Result logdir: /home/dl-user/ray_results/MCv0_DQN_BOHB
Number of trials: 1000 (166 PAUSED, 1 RUNNING, 833 TERMINATED)
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
| Trial name | status | loc | batch_mode | lr | train_batch_size | iter | total time (s) | ts | reward |
|-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------|
| DQN_MountainCar-v0_0428be42 | PAUSED | | truncate_episodes | 1.99095e-05 | 408 | 2 | 25.6885 | 4032 | -200 |
| DQN_MountainCar-v0_0428be45 | PAUSED | | truncate_episodes | 0.000382289 | 211 | 2 | 24.7536 | 5040 | -200 |
| DQN_MountainCar-v0_0428be48 | PAUSED | | truncate_episodes | 0.000324929 | 233 | 2 | 25.5532 | 5040 | -200 |
| DQN_MountainCar-v0_0747e5f2 | PAUSED | | truncate_episodes | 0.000114766 | 38 | 2 | 23.8492 | 7056 | -200 |
| DQN_MountainCar-v0_0747e5f5 | PAUSED | | truncate_episodes | 9.1226e-05 | 200 | 2 | 24.2349 | 5040 | -200 |
| DQN_MountainCar-v0_08218bf0 | PAUSED | | truncate_episodes | 0.000284028 | 69 | 2 | 25.3671 | 7056 | -200 |
| DQN_MountainCar-v0_093c0b8c | PAUSED | | truncate_episodes | 0.00237606 | 114 | 2 | 23.3935 | 6048 | -200 |
| DQN_MountainCar-v0_0a55eae6 | PAUSED | | truncate_episodes | 0.000417829 | 111 | 2 | 23.4849 | 6048 | -200 |
| DQN_MountainCar-v0_0b307d56 | PAUSED | | truncate_episodes | 0.000196047 | 59 | 2 | 23.1338 | 7056 | -200 |
| DQN_MountainCar-v0_0eedea91 | PAUSED | | truncate_episodes | 6.58278e-05 | 59 | 2 | 24.0254 | 7056 | -200 |
| DQN_MountainCar-v0_1fcd888b | RUNNING | 172.16.160.219:47910 | truncate_episodes | 0.000237864 | 751 | 88 | 1638.34 | 199584 | -122.05 |
| DQN_MountainCar-v0_0023f4f6 | TERMINATED | | truncate_episodes | 0.000255833 | 158 | 1 | 5.56779 | 1008 | -200 |
| DQN_MountainCar-v0_0023f4f9 | TERMINATED | | complete_episodes | 0.000262904 | 156 | 1 | 5.43817 | 1200 | -200 |
| DQN_MountainCar-v0_0023f4fc | TERMINATED | | complete_episodes | 0.0002605 | 260 | 1 | 5.33452 | 1200 | -200 |
| DQN_MountainCar-v0_0108428e | TERMINATED | | truncate_episodes | 3.89327e-05 | 732 | 4 | 36.2218 | 5040 | -200 |
| DQN_MountainCar-v0_01084291 | TERMINATED | | truncate_episodes | 2.39745e-05 | 714 | 4 | 36.2585 | 5040 | -200 |
| DQN_MountainCar-v0_01084294 | TERMINATED | | truncate_episodes | 4.9252e-05 | 808 | 4 | 38.4182 | 5040 | -200 |
| DQN_MountainCar-v0_01084297 | TERMINATED | | truncate_episodes | 7.42384e-05 | 804 | 4 | 38.0425 | 5040 | -200 |
| DQN_MountainCar-v0_014223c0 | TERMINATED | | truncate_episodes | 0.0520328 | 71 | 1 | 6.21906 | 1008 | -200 |
| DQN_MountainCar-v0_01939ac4 | TERMINATED | | complete_episodes | 8.34678e-05 | 124 | 1 | 5.37302 | 1200 | -200 |
| DQN_MountainCar-v0_01a4cc45 | TERMINATED | | complete_episodes | 0.00973094 | 373 | 3 | 27.2147 | 24000 | -200 |
+-----------------------------+------------+----------------------+-------------------+-------------+--------------------+--------+------------------+--------+----------+
... 980 more trials not shown (156 PAUSED, 823 TERMINATED)
Traceback (most recent call last):
File "/home/dl-user/python-code/modularized_version_ray/ray_BOHB.py", line 123, in <module>
verbose=1,
File "/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/tune.py", line 327, in run
runner.step()
File "/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 342, in step
self.trial_executor.on_no_available_trials(self)
File "/home/dl-user/.local/lib/python3.7/site-packages/ray/tune/trial_executor.py", line 177, in on_no_available_trials
raise TuneError("There are paused trials, but no more pending "
ray.tune.error.TuneError: There are paused trials, but no more pending trials with sufficient resources.
Process finished with exit code 1
```
By the way, why is the first bracket ``none``?
And also it seems that the whole process was pretty slow.
Is there any systematic way to find out the bottlenecks?
You guys are doing a great job with this framework! Thanks in advance for your help!
*Ray version and other system information (Python version, TensorFlow version, OS):*
OS: Ubuntu 16.04
Python Version: 3.7.8
Ray Version: 0.8.6
### Reproduction (REQUIRED)
```python
import ray
from ray import tune
from ray.tune.suggest.bohb import TuneBOHB
from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
import ConfigSpace as CS
ray.init()
config_space = CS.ConfigurationSpace()
config_space.add_hyperparameter(CS.UniformFloatHyperparameter("lr", lower=0.00001, upper=0.1, log=True))
config_space.add_hyperparameter(CS.UniformIntegerHyperparameter("train_batch_size", lower=32, upper=1024, log=False))
config_space.add_hyperparameter(CS.CategoricalHyperparameter("batch_mode", choices=['truncate_episodes', 'complete_episodes']))
bohb_search = TuneBOHB(
space=config_space,
bohb_config=None,
max_concurrent=32,
metric='episode_reward_mean',
mode='max'
)
bohb_hyperband = HyperBandForBOHB(
time_attr='episodes_total',
metric='episode_reward_mean',
mode='max',
max_t=2000,
reduction_factor=3,
)
config = {
"env": "MountainCar-v0",
"min_iter_time_s": 15,
"num_gpus": 0,
"num_workers": 1,
"double_q": True,
"n_step": 3,
"target_network_update_freq": 1000,
"buffer_size": 20000,
# "prioritzed_replay": True,
"learning_starts": 1000,
"log_level": "ERROR"
}
analysis = tune.run(
run_or_experiment="DQN",
name='MCv0_DQN_BOHB',
config=config,
num_samples=1000,
checkpoint_at_end=True,
search_alg=bohb_search,
scheduler=bohb_hyperband,
verbose=1,
)
```
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| Hey - I have a very similar issue with BOHB, with very similar code to above.
It seems to work when you use "training_iteration" but not when you use episodes or timesteps, with batch size as a hyperparameter. It seems to break when the number of update steps is not uniform across agents.
Not sure if this helps someone resolve the issue. It would be great if it can be resolved! | 2020-09-03T13:23:21 |
ray-project/ray | 10,547 | ray-project__ray-10547 | [
"10503"
] | 0c0b0d0a73fc7fed019b164336382ed6ee3c04ea | diff --git a/python/ray/experimental/dask/scheduler.py b/python/ray/experimental/dask/scheduler.py
--- a/python/ray/experimental/dask/scheduler.py
+++ b/python/ray/experimental/dask/scheduler.py
@@ -196,7 +196,8 @@ def _rayify_task(task, key, deps):
# Ray properly tracks the object dependencies between Ray tasks.
object_refs, repack = unpack_object_refs(args, deps)
# Submit the task using a wrapper function.
- return dask_task_wrapper.remote(func, repack, *object_refs)
+ return dask_task_wrapper.options(name=f"dask:{key!s}").remote(
+ func, repack, *object_refs)
elif not ishashable(task):
return task
elif task in deps:
| [dask-on-ray] Set task name to Dask key at task submission time.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### Describe your feature request
To provide better dashboard, metrics, and debugging UX, we should set the task name to the Dask key. The Dask-Ray scheduler [uses a single remote function](https://github.com/ray-project/ray/blob/ef18893fb50c97c2c0d9d7b8b951455683f89635/python/ray/experimental/dask/scheduler.py#L208-L233) for executing all Dask tasks; Dask task functions are all dynamically composed (via partial application and the like), and using this single remote function allows us to cut down on function exporting overhead. However, this means that the default task name (derived from the function descriptor) is the same for every Dask task, which hurts dashboard and debugging UX considerably. By setting the task name to the Dask key at task submission time, we'll be able to attach meaningful semantics to each task that will be displayed in the dashboard and included in task spec log records.
### Blockers
Blocked by https://github.com/ray-project/ray/issues/10371.
| 2020-09-03T19:29:46 |
||
ray-project/ray | 10,563 | ray-project__ray-10563 | [
"10562"
] | f38dba09b2cd07a27d1e9659176c8a60972bfd44 | diff --git a/python/ray/utils.py b/python/ray/utils.py
--- a/python/ray/utils.py
+++ b/python/ray/utils.py
@@ -365,9 +365,10 @@ def resources_from_resource_arguments(
elif default_num_gpus is not None:
resources["GPU"] = default_num_gpus
- memory = default_memory or runtime_memory
- object_store_memory = (default_object_store_memory
- or runtime_object_store_memory)
+ # Order of arguments matter for short circuiting.
+ memory = runtime_memory or default_memory
+ object_store_memory = (runtime_object_store_memory
+ or default_object_store_memory)
if memory is not None:
resources["memory"] = ray_constants.to_memory_units(
memory, round_up=True)
diff --git a/rllib/evaluation/worker_set.py b/rllib/evaluation/worker_set.py
--- a/rllib/evaluation/worker_set.py
+++ b/rllib/evaluation/worker_set.py
@@ -98,9 +98,10 @@ def add_workers(self, num_workers: int) -> None:
remote_args = {
"num_cpus": self._remote_config["num_cpus_per_worker"],
"num_gpus": self._remote_config["num_gpus_per_worker"],
- "memory": self._remote_config["memory_per_worker"],
- "object_store_memory": self._remote_config[
- "object_store_memory_per_worker"],
+ # memory=0 is an error, but memory=None means no limits.
+ "memory": self._remote_config["memory_per_worker"] or None,
+ "object_store_memory": self.
+ _remote_config["object_store_memory_per_worker"] or None,
"resources": self._remote_config["custom_resources_per_worker"],
}
cls = RolloutWorker.as_remote(**remote_args).remote
| diff --git a/python/ray/tests/test_basic.py b/python/ray/tests/test_basic.py
--- a/python/ray/tests/test_basic.py
+++ b/python/ray/tests/test_basic.py
@@ -341,6 +341,30 @@ def test_function_descriptor():
assert d.get(python_descriptor2) == 123
+def test_ray_options(shutdown_only):
+ @ray.remote(
+ num_cpus=2, num_gpus=3, memory=150 * 2**20, resources={"custom1": 1})
+ def foo():
+ return ray.available_resources()
+
+ ray.init(num_cpus=10, num_gpus=10, resources={"custom1": 2})
+
+ without_options = ray.get(foo.remote())
+ with_options = ray.get(
+ foo.options(
+ num_cpus=3,
+ num_gpus=4,
+ memory=50 * 2**20,
+ resources={
+ "custom1": 0.5
+ }).remote())
+
+ to_check = ["CPU", "GPU", "memory", "custom1"]
+ for key in to_check:
+ assert without_options[key] != with_options[key]
+ assert without_options != with_options
+
+
def test_nested_functions(ray_start_shared_local_modes):
# Make sure that remote functions can use other values that are defined
# after the remote function but before the first function invocation.
| [Core] `ray.options(memory=)` not overriding default.
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```
>>> @ray.remote(memory=2**30)
... def foo():
... print(f"cluster memory: {ray.cluster_resources()['memory']}")
... print(f"available memory: {ray.available_resources()['memory']}")
...
>>> ray.get(foo.remote())
(pid=41181) cluster memory: 103.0
(pid=41181) available memory: 82.0
>>> ray.get(foo.options(memory=2**31).remote())
(pid=41181) cluster memory: 103.0
(pid=41181) available memory: 82.0
```
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-04T04:36:30 |
|
ray-project/ray | 10,593 | ray-project__ray-10593 | [
"10492"
] | c4c0857107ee4c4b982667339e0e4c5d33d97e96 | diff --git a/python/ray/job_config.py b/python/ray/job_config.py
--- a/python/ray/job_config.py
+++ b/python/ray/job_config.py
@@ -15,7 +15,7 @@ class JobConfig:
def __init__(
self,
worker_env=None,
- num_java_workers_per_process=10,
+ num_java_workers_per_process=1,
jvm_options=None,
):
if worker_env is None:
| diff --git a/java/test.sh b/java/test.sh
--- a/java/test.sh
+++ b/java/test.sh
@@ -33,15 +33,18 @@ bazel build //java:gen_maven_deps
echo "Build test jar."
bazel build //java:all_tests_deploy.jar
+# Enable multi-worker feature in Java test
+TEST_ARGS=(-Dray.raylet.config.num_workers_per_process_java=10 -Dray.job.num-java-workers-per-process=10)
+
echo "Running tests under cluster mode."
# TODO(hchen): Ideally, we should use the following bazel command to run Java tests. However, if there're skipped tests,
# TestNG will exit with code 2. And bazel treats it as test failure.
# bazel test //java:all_tests --action_env=ENABLE_MULTI_LANGUAGE_TESTS=1 --config=ci || cluster_exit_code=$?
-ENABLE_MULTI_LANGUAGE_TESTS=1 run_testng java -cp "$ROOT_DIR"/../bazel-bin/java/all_tests_deploy.jar org.testng.TestNG -d /tmp/ray_java_test_output "$ROOT_DIR"/testng.xml
+ENABLE_MULTI_LANGUAGE_TESTS=1 run_testng java -cp "$ROOT_DIR"/../bazel-bin/java/all_tests_deploy.jar "${TEST_ARGS[@]}" org.testng.TestNG -d /tmp/ray_java_test_output "$ROOT_DIR"/testng.xml
echo "Running tests under single-process mode."
# bazel test //java:all_tests --jvmopt="-Dray.run-mode=SINGLE_PROCESS" --config=ci || single_exit_code=$?
-run_testng java -Dray.run-mode="SINGLE_PROCESS" -cp "$ROOT_DIR"/../bazel-bin/java/all_tests_deploy.jar org.testng.TestNG -d /tmp/ray_java_test_output "$ROOT_DIR"/testng.xml
+run_testng java -Dray.run-mode="SINGLE_PROCESS" -cp "$ROOT_DIR"/../bazel-bin/java/all_tests_deploy.jar "${TEST_ARGS[@]}" org.testng.TestNG -d /tmp/ray_java_test_output "$ROOT_DIR"/testng.xml
popd
diff --git a/java/test/src/main/java/io/ray/test/ClassLoaderTest.java b/java/test/src/main/java/io/ray/test/ClassLoaderTest.java
--- a/java/test/src/main/java/io/ray/test/ClassLoaderTest.java
+++ b/java/test/src/main/java/io/ray/test/ClassLoaderTest.java
@@ -40,7 +40,7 @@ public void tearDown() {
@Test(groups = {"cluster"})
public void testClassLoaderInMultiThreading() throws Exception {
- Assert.assertTrue(TestUtils.getRuntime().getRayConfig().numWorkersPerProcess > 1);
+ Assert.assertTrue(TestUtils.getNumWorkersPerProcess() > 1);
final String jobResourcePath = resourcePath + "/" + Ray.getRuntimeContext().getCurrentJobId();
File jobResourceDir = new File(jobResourcePath);
diff --git a/java/test/src/main/java/io/ray/test/ExitActorTest.java b/java/test/src/main/java/io/ray/test/ExitActorTest.java
--- a/java/test/src/main/java/io/ray/test/ExitActorTest.java
+++ b/java/test/src/main/java/io/ray/test/ExitActorTest.java
@@ -73,7 +73,7 @@ public void testExitActor() throws IOException, InterruptedException {
}
public void testExitActorInMultiWorker() {
- Assert.assertTrue(TestUtils.getRuntime().getRayConfig().numWorkersPerProcess > 1);
+ Assert.assertTrue(TestUtils.getNumWorkersPerProcess() > 1);
ActorHandle<ExitingActor> actor1 = Ray.actor(ExitingActor::new)
.setMaxRestarts(10000).remote();
int pid = actor1.task(ExitingActor::getPid).remote().get();
diff --git a/java/test/src/main/java/io/ray/test/FailureTest.java b/java/test/src/main/java/io/ray/test/FailureTest.java
--- a/java/test/src/main/java/io/ray/test/FailureTest.java
+++ b/java/test/src/main/java/io/ray/test/FailureTest.java
@@ -21,17 +21,20 @@ public class FailureTest extends BaseTest {
private static final String EXCEPTION_MESSAGE = "Oops";
+ private String oldNumWorkersPerProcess;
+
@BeforeClass
public void setUp() {
// This is needed by `testGetThrowsQuicklyWhenFoundException`.
// Set one worker per process. Otherwise, if `badFunc2` and `slowFunc` run in the same
// process, `sleep` will delay `System.exit`.
+ oldNumWorkersPerProcess = System.getProperty("ray.raylet.config.num_workers_per_process_java");
System.setProperty("ray.raylet.config.num_workers_per_process_java", "1");
}
@AfterClass
public void tearDown() {
- System.clearProperty("ray.raylet.config.num_workers_per_process_java");
+ System.setProperty("ray.raylet.config.num_workers_per_process_java", oldNumWorkersPerProcess);
}
public static int badFunc() {
diff --git a/java/test/src/main/java/io/ray/test/JobConfigTest.java b/java/test/src/main/java/io/ray/test/JobConfigTest.java
--- a/java/test/src/main/java/io/ray/test/JobConfigTest.java
+++ b/java/test/src/main/java/io/ray/test/JobConfigTest.java
@@ -1,7 +1,6 @@
package io.ray.test;
import io.ray.api.ActorHandle;
-import io.ray.api.ObjectRef;
import io.ray.api.Ray;
import org.testng.Assert;
import org.testng.annotations.AfterClass;
@@ -11,9 +10,12 @@
@Test(groups = {"cluster"})
public class JobConfigTest extends BaseTest {
+ private String oldNumWorkersPerProcess;
+
@BeforeClass
public void setupJobConfig() {
System.setProperty("ray.raylet.config.enable_multi_tenancy", "true");
+ oldNumWorkersPerProcess = System.getProperty("ray.job.num-java-workers-per-process");
System.setProperty("ray.job.num-java-workers-per-process", "3");
System.setProperty("ray.job.jvm-options.0", "-DX=999");
System.setProperty("ray.job.jvm-options.1", "-DY=998");
@@ -24,7 +26,7 @@ public void setupJobConfig() {
@AfterClass
public void tearDownJobConfig() {
System.clearProperty("ray.raylet.config.enable_multi_tenancy");
- System.clearProperty("ray.job.num-java-workers-per-process");
+ System.setProperty("ray.job.num-java-workers-per-process", oldNumWorkersPerProcess);
System.clearProperty("ray.job.jvm-options.0");
System.clearProperty("ray.job.jvm-options.1");
System.clearProperty("ray.job.worker-env.foo1");
@@ -39,16 +41,8 @@ public static String getEnvVariable(String key) {
return System.getenv(key);
}
- public static Integer getWorkersNum() {
- return TestUtils.getRuntime().getRayConfig().numWorkersPerProcess;
- }
-
public static class MyActor {
- public Integer getWorkersNum() {
- return TestUtils.getRuntime().getRayConfig().numWorkersPerProcess;
- }
-
public String getJvmOptions(String propertyName) {
return System.getProperty(propertyName);
}
@@ -68,9 +62,8 @@ public void testWorkerEnvVariable() {
Assert.assertEquals("bar2", Ray.task(JobConfigTest::getEnvVariable, "foo2").remote().get());
}
- public void testNumJavaWorkerPerProcess() {
- ObjectRef<Integer> obj = Ray.task(JobConfigTest::getWorkersNum).remote();
- Assert.assertEquals(3, (int) obj.get());
+ public void testNumJavaWorkersPerProcess() {
+ Assert.assertEquals(TestUtils.getNumWorkersPerProcess(), 3);
}
@@ -84,9 +77,5 @@ public void testInActor() {
// test worker env variables
Assert.assertEquals("bar1", Ray.task(MyActor::getEnvVariable, "foo1").remote().get());
Assert.assertEquals("bar2", Ray.task(MyActor::getEnvVariable, "foo2").remote().get());
-
- // test workers number.
- ObjectRef<Integer> obj2 = actor.task(MyActor::getWorkersNum).remote();
- Assert.assertEquals(3, (int) obj2.get());
}
}
diff --git a/java/test/src/main/java/io/ray/test/KillActorTest.java b/java/test/src/main/java/io/ray/test/KillActorTest.java
--- a/java/test/src/main/java/io/ray/test/KillActorTest.java
+++ b/java/test/src/main/java/io/ray/test/KillActorTest.java
@@ -14,14 +14,17 @@
@Test(groups = {"cluster"})
public class KillActorTest extends BaseTest {
+ private String oldNumWorkersPerProcess;
+
@BeforeClass
public void setUp() {
+ oldNumWorkersPerProcess = System.getProperty("ray.raylet.config.num_workers_per_process_java");
System.setProperty("ray.raylet.config.num_workers_per_process_java", "1");
}
@AfterClass
public void tearDown() {
- System.clearProperty("ray.raylet.config.num_workers_per_process_java");
+ System.setProperty("ray.raylet.config.num_workers_per_process_java", oldNumWorkersPerProcess);
}
public static class HangActor {
diff --git a/java/test/src/main/java/io/ray/test/MultiThreadingTest.java b/java/test/src/main/java/io/ray/test/MultiThreadingTest.java
--- a/java/test/src/main/java/io/ray/test/MultiThreadingTest.java
+++ b/java/test/src/main/java/io/ray/test/MultiThreadingTest.java
@@ -19,7 +19,7 @@
import org.testng.Assert;
import org.testng.annotations.Test;
-@Test
+@Test(groups = {"cluster"})
public class MultiThreadingTest extends BaseTest {
private static final Logger LOGGER = LoggerFactory.getLogger(MultiThreadingTest.class);
@@ -221,11 +221,6 @@ static boolean testMissingWrapRunnable() throws InterruptedException {
return true;
}
- @Test
- public void testMissingWrapRunnableInDriver() throws InterruptedException {
- testMissingWrapRunnable();
- }
-
@Test
public void testMissingWrapRunnableInWorker() {
Ray.task(MultiThreadingTest::testMissingWrapRunnable).remote().get();
diff --git a/java/test/src/main/java/io/ray/test/TestUtils.java b/java/test/src/main/java/io/ray/test/TestUtils.java
--- a/java/test/src/main/java/io/ray/test/TestUtils.java
+++ b/java/test/src/main/java/io/ray/test/TestUtils.java
@@ -3,6 +3,7 @@
import com.google.common.base.Preconditions;
import io.ray.api.ObjectRef;
import io.ray.api.Ray;
+import io.ray.runtime.AbstractRayRuntime;
import io.ray.runtime.RayRuntimeInternal;
import io.ray.runtime.RayRuntimeProxy;
import io.ray.runtime.config.RunMode;
@@ -83,8 +84,19 @@ public static RayRuntimeInternal getRuntime() {
}
public static RayRuntimeInternal getUnderlyingRuntime() {
+ if (Ray.internal() instanceof AbstractRayRuntime) {
+ return (RayRuntimeInternal) Ray.internal();
+ }
RayRuntimeProxy proxy = (RayRuntimeProxy) (java.lang.reflect.Proxy
.getInvocationHandler(Ray.internal()));
return proxy.getRuntimeObject();
}
+
+ private static int getNumWorkersPerProcessRemoteFunction() {
+ return TestUtils.getRuntime().getRayConfig().numWorkersPerProcess;
+ }
+
+ public static int getNumWorkersPerProcess() {
+ return Ray.task(TestUtils::getNumWorkersPerProcessRemoteFunction).remote().get();
+ }
}
| Make the multi-worker feature for Java worker experimental
Right now, the multi-worker feature for Java worker is enabled by default, but the `ActorHandle::kill()` API doesn't work well if multi-worker is enabled because it will kill the whole process instead of one worker in the process.
To avoid complaints from Java users, we should disable the multi-worker feature by default, but we still enable it in unit test.
| #7291 | 2020-09-05T00:36:46 |
ray-project/ray | 10,672 | ray-project__ray-10672 | [
"10668"
] | d7c7aba99cc2e6383af605aaa4671f4266fad979 | diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py
--- a/python/ray/scripts/scripts.py
+++ b/python/ray/scripts/scripts.py
@@ -622,7 +622,7 @@ def start(node_ip_address, redis_address, address, redis_port, port,
" ray stop".format(
redis_address, " --redis-password='" + redis_password + "'"
if redis_password else "",
- ", redis_password='" + redis_password + "'"
+ ", _redis_password='" + redis_password + "'"
if redis_password else ""))
else:
# Start Ray on a non-head node.
@@ -1472,7 +1472,7 @@ def memory(address, redis_password):
if not address:
address = services.find_redis_address_or_die()
logger.info(f"Connecting to Ray instance at {address}.")
- ray.init(address=address, redis_password=redis_password)
+ ray.init(address=address, _redis_password=redis_password)
print(ray.internal.internal_api.memory_summary())
| A lot of Ray commands fail in nightly build
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
This is because they keep passing the deleted `redis_password` keyword.
For example,
```
$ ray memory
2020-09-09 05:24:50,248 INFO scripts.py:1474 -- Connecting to Ray instance at 172.31.56.46:6379.
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/bin/ray", line 8, in <module>
sys.exit(main())
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py", line 1602, in main
return cli()
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py", line 782, in main
rv = self.invoke(ctx)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py", line 1259, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/click/core.py", line 610, in invoke
return callback(*args, **kwargs)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ray/scripts/scripts.py", line 1475, in memory
ray.init(address=address, redis_password=redis_password)
TypeError: init() got an unexpected keyword argument 'redis_password'
```
Also I feel confused about many prompts and docs still mentioning `redis_password` while it no longer exists in `ray.init`
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-09T06:13:47 |
||
ray-project/ray | 10,680 | ray-project__ray-10680 | [
"10426"
] | 799318d7d785881b7dc89aad817284683a4ea4f5 | diff --git a/python/ray/tune/integration/wandb.py b/python/ray/tune/integration/wandb.py
--- a/python/ray/tune/integration/wandb.py
+++ b/python/ray/tune/integration/wandb.py
@@ -9,6 +9,8 @@
from ray.tune.logger import Logger
from ray.tune.utils import flatten_dict
+import yaml
+
try:
import wandb
except ImportError:
@@ -29,6 +31,12 @@ def _clean_log(obj):
# Else
try:
pickle.dumps(obj)
+ yaml.dump(
+ obj,
+ Dumper=yaml.SafeDumper,
+ default_flow_style=False,
+ allow_unicode=True,
+ encoding="utf-8")
return obj
except Exception:
# give up, similar to _SafeFallBackEncoder
| [rllib] Weights & Biases logger cannot handle objects in configuration
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
The Weights & Biases logger cannot handle object references in RLlib configurations, for example in the callback API.
```
Process _WandbLoggingProcess-1:
Traceback (most recent call last):
File "/usr/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "[...]/ray/tune/integration/wandb.py", line 127, in run
wandb.init(*self.args, **self.kwargs)
File "[...]/wandb/__init__.py", line 1303, in init
as_defaults=not allow_val_change)
File "[...]/wandb/wandb_config.py", line 333, in _update
self.persist()
File "[...]/wandb/wandb_config.py", line 238, in persist
conf_file.write(str(self))
File "[...]/wandb/wandb_config.py", line 374, in __str__
allow_unicode=True, encoding='utf-8')
File "[...]/yaml/__init__.py", line 290, in dump
return dump_all([data], stream, Dumper=Dumper, **kwds)
File "[...]/yaml/__init__.py", line 278, in dump_all
dumper.represent(data)
File "[...]/yaml/representer.py", line 27, in represent
node = self.represent_data(data)
File "[...]/yaml/representer.py", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File "[...]/yaml/representer.py", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File "[...]/yaml/representer.py", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File "[...]/yaml/representer.py", line 48, in represent_data
node = self.yaml_representers[data_types[0]](self, data)
File "[...]/yaml/representer.py", line 207, in represent_dict
return self.represent_mapping('tag:yaml.org,2002:map', data)
File "[...]/yaml/representer.py", line 118, in represent_mapping
node_value = self.represent_data(item_value)
File "[...]/yaml/representer.py", line 58, in represent_data
node = self.yaml_representers[None](self, data)
File "[...]/yaml/representer.py", line 231, in represent_undefined
raise RepresenterError("cannot represent an object", data)
yaml.representer.RepresenterError: ('cannot represent an object', <class '__main__.MyCallbacks'>)
```
*Ray version and other system information (Python version, TensorFlow version, OS):*
- Ray 0.8.7
- Ubuntu 18.04
- Python 3.7
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```
from ray import tune
from ray.rllib.agents.ppo import PPOTrainer
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.tune.integration.wandb import WandbLogger
class MyCallbacks(DefaultCallbacks):
def on_episode_end(self, worker, base_env, policies, episode, **kwargs):
print("Episode ended")
tune.run(
PPOTrainer,
checkpoint_freq=1,
config={
"framework": "torch",
"num_workers": 8,
"num_gpus": 1,
"env": "CartPole-v0",
"callbacks": MyCallbacks,
"logger_config": {
"wandb": {
"project": "test",
"api_key_file": "./wandb_api_key_file",
}
}
},
stop={
"training_iteration":10
},
loggers=[WandbLogger]
)
```
If we cannot run your script, we cannot fix your issue.
- [X] I have verified my script runs in a clean environment and reproduces the issue.
- [X] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| I noticed that the callback is already [explicitly filtered out in the result handling](https://github.com/ray-project/ray/blob/0aec4cbccb5862a7808f98b90696236da9e2a03e/python/ray/tune/integration/wandb.py#L159), but of course that doesn't help during initialization. Is there any reason why objects shouldn't be generally filtered out? Probably only numbers and strings make sense to log anyway?
cc @krfricke
Thanks. For the moment I submitted a [tiny PR](https://github.com/ray-project/ray/pull/10441) to fix the callback API specifically. I agree it makes sense to filter out invalid values, I'll get back to this soon.
@krfricke I am having a similar problem trying to use APPO (PPO works fine). See https://pastebin.com/SdpNp0Pq
Hey @janblumenkamp and @rhamnett this should be fixed via #10654. If this issue prevails, feel free to re-open!
@krfricke @richardliaw Afraid that I am still seeing the same error when running an APPO tune job
Hi @rhamnett, did you install the latest nightly ray release? Do you have a script to reproduce the error?
@krfricke yes I am familiar with re-installing the nightly and can confirm that I have done that correctly. My script is lengthy and contains private intellectual property, so I am wondering if you can just simply adapt a PPO test you might have to APPO (should be trivial) and then let me know if you can re-produce?
If after trying that, you can't reproduce, I will seek to dedicate some time to give you an example script. | 2020-09-09T17:18:24 |
|
ray-project/ray | 10,703 | ray-project__ray-10703 | [
"10692"
] | e72838c03d87c8eb10f7cb8df8804496b14121ab | diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -64,6 +64,9 @@ def __init__(self,
process_runner=subprocess,
update_interval_s=AUTOSCALER_UPDATE_INTERVAL_S):
self.config_path = config_path
+ # Keep this before self.reset (self.provider needs to be created
+ # exactly once).
+ self.provider = None
self.reset(errors_fatal=True)
self.load_metrics = load_metrics
@@ -250,6 +253,7 @@ def _update(self):
self.should_update(node_id) for node_id in nodes):
if node_id is not None:
resources = self._node_resources(node_id)
+ logger.debug(f"{node_id}: Starting new thread runner.")
T.append(
threading.Thread(
target=self.spawn_updater,
@@ -295,9 +299,9 @@ def reset(self, errors_fatal=False):
self.config = new_config
self.runtime_hash = new_runtime_hash
self.file_mounts_contents_hash = new_file_mounts_contents_hash
-
- self.provider = get_node_provider(self.config["provider"],
- self.config["cluster_name"])
+ if not self.provider:
+ self.provider = get_node_provider(self.config["provider"],
+ self.config["cluster_name"])
# Check whether we can enable the resource demand scheduler.
if "available_node_types" in self.config:
self.available_node_types = self.config["available_node_types"]
@@ -462,6 +466,8 @@ def should_update(self, node_id):
def spawn_updater(self, node_id, init_commands, ray_start_commands,
node_resources, docker_config):
+ logger.info(f"Creating new (spawn_updater) updater thread for node"
+ f" {node_id}.")
updater = NodeUpdaterThread(
node_id=node_id,
provider_config=self.config["provider"],
@@ -492,6 +498,8 @@ def can_update(self, node_id):
return False
if self.num_failed_updates.get(node_id, 0) > 0: # TODO(ekl) retry?
return False
+ logger.debug(f"{node_id} is not being updated and "
+ "passes config check (can_update=True).")
return True
def launch_new_node(self, count: int, node_type: Optional[str]) -> None:
diff --git a/python/ray/autoscaler/commands.py b/python/ray/autoscaler/commands.py
--- a/python/ray/autoscaler/commands.py
+++ b/python/ray/autoscaler/commands.py
@@ -455,7 +455,7 @@ def kill_node(config_file, yes, hard, override_cluster_name):
def monitor_cluster(cluster_config_file, num_lines, override_cluster_name):
"""Tails the autoscaler logs of a Ray cluster."""
- cmd = "tail -n {} -f /tmp/ray/session_*/logs/monitor*".format(num_lines)
+ cmd = f"tail -n {num_lines} -f /tmp/ray/session_latest/logs/monitor*"
exec_cluster(
cluster_config_file,
cmd=cmd,
@@ -717,7 +717,7 @@ def get_or_create_head_node(config,
logger, "get_or_create_head_node: "
"Head node up-to-date, IP address is: {}", head_node_ip)
- monitor_str = "tail -n 100 -f /tmp/ray/session_*/logs/monitor*"
+ monitor_str = "tail -n 100 -f /tmp/ray/session_latest/logs/monitor*"
if override_cluster_name:
modifiers = " --cluster-name={}".format(
quote(override_cluster_name))
| [autoscaler] monitor.py has a memory/thread leak
### What is the problem?
With this cluster yaml, you can watch monitor.py via `htop` quickly eat up all of the available memory.
It also seems to be creating hundreds of NodeUpdater threads.
*Ray version and other system information (Python version, TensorFlow version, OS):* `master` latest wheels
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```
cluster_name: memoryleak
min_workers: 2
max_workers: 2
initial_workers: 2
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 60
provider:
type: aws
region: us-east-1
cache_stopped_nodes: false
auth:
ssh_user: ubuntu
head_node:
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 300
DeleteOnTermination: true
VolumeType: gp2
ImageId: latest_dlami
InstanceType: t3.xlarge
worker_nodes:
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 300
DeleteOnTermination: true
VolumeType: gp2
ImageId: latest_dlami
InstanceType: t3.xlarge
file_mounts: {}
cluster_synced_files:
- ~/my-cluster-synced-file
file_mounts_sync_continuously: true
initialization_commands: []
setup_commands:
- pip install --user -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
head_setup_commands:
- tmux new-session -d "python3 -m http.server"
- pip install --user -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
- sleep 5
- curl localhost:8000 -o ~/my-cluster-synced-file
worker_setup_commands: []
```
cc @ericl @wuisawesome
| @wuisawesome could this be related to your new reloading code that creates a new node provider on config update?
Just did some digging - we cannot create providers constantly, as is currently done in `autoscaler.reset()`.
```
==> /tmp/ray/session_2020-09-10_02-30-21_292916_30660/logs/monitor.err <==
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 368, in <module>
monitor.run()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 120, in update
self.reset(errors_fatal=False)
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 302, in reset
self.config["cluster_name"])
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/node_provider.py", line 148, in get_node_provider
return provider_cls(provider_config, cluster_name)
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/aws/node_provider.py", line 55, in __init__
traceback.print_stack()
==> /tmp/ray/session_2020-09-10_02-30-21_292916_30660/logs/monitor.out <==
Creating a new NODE PROVIDER!
==> /tmp/ray/session_2020-09-10_02-30-21_292916_30660/logs/monitor.err <==
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 368, in <module>
monitor.run()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 120, in update
self.reset(errors_fatal=False)
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 302, in reset
self.config["cluster_name"])
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/node_provider.py", line 148, in get_node_provider
return provider_cls(provider_config, cluster_name)
File "/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/aws/node_provider.py", line 55, in __init__
traceback.print_stack()
```
Seems like this is a necessary fix (or at least you need to clean up the existing provider):
```
@@ -295,9 +297,9 @@ class StandardAutoscaler:
self.config = new_config
self.runtime_hash = new_runtime_hash
self.file_mounts_contents_hash = new_file_mounts_contents_hash
-
- self.provider = get_node_provider(self.config["provider"],
- self.config["cluster_name"])
+ if not self.provider:
+ self.provider = get_node_provider(self.config["provider"],
+ self.config["cluster_name"])
# Check whether we can enable the resource demand scheduler.
if "available_node_types" in self.config:
self.available_node_types = self.config["available_node_types"] | 2020-09-10T05:08:52 |
|
ray-project/ray | 10,705 | ray-project__ray-10705 | [
"10702"
] | eb025ea8cb27583e8ef6287f5654f23d1ab270ef | diff --git a/python/ray/autoscaler/updater.py b/python/ray/autoscaler/updater.py
--- a/python/ray/autoscaler/updater.py
+++ b/python/ray/autoscaler/updater.py
@@ -78,17 +78,28 @@ def __init__(self,
self.process_runner = process_runner
self.node_id = node_id
self.provider = provider
+ # Some node providers don't specify empty structures as
+ # defaults. Better to be defensive.
+ file_mounts = file_mounts or {}
self.file_mounts = {
remote: os.path.expanduser(local)
for remote, local in file_mounts.items()
}
+
self.initialization_commands = initialization_commands
self.setup_commands = setup_commands
self.ray_start_commands = ray_start_commands
self.node_resources = node_resources
self.runtime_hash = runtime_hash
self.file_mounts_contents_hash = file_mounts_contents_hash
- self.cluster_synced_files = cluster_synced_files
+ # TODO (Alex): This makes the assumption that $HOME on the head and
+ # worker nodes is the same. Also note that `cluster_synced_files` is
+ # set on the head -> worker updaters only (so `expanduser` is only run
+ # on the head node).
+ cluster_synced_files = cluster_synced_files or []
+ self.cluster_synced_files = [
+ os.path.expanduser(path) for path in cluster_synced_files
+ ]
self.auth_config = auth_config
self.is_head_node = is_head_node
@@ -166,6 +177,8 @@ def sync_file_mounts(self, sync_cmd, step_numbers=(0, 2)):
def do_sync(remote_path, local_path, allow_non_existing_paths=False):
if allow_non_existing_paths and not os.path.exists(local_path):
+ cli_logger.print("sync: {} does not exist. Skipping.",
+ local_path)
# Ignore missing source files. In the future we should support
# the --delete-missing-args command to delete files that have
# been removed
@@ -199,17 +212,21 @@ def do_sync(remote_path, local_path, allow_non_existing_paths=False):
_numbered=("[]", previous_steps + 1, total_steps)):
for remote_path, local_path in self.file_mounts.items():
do_sync(remote_path, local_path)
+ previous_steps += 1
if self.cluster_synced_files:
with cli_logger.group(
"Processing worker file mounts",
- _numbered=("[]", previous_steps + 2, total_steps)):
+ _numbered=("[]", previous_steps + 1, total_steps)):
+ cli_logger.print("synced files: {}",
+ str(self.cluster_synced_files))
for path in self.cluster_synced_files:
do_sync(path, path, allow_non_existing_paths=True)
+ previous_steps += 1
else:
cli_logger.print(
"No worker file mounts to sync",
- _numbered=("[]", previous_steps + 2, total_steps))
+ _numbered=("[]", previous_steps + 1, total_steps))
def wait_ready(self, deadline):
with cli_logger.group(
| [autoscaler] cluster_synced_files doesn't support `~`
### What is the problem?
`~/filename` is marked as "nonexistent" despite actually existing on the cluster.
*Ray version and other system information (Python version, TensorFlow version, OS):* master
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
```
sent 10,834 bytes received 637 bytes 22,942.00 bytes/sec
total size is 684,320 speedup is 59.66
/home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/ from /home/ubuntu/.local/lib/python3.7/site-packages/ray/autoscaler/
[4/6] Processing worker file mounts
synced files: ['~/my-cluster-synced-file']
Does ~/my-cluster-synced-file exist? False
```
```
cluster_name: SYNC_TEST
min_workers: 2
max_workers: 2
initial_workers: 2
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 60
provider:
type: aws
region: us-east-1
cache_stopped_nodes: false
auth:
ssh_user: ubuntu
head_node:
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 350
DeleteOnTermination: true
VolumeType: gp2
ImageId: latest_dlami
InstanceType: t3.xlarge
worker_nodes:
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 300
DeleteOnTermination: true
VolumeType: gp2
ImageId: latest_dlami
InstanceType: t3.xlarge
cluster_synced_files:
- ~/my-cluster-synced-file
file_mounts_sync_continuously: true
initialization_commands: []
setup_commands:
- pip install --user https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
head_setup_commands:
- tmux new-session -d "python3 -m http.server"
- pip install --user https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
- sleep 5
- curl localhost:8000 -o ~/my-cluster-synced-file
worker_setup_commands: []
```
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-10T05:28:00 |
||
ray-project/ray | 10,706 | ray-project__ray-10706 | [
"10690"
] | 7aa55ca82cfe9c1985b341dae81dfc9914617bf0 | diff --git a/python/ray/autoscaler/autoscaler.py b/python/ray/autoscaler/autoscaler.py
--- a/python/ray/autoscaler/autoscaler.py
+++ b/python/ray/autoscaler/autoscaler.py
@@ -430,6 +430,8 @@ def _get_node_type_specific_fields(self, node_id: str,
return fields
def _get_node_specific_docker_config(self, node_id):
+ if "docker" not in self.config:
+ return {}
docker_config = copy.deepcopy(self.config.get("docker", {}))
node_specific_docker = self._get_node_type_specific_fields(
node_id, "docker")
| diff --git a/python/ray/tests/test_resource_demand_scheduler.py b/python/ray/tests/test_resource_demand_scheduler.py
--- a/python/ray/tests/test_resource_demand_scheduler.py
+++ b/python/ray/tests/test_resource_demand_scheduler.py
@@ -605,6 +605,32 @@ def testUpdateConfig(self):
autoscaler.update()
self.waitForNodes(0)
+ def testEmptyDocker(self):
+ config = MULTI_WORKER_CLUSTER.copy()
+ del config["docker"]
+ config["min_workers"] = 0
+ config["max_workers"] = 10
+ config_path = self.write_config(config)
+ self.provider = MockProvider()
+ runner = MockProcessRunner()
+ autoscaler = StandardAutoscaler(
+ config_path,
+ LoadMetrics(),
+ max_failures=0,
+ process_runner=runner,
+ update_interval_s=0)
+ assert len(self.provider.non_terminated_nodes({})) == 0
+ autoscaler.update()
+ self.waitForNodes(0)
+ autoscaler.request_resources([{"CPU": 1}])
+ autoscaler.update()
+ self.waitForNodes(1)
+ assert self.provider.mock_nodes[0].node_type == "m4.large"
+ autoscaler.request_resources([{"GPU": 8}])
+ autoscaler.update()
+ self.waitForNodes(2)
+ assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
+
if __name__ == "__main__":
import sys
| [k8s][autoscaler] Possible command parsing issue, worker nodes quickly dying
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
There seem to be multiple bugs in the kuburnetes autoscaler. When running `ray`, commands appear to be parsed incorrectly, resulting in the failure to start the ray runtime on the head node. After manually entering the pod on the head node and starting the ray runtime, a worker node is briefly spawned and quickly dies.
### Version/System
I'm working off of the latest master branch ray v0.9.0dev0, with Python3.7 on Ubuntu 20.04. I'm running on a preexisting Kubernetes cluster of two c5.2xlarge nodes on AWS.
### Reproduction
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
I use the provided example-full.yaml file with the only difference being the following configuration:
```
min_workers: 1
max_workers: 1
initial_workers: 1
```
Started with the following command:
`ray up python/ray/autoscaler/kubernetes/example-full.yaml`
This produces the following output. The ray runtime fails to start.
```
(myenv) tcw@pop-os:~/projects/ray$ ray up example-full.yaml
Commands running under a login shell can produce more output than special processing can handle.
Thus, the output from subcommands will be logged as is.
Consider using --use-normal-shells, if you tested your workflow and it is compatible.
Cluster configuration valid
Cluster: default
2020-09-09 20:16:16,175 INFO config.py:68 -- KubernetesNodeProvider: namespace 'ray' not found, attempting to create it
2020-09-09 20:16:16,213 INFO config.py:72 -- KubernetesNodeProvider: successfully created namespace 'ray'
2020-09-09 20:16:16,250 INFO config.py:97 -- KubernetesNodeProvider: autoscaler_service_account 'autoscaler' not found, attempting to create it
2020-09-09 20:16:16,291 INFO config.py:99 -- KubernetesNodeProvider: successfully created autoscaler_service_account 'autoscaler'
2020-09-09 20:16:16,401 INFO config.py:123 -- KubernetesNodeProvider: autoscaler_role 'autoscaler' not found, attempting to create it
2020-09-09 20:16:16,436 INFO config.py:125 -- KubernetesNodeProvider: successfully created autoscaler_role 'autoscaler'
2020-09-09 20:16:16,473 INFO config.py:156 -- KubernetesNodeProvider: autoscaler_role_binding 'autoscaler' not found, attempting to create it
2020-09-09 20:16:16,518 INFO config.py:158 -- KubernetesNodeProvider: successfully created autoscaler_role_binding 'autoscaler'
2020-09-09 20:16:16,554 INFO config.py:189 -- KubernetesNodeProvider: service 'ray-head' not found, attempting to create it
2020-09-09 20:16:16,613 INFO config.py:191 -- KubernetesNodeProvider: successfully created service 'ray-head'
2020-09-09 20:16:16,653 INFO config.py:189 -- KubernetesNodeProvider: service 'ray-workers' not found, attempting to create it
2020-09-09 20:16:16,702 INFO config.py:191 -- KubernetesNodeProvider: successfully created service 'ray-workers'
This will create a new cluster [y/N]: y
2020-09-09 20:16:18,507 INFO commands.py:592 -- get_or_create_head_node: Launching new head node...
2020-09-09 20:16:18,507 INFO node_provider.py:87 -- KubernetesNodeProvider: calling create_namespaced_pod (count=1).
2020-09-09 20:16:18,606 INFO commands.py:631 -- get_or_create_head_node: Updating files on head node...
2020-09-09 20:16:18,609 INFO updater.py:97 -- NodeUpdater: ray-head-xn4b5: Updating to c1ad72dff590afc61824084810dc92021a0f3d39
2020-09-09 20:16:18,697 INFO updater.py:219 -- NodeUpdater: ray-head-xn4b5: Waiting for remote shell...
2020-09-09 20:16:18,729 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running ['kubectl', '-n', 'ray', 'exec', '-it', 'ray-head-xn4b5', '--', 'bash', '--login', '-c', '-i', "'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (uptime)'"]
kubectl controls the Kubernetes cluster manager.
Find more information at: https://kubernetes.io/docs/reference/kubectl/overview/
Basic Commands (Beginner):
create Create a resource from a file or from stdin.
expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service
run Run a particular image on the cluster
set Set specific features on objects
Basic Commands (Intermediate):
explain Documentation of resources
get Display one or many resources
edit Edit a resource on the server
delete Delete resources by filenames, stdin, resources and names, or by resources and label selector
Deploy Commands:
rollout Manage the rollout of a resource
scale Set a new size for a Deployment, ReplicaSet or Replication Controller
autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController
Cluster Management Commands:
certificate Modify certificate resources.
cluster-info Display cluster info
top Display Resource (CPU/Memory/Storage) usage.
cordon Mark node as unschedulable
uncordon Mark node as schedulable
drain Drain node in preparation for maintenance
taint Update the taints on one or more nodes
Troubleshooting and Debugging Commands:
describe Show details of a specific resource or group of resources
logs Print the logs for a container in a pod
attach Attach to a running container
exec Execute a command in a container
port-forward Forward one or more local ports to a pod
proxy Run a proxy to the Kubernetes API server
cp Copy files and directories to and from containers.
auth Inspect authorization
Advanced Commands:
diff Diff live version against would-be applied version
apply Apply a configuration to a resource by filename or stdin
patch Update field(s) of a resource using strategic merge patch
replace Replace a resource by filename or stdin
wait Experimental: Wait for a specific condition on one or many resources.
convert Convert config files between different API versions
kustomize Build a kustomization target from a directory or a remote url.
Settings Commands:
label Update the labels on a resource
annotate Update the annotations on a resource
completion Output shell completion code for the specified shell (bash or zsh)
Other Commands:
api-resources Print the supported API resources on the server
api-versions Print the supported API versions on the server, in the form of "group/version"
config Modify kubeconfig files
plugin Provides utilities for interacting with plugins.
version Print the client and server version information
Usage:
kubectl [flags] [options]
Use "kubectl <command> --help" for more information about a given command.
Use "kubectl options" for a list of global command-line options (applies to all commands).
2020-09-09 20:16:18,777 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Got remote shell [LogTimer=80ms]
2020-09-09 20:16:18,905 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running ['kubectl', '-n', 'ray', 'exec', '-it', 'ray-head-xn4b5', '--', 'bash', '--login', '-c', '-i', "'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (mkdir -p ~)'"]
kubectl controls the Kubernetes cluster manager.
Find more information at: https://kubernetes.io/docs/reference/kubectl/overview/
Basic Commands (Beginner):
create Create a resource from a file or from stdin.
expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service
run Run a particular image on the cluster
set Set specific features on objects
Basic Commands (Intermediate):
explain Documentation of resources
get Display one or many resources
edit Edit a resource on the server
delete Delete resources by filenames, stdin, resources and names, or by resources and label selector
Deploy Commands:
rollout Manage the rollout of a resource
scale Set a new size for a Deployment, ReplicaSet or Replication Controller
autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController
Cluster Management Commands:
certificate Modify certificate resources.
cluster-info Display cluster info
top Display Resource (CPU/Memory/Storage) usage.
cordon Mark node as unschedulable
uncordon Mark node as schedulable
drain Drain node in preparation for maintenance
taint Update the taints on one or more nodes
Troubleshooting and Debugging Commands:
describe Show details of a specific resource or group of resources
logs Print the logs for a container in a pod
attach Attach to a running container
exec Execute a command in a container
port-forward Forward one or more local ports to a pod
proxy Run a proxy to the Kubernetes API server
cp Copy files and directories to and from containers.
auth Inspect authorization
Advanced Commands:
diff Diff live version against would-be applied version
apply Apply a configuration to a resource by filename or stdin
patch Update field(s) of a resource using strategic merge patch
replace Replace a resource by filename or stdin
wait Experimental: Wait for a specific condition on one or many resources.
convert Convert config files between different API versions
kustomize Build a kustomization target from a directory or a remote url.
Settings Commands:
label Update the labels on a resource
annotate Update the annotations on a resource
completion Output shell completion code for the specified shell (bash or zsh)
Other Commands:
api-resources Print the supported API resources on the server
api-versions Print the supported API versions on the server, in the form of "group/version"
config Modify kubeconfig files
plugin Provides utilities for interacting with plugins.
version Print the client and server version information
Usage:
kubectl [flags] [options]
Use "kubectl <command> --help" for more information about a given command.
Use "kubectl options" for a list of global command-line options (applies to all commands).
2020-09-09 20:16:18,985 INFO updater.py:420 -- NodeUpdater: ray-head-xn4b5: Syncing /tmp/ray-bootstrap-ixliccej to ~/ray_bootstrap_config.yaml...
error: unable to upgrade connection: container not found ("ray-node")
rsync: connection unexpectedly closed (0 bytes received so far) [sender]
rsync error: error in rsync protocol data stream (code 12) at io.c(235) [sender=3.1.3]
2020-09-09 20:16:19,670 WARNING command_runner.py:255 -- NodeUpdater: ray-head-xn4b5: rsync failed: 'Command '['/home/tcw/projects/mu0_minimal/myenv/lib/python3.7/site-packages/ray/autoscaler/kubernetes/kubectl-rsync.sh', '-avz', '/tmp/ray-bootstrap-ixliccej', 'ray-head-xn4b5@ray:/root/ray_bootstrap_config.yaml']' returned non-zero exit status 12.'. Falling back to 'kubectl cp'
2020-09-09 20:16:20,912 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Synced /tmp/ray-bootstrap-ixliccej to ~/ray_bootstrap_config.yaml [LogTimer=2006ms]
2020-09-09 20:16:21,012 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running ['kubectl', '-n', 'ray', 'exec', '-it', 'ray-head-xn4b5', '--', 'bash', '--login', '-c', '-i', "'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (ray stop)'"]
kubectl controls the Kubernetes cluster manager.
Find more information at: https://kubernetes.io/docs/reference/kubectl/overview/
Basic Commands (Beginner):
create Create a resource from a file or from stdin.
expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service
run Run a particular image on the cluster
set Set specific features on objects
Basic Commands (Intermediate):
explain Documentation of resources
get Display one or many resources
edit Edit a resource on the server
delete Delete resources by filenames, stdin, resources and names, or by resources and label selector
Deploy Commands:
rollout Manage the rollout of a resource
scale Set a new size for a Deployment, ReplicaSet or Replication Controller
autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController
Cluster Management Commands:
certificate Modify certificate resources.
cluster-info Display cluster info
top Display Resource (CPU/Memory/Storage) usage.
cordon Mark node as unschedulable
uncordon Mark node as schedulable
drain Drain node in preparation for maintenance
taint Update the taints on one or more nodes
Troubleshooting and Debugging Commands:
describe Show details of a specific resource or group of resources
logs Print the logs for a container in a pod
attach Attach to a running container
exec Execute a command in a container
port-forward Forward one or more local ports to a pod
proxy Run a proxy to the Kubernetes API server
cp Copy files and directories to and from containers.
auth Inspect authorization
Advanced Commands:
diff Diff live version against would-be applied version
apply Apply a configuration to a resource by filename or stdin
patch Update field(s) of a resource using strategic merge patch
replace Replace a resource by filename or stdin
wait Experimental: Wait for a specific condition on one or many resources.
convert Convert config files between different API versions
kustomize Build a kustomization target from a directory or a remote url.
Settings Commands:
label Update the labels on a resource
annotate Update the annotations on a resource
completion Output shell completion code for the specified shell (bash or zsh)
Other Commands:
api-resources Print the supported API resources on the server
api-versions Print the supported API versions on the server, in the form of "group/version"
config Modify kubeconfig files
plugin Provides utilities for interacting with plugins.
version Print the client and server version information
Usage:
kubectl [flags] [options]
Use "kubectl <command> --help" for more information about a given command.
Use "kubectl options" for a list of global command-line options (applies to all commands).
2020-09-09 20:16:21,057 INFO command_runner.py:223 -- NodeUpdater: ray-head-xn4b5: Running ['kubectl', '-n', 'ray', 'exec', '-it', 'ray-head-xn4b5', '--', 'bash', '--login', '-c', '-i', "'true && source ~/.bashrc && export OMP_NUM_THREADS=1 PYTHONWARNINGS=ignore && (ulimit -n 65536; ray start --head --num-cpus=$MY_CPU_REQUEST --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --webui-host 0.0.0.0)'"]
kubectl controls the Kubernetes cluster manager.
Find more information at: https://kubernetes.io/docs/reference/kubectl/overview/
Basic Commands (Beginner):
create Create a resource from a file or from stdin.
expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service
run Run a particular image on the cluster
set Set specific features on objects
Basic Commands (Intermediate):
explain Documentation of resources
get Display one or many resources
edit Edit a resource on the server
delete Delete resources by filenames, stdin, resources and names, or by resources and label selector
Deploy Commands:
rollout Manage the rollout of a resource
scale Set a new size for a Deployment, ReplicaSet or Replication Controller
autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController
Cluster Management Commands:
certificate Modify certificate resources.
cluster-info Display cluster info
top Display Resource (CPU/Memory/Storage) usage.
cordon Mark node as unschedulable
uncordon Mark node as schedulable
drain Drain node in preparation for maintenance
taint Update the taints on one or more nodes
Troubleshooting and Debugging Commands:
describe Show details of a specific resource or group of resources
logs Print the logs for a container in a pod
attach Attach to a running container
exec Execute a command in a container
port-forward Forward one or more local ports to a pod
proxy Run a proxy to the Kubernetes API server
cp Copy files and directories to and from containers.
auth Inspect authorization
Advanced Commands:
diff Diff live version against would-be applied version
apply Apply a configuration to a resource by filename or stdin
patch Update field(s) of a resource using strategic merge patch
replace Replace a resource by filename or stdin
wait Experimental: Wait for a specific condition on one or many resources.
convert Convert config files between different API versions
kustomize Build a kustomization target from a directory or a remote url.
Settings Commands:
label Update the labels on a resource
annotate Update the annotations on a resource
completion Output shell completion code for the specified shell (bash or zsh)
Other Commands:
api-resources Print the supported API resources on the server
api-versions Print the supported API versions on the server, in the form of "group/version"
config Modify kubeconfig files
plugin Provides utilities for interacting with plugins.
version Print the client and server version information
Usage:
kubectl [flags] [options]
Use "kubectl <command> --help" for more information about a given command.
Use "kubectl options" for a list of global command-line options (applies to all commands).
2020-09-09 20:16:21,096 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Ray start commands succeeded [LogTimer=84ms]
2020-09-09 20:16:21,096 INFO log_timer.py:27 -- NodeUpdater: ray-head-xn4b5: Applied config c1ad72dff590afc61824084810dc92021a0f3d39 [LogTimer=2486ms]
2020-09-09 20:16:21,254 INFO commands.py:718 -- get_or_create_head_node: Head node up-to-date, IP address is: 192.168.6.142
To monitor autoscaling activity, you can run:
ray exec /home/tcw/projects/mu0_minimal/ray/example-full.yaml 'tail -n 100 -f /tmp/ray/session_*/logs/monitor*'
To open a console on the cluster:
ray attach /home/tcw/projects/mu0_minimal/ray/example-full.yaml
To get a remote shell to the cluster manually, run:
kubectl -n ray exec -it ray-head-xn4b5 bash
```
Other ray commands like `ray attach` and `ray down` likewise fail.
Upon entering the head node and starting the ray runtime with
```
$ kubectl -n ray exec -it ray-head-xn4b5 bash
(base) root@ray-head-xn4b5:/# ray start --head --num-cpus=3 --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --webui-host 0.0.0.0
```
and getting logs with
```
(base) root@ray-head-xn4b5:/# tail -n 100 -f /tmp/ray/session_*/logs/monitor*
```
I received the following output:
```
==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.err <==
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.13077020645141602}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0
2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.14269304275512695}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0
2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.
Error in monitor loop
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 132, in update
raise e
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered
2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod
2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)
Error in sys.excepthook:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/worker.py", line 834, in custom_excepthook
worker_id = global_worker.worker_id
AttributeError: 'Worker' object has no attribute 'worker_id'
Original exception was:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 368, in <module>
monitor.run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 132, in update
raise e
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 380, in <module>
redis_client, ray_constants.MONITOR_DIED_ERROR, message)
File "/root/anaconda3/lib/python3.7/site-packages/ray/utils.py", line 128, in push_error_to_driver_through_redis
pubsub_msg.SerializeAsString())
AttributeError: SerializeAsString
==> /tmp/ray/session_2020-09-09_17-20-39_779593_74/logs/monitor.out <==
Destroying cluster. Confirm [y/N]: y [automatic, due to --yes]
1 random worker nodes will not be shut down. (due to --keep-min-workers)
The head node will not be shut down. (due to --workers-only)
No nodes remaining.
==> /tmp/ray/session_latest/logs/monitor.err <==
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,100 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.13077020645141602}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0
2020-09-09 17:21:11,112 INFO autoscaler.py:520 -- Cluster status: 1/1 target nodes (0 pending)
- MostDelayedHeartbeats: {'192.168.6.142': 0.14269304275512695}
- NodeIdleSeconds: Min=29 Mean=29 Max=29
- NumNodesConnected: 1
- NumNodesUsed: 0.0
- ResourceUsage: 0.0/3.0 CPU, 0.0 GiB/1.21 GiB memory, 0.0 GiB/0.42 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0
2020-09-09 17:21:11,128 ERROR autoscaler.py:123 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 CRITICAL autoscaler.py:130 -- StandardAutoscaler: Too many errors, abort.
Error in monitor loop
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 132, in update
raise e
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
2020-09-09 17:21:11,129 ERROR autoscaler.py:554 -- StandardAutoscaler: kill_workers triggered
2020-09-09 17:21:11,134 INFO node_provider.py:121 -- KubernetesNodeProvider: calling delete_namespaced_pod
2020-09-09 17:21:11,148 ERROR autoscaler.py:559 -- StandardAutoscaler: terminated 1 node(s)
Error in sys.excepthook:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/worker.py", line 834, in custom_excepthook
worker_id = global_worker.worker_id
AttributeError: 'Worker' object has no attribute 'worker_id'
Original exception was:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 368, in <module>
monitor.run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 313, in run
self._run()
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 268, in _run
self.autoscaler.update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 132, in update
raise e
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 121, in update
self._update()
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in _update
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 250, in <genexpr>
self.should_update(node_id) for node_id in nodes):
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 456, in should_update
docker_config = self._get_node_specific_docker_config(node_id)
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 431, in _get_node_specific_docker_config
node_id, "docker")
File "/root/anaconda3/lib/python3.7/site-packages/ray/autoscaler/autoscaler.py", line 417, in _get_node_type_specific_fields
fields = self.config[fields_key]
KeyError: 'docker'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.7/site-packages/ray/monitor.py", line 380, in <module>
redis_client, ray_constants.MONITOR_DIED_ERROR, message)
File "/root/anaconda3/lib/python3.7/site-packages/ray/utils.py", line 128, in push_error_to_driver_through_redis
pubsub_msg.SerializeAsString())
AttributeError: SerializeAsString
==> /tmp/ray/session_latest/logs/monitor.out <==
Destroying cluster. Confirm [y/N]: y [automatic, due to --yes]
1 random worker nodes will not be shut down. (due to --keep-min-workers)
The head node will not be shut down. (due to --workers-only)
No nodes remaining.
```
If we cannot run your script, we cannot fix your issue.
- [x] I have verified my script runs in a clean environment and reproduces the issue.
- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| @twillkens thanks a bunch for taking the time to open this issue!
cc @ijrsvt @wuisawesome could you take a look at this? I've marked it as a release blocker for now. | 2020-09-10T05:44:23 |
ray-project/ray | 10,721 | ray-project__ray-10721 | [
"10720"
] | 608bbb1ee7e5624f876a0e7d9882ffc4c27a039c | diff --git a/python/ray/autoscaler/commands.py b/python/ray/autoscaler/commands.py
--- a/python/ray/autoscaler/commands.py
+++ b/python/ray/autoscaler/commands.py
@@ -959,10 +959,11 @@ def rsync(config_file: str,
config = _bootstrap_config(config, no_config_cache=no_config_cache)
is_file_mount = False
- for remote_mount in config.get("file_mounts", {}).keys():
- if remote_mount in (source if down else target):
- is_file_mount = True
- break
+ if source and target:
+ for remote_mount in config.get("file_mounts", {}).keys():
+ if (source if down else target).startswith(remote_mount):
+ is_file_mount = True
+ break
provider = get_node_provider(config["provider"], config["cluster_name"])
try:
| [autoscaler] Rsync breaks when `target` or `source` is None
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
When `rsync` is run with `source` and `target` set to `None` and `file_mounts` are included in the YAML, ray fails with
```
None type is not iterable
```
Introduced by #10633
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-10T16:22:34 |
||
ray-project/ray | 10,734 | ray-project__ray-10734 | [
"10735"
] | 427ba3df47f826f5114c81a98ff29ba1fc8efd13 | diff --git a/python/ray/autoscaler/command_runner.py b/python/ray/autoscaler/command_runner.py
--- a/python/ray/autoscaler/command_runner.py
+++ b/python/ray/autoscaler/command_runner.py
@@ -706,6 +706,10 @@ def _docker_expand_user(self, string, any_char=False):
return string
def run_init(self, *, as_head, file_mounts):
+ BOOTSTRAP_MOUNTS = [
+ "~/ray_bootstrap_config.yaml", "~/ray_bootstrap_key.pem"
+ ]
+
image = self.docker_config.get("image")
image = self.docker_config.get(
f"{'head' if as_head else 'worker'}_image", image)
@@ -717,8 +721,14 @@ def run_init(self, *, as_head, file_mounts):
self.run("docker pull {}".format(image), run_env="host")
+ # Bootstrap files cannot be bind mounted because docker opens the
+ # underlying inode. When the file is switched, docker becomes outdated.
+ cleaned_bind_mounts = file_mounts.copy()
+ for mnt in BOOTSTRAP_MOUNTS:
+ cleaned_bind_mounts.pop(mnt, None)
+
start_command = docker_start_cmds(
- self.ssh_command_runner.ssh_user, image, file_mounts,
+ self.ssh_command_runner.ssh_user, image, cleaned_bind_mounts,
self.container_name,
self.docker_config.get("run_options", []) + self.docker_config.get(
f"{'head' if as_head else 'worker'}_run_options", []))
@@ -743,7 +753,8 @@ def run_init(self, *, as_head, file_mounts):
active_remote_mounts = [
mnt["Destination"] for mnt in active_mounts
]
- for remote, local in file_mounts.items():
+ # Ignore ray bootstrap files.
+ for remote, local in cleaned_bind_mounts.items():
remote = self._docker_expand_user(remote)
if remote not in active_remote_mounts:
cli_logger.error(
@@ -753,4 +764,13 @@ def run_init(self, *, as_head, file_mounts):
cli_logger.verbose(
"Unable to check if file_mounts specified in the YAML "
"differ from those on the running container.")
+
+ # Explicitly copy in ray bootstrap files.
+ for mount in BOOTSTRAP_MOUNTS:
+ if mount in file_mounts:
+ self.ssh_command_runner.run(
+ "docker cp {src} {container}:{dst}".format(
+ src=os.path.join(DOCKER_MOUNT_PREFIX, mount),
+ container=self.container_name,
+ dst=self._docker_expand_user(mount)))
self.initialized = True
| diff --git a/python/ray/tests/test_autoscaler.py b/python/ray/tests/test_autoscaler.py
--- a/python/ray/tests/test_autoscaler.py
+++ b/python/ray/tests/test_autoscaler.py
@@ -54,15 +54,17 @@ def check_call(self, cmd, *args, **kwargs):
def check_output(self, cmd):
self.check_call(cmd)
return_string = "command-output"
- key_to_delete = None
- for pattern, pair in self.call_response.items():
+ key_to_shrink = None
+ for pattern, response_list in self.call_response.items():
if pattern in str(cmd):
- return_string = pair[0]
- if pair[1] - 1 == 0:
- key_to_delete = pattern
+ return_string = response_list[0]
+ key_to_shrink = pattern
break
- if key_to_delete:
- del self.call_response[key_to_delete]
+ if key_to_shrink:
+ self.call_response[key_to_shrink] = self.call_response[
+ key_to_shrink][1:]
+ if len(self.call_response[key_to_shrink]) == 0:
+ del self.call_response[key_to_shrink]
return return_string.encode()
@@ -108,8 +110,8 @@ def assert_not_has_call(self, ip, pattern):
def clear_history(self):
self.calls = []
- def respond_to_call(self, pattern, response, num_times=1):
- self.call_response[pattern] = (response, num_times)
+ def respond_to_call(self, pattern, response_list):
+ self.call_response[pattern] = response_list
class MockProvider(NodeProvider):
@@ -400,6 +402,10 @@ def testGetOrCreateHeadNode(self):
config_path = self.write_config(SMALL_CLUSTER)
self.provider = MockProvider()
runner = MockProcessRunner()
+ runner.respond_to_call("json .Mounts", ["[]"])
+ # Two initial calls to docker cp, one before run, two final calls to cp
+ runner.respond_to_call(".State.Running",
+ ["false", "false", "false", "true", "true"])
get_or_create_head_node(
SMALL_CLUSTER,
config_path,
@@ -414,6 +420,15 @@ def testGetOrCreateHeadNode(self):
runner.assert_has_call("1.2.3.4", "head_setup_cmd")
runner.assert_has_call("1.2.3.4", "start_ray_head")
self.assertEqual(self.provider.mock_nodes[0].node_type, None)
+ runner.assert_has_call("1.2.3.4", pattern="docker run")
+ runner.assert_not_has_call(
+ "1.2.3.4", pattern="-v /tmp/ray_tmp_mount/~/ray_bootstrap_config")
+ runner.assert_has_call(
+ "1.2.3.4",
+ pattern="docker cp /tmp/ray_tmp_mount/~/ray_bootstrap_key.pem")
+ runner.assert_has_call(
+ "1.2.3.4",
+ pattern="docker cp /tmp/ray_tmp_mount/~/ray_bootstrap_config.yaml")
def testScaleUp(self):
config_path = self.write_config(SMALL_CLUSTER)
diff --git a/python/ray/tests/test_command_runner.py b/python/ray/tests/test_command_runner.py
--- a/python/ray/tests/test_command_runner.py
+++ b/python/ray/tests/test_command_runner.py
@@ -205,7 +205,7 @@ def test_docker_rsync():
remote_file = "/root/protected-file"
remote_host_file = f"{DOCKER_MOUNT_PREFIX}{remote_file}"
- process_runner.respond_to_call("docker inspect -f", "true")
+ process_runner.respond_to_call("docker inspect -f", ["true"])
cmd_runner.run_rsync_up(
local_mount, remote_mount, options={"file_mount": True})
@@ -222,7 +222,7 @@ def test_docker_rsync():
process_runner.clear_history()
##############################
- process_runner.respond_to_call("docker inspect -f", "true")
+ process_runner.respond_to_call("docker inspect -f", ["true"])
cmd_runner.run_rsync_up(
local_file, remote_file, options={"file_mount": False})
| [docker] Config updates do not propagate into the container
<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->
### What is the problem?
Docker file mounts are based on the inode of the host file. When `rsync` replaces the host file, it creates a new inode, effectively making the mount useless.
*Ray version and other system information (Python version, TensorFlow version, OS):*
### Reproduction (REQUIRED)
1. Run `ray up myscript.yaml`
2. Update worker nodes to include a larger number of `min/max/initial` `workers`
3. Rerun `ray up myscript.yaml`
4. Run `ray attach myscript.yaml`
5. Open up `~/ray_bootstrap_config.yaml` and note how the changed values _are not reflected there_
If we cannot run your script, we cannot fix your issue.
- [ ] I have verified my script runs in a clean environment and reproduces the issue.
- [ ] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).
| 2020-09-11T08:52:05 |
|
ray-project/ray | 10,750 | ray-project__ray-10750 | [
"10747"
] | 6662efd74b28faa176208523ed0bddc39bf7ec67 | diff --git a/python/ray/services.py b/python/ray/services.py
--- a/python/ray/services.py
+++ b/python/ray/services.py
@@ -6,6 +6,7 @@
import multiprocessing
import os
import random
+import shutil
import signal
import socket
import subprocess
@@ -1314,15 +1315,8 @@ def start_raylet(redis_address,
gcs_ip_address, gcs_port = redis_address.split(":")
has_java_command = False
- try:
- java_proc = subprocess.run(
- ["java", "-version"],
- stdout=subprocess.PIPE,
- stderr=subprocess.PIPE)
- if java_proc.returncode == 0:
- has_java_command = True
- except OSError:
- pass
+ if shutil.which("java") is not None:
+ has_java_command = True
ray_java_installed = False
try:
| [java] Java usage is shown when importing from Python
### What is the problem?
*Ray version and other system information (Python version, TensorFlow version, OS):*
master
### Reproduction (REQUIRED)
Please provide a script that can be run to reproduce the issue. The script should have **no external library dependencies** (i.e., use fake or mock data / environments):
I get shown:
<img width="505" alt="Screen Shot 2020-09-11 at 12 50 54 PM" src="https://user-images.githubusercontent.com/4529381/92967051-701ee500-f42d-11ea-877c-c6ddc3fde1ef.png">
when calling `ray.init()`. The Python library should have no dependency on Java.
| That was easy to find:
```
git diff origin/releases/0.8.7..origin/master python/ray/services.py
```
I betcha dollars to donuts its afa0216280409ff9806640658605e24b64d81ea7, specifically https://github.com/ray-project/ray/commit/afa0216280409ff9806640658605e24b64d81ea7#diff-54ac27010a06993004bec4677c7e583eR1329-R1332
what you really want is to do the equivalent of a `which java` instead of attempting to actually run `java` -- if someone has errantly created a java binary of their own, that's their own fault. Indeed, that dialog is MacOS "helpfully" doing exactly this | 2020-09-11T21:59:48 |
|
ray-project/ray | 10,767 | ray-project__ray-10767 | [
"10763"
] | 9795356ac083eee39b55c2f72428025fbeb6ba13 | diff --git a/python/ray/autoscaler/commands.py b/python/ray/autoscaler/commands.py
--- a/python/ray/autoscaler/commands.py
+++ b/python/ray/autoscaler/commands.py
@@ -730,9 +730,13 @@ def get_or_create_head_node(config,
cf.bold(" ray exec {}{} {}"), config_file, modifiers,
quote(monitor_str))
- cli_logger.print("Connect to a terminal on the cluster head")
+ cli_logger.print("Connect to a terminal on the cluster head:")
cli_logger.print(
cf.bold(" ray attach {}{}"), config_file, modifiers)
+
+ remote_shell_str = updater.cmd_runner.remote_shell_command_str()
+ cli_logger.print("Get a remote shell to the cluster manually:")
+ cli_logger.print(" {}", remote_shell_str.strip())
finally:
provider.cleanup()
| diff --git a/python/ray/tests/test_cli.py b/python/ray/tests/test_cli.py
--- a/python/ray/tests/test_cli.py
+++ b/python/ray/tests/test_cli.py
@@ -91,21 +91,23 @@ def _debug_check_line_by_line(result, expected_lines):
i = 0
for out in output_lines:
- print(out)
-
if i >= len(expected_lines):
i += 1
print("!!!!!! Expected fewer lines")
- print("\n".join(output_lines[i:]))
+ context = [f"CONTEXT: {line}" for line in output_lines[i - 3:i]]
+ print("\n".join(context))
+ extra = [f"-- {line}" for line in output_lines[i:]]
+ print("\n".join(extra))
break
exp = expected_lines[i]
matched = re.fullmatch(exp + r" *", out) is not None
if not matched:
- print(f"!!!!!!! Expected (regex): {repr(exp)}")
+ print(f"!!! ERROR: Expected (regex): {repr(exp)}")
+ print(f"Got: {out}")
i += 1
if i < len(expected_lines):
- print("!!!!!!! Expected (regex):")
+ print("!!! ERROR: Expected extra lines (regex):")
for line in expected_lines[i:]:
print(repr(line))
diff --git a/python/ray/tests/test_cli_patterns/test_ray_up.txt b/python/ray/tests/test_cli_patterns/test_ray_up.txt
--- a/python/ray/tests/test_cli_patterns/test_ray_up.txt
+++ b/python/ray/tests/test_cli_patterns/test_ray_up.txt
@@ -42,5 +42,7 @@ Acquiring an up-to-date head node
Useful commands
Monitor autoscaling with
ray exec .+ 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor\*'
- Connect to a terminal on the cluster head
+ Connect to a terminal on the cluster head:
ray attach .+
+ Get a remote shell to the cluster manually:
+ ssh .+
diff --git a/python/ray/tests/test_cli_patterns/test_ray_up_record.txt b/python/ray/tests/test_cli_patterns/test_ray_up_record.txt
--- a/python/ray/tests/test_cli_patterns/test_ray_up_record.txt
+++ b/python/ray/tests/test_cli_patterns/test_ray_up_record.txt
@@ -71,5 +71,7 @@
.+\.py.*Useful commands
.+\.py.*Monitor autoscaling with
.+\.py.* ray exec .+ 'tail -n 100 -f .+
-.+\.py.*Connect to a terminal on the cluster head
+.+\.py.*Connect to a terminal on the cluster head:
.+\.py.* ray attach .+
+.+\.py.*Get a remote shell to the cluster manually:
+.+\.py.* ssh.+\.pem.+
| [autoscaler] Usability regressions in new autoscaler output
@richardliaw we've regressed the output here, it's a minor issue but super annoying.
```
Useful commands
Monitor autoscaling with
ray exec example-ml.yaml 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor*'
Connect to a terminal on the cluster head
ray attach example-ml.yaml
```
It used to print the absolute path of the yaml, so you can copy paste into any terminal. Now, the user has to navigate to the directory YAML for the command to work.
| Also, didn't we used to print the raw SSH command if you want to use a direct shell? If we don't print that, then users will have a hard time figuring out how to get direct access if they want more custom command execution.
The first issue is fixed on master - https://github.com/ray-project/ray/commit/401195d9359b4d6641776f43706bc3ced414819e#diff-4fd45cbce68a36f9e4f5a688ac099228
If not please provide repro:
```
Useful commands
Monitor autoscaling with
ray exec /Users/rliaw/dev/cfgs/anyscale.yaml 'tail -n 100 -f /tmp/ray/session_latest/logs/monitor*'
Connect to a terminal on the cluster head
ray attach /Users/rliaw/dev/cfgs/anyscale.yaml
```
Thanks!
Actually, how about the raw SSH command?
the raw ssh command is not yet
Hm, maybe it was that when you run "ray attach etc.", it used to print the raw SSH command being executed. Can we ensure this is printed out so the commands aren't so "magic"?
Yeah - I'll add a line. | 2020-09-14T00:06:24 |
ray-project/ray | 10,775 | ray-project__ray-10775 | [
"10653"
] | 829a2307dff07f8775aac2b766c5ffa56976e03a | diff --git a/rllib/agents/ddpg/ddpg_tf_policy.py b/rllib/agents/ddpg/ddpg_tf_policy.py
--- a/rllib/agents/ddpg/ddpg_tf_policy.py
+++ b/rllib/agents/ddpg/ddpg_tf_policy.py
@@ -175,7 +175,6 @@ def ddpg_actor_critic_loss(policy, model, _, train_batch):
if twin_q:
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
- td_error = td_error + twin_td_error
if use_huber:
errors = huber_loss(td_error, huber_threshold) + \
huber_loss(twin_td_error, huber_threshold)
diff --git a/rllib/agents/ddpg/ddpg_torch_policy.py b/rllib/agents/ddpg/ddpg_torch_policy.py
--- a/rllib/agents/ddpg/ddpg_torch_policy.py
+++ b/rllib/agents/ddpg/ddpg_torch_policy.py
@@ -124,7 +124,6 @@ def ddpg_actor_critic_loss(policy, model, _, train_batch):
if twin_q:
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
- td_error = td_error + twin_td_error
if use_huber:
errors = huber_loss(td_error, huber_threshold) \
+ huber_loss(twin_td_error, huber_threshold)
| diff --git a/rllib/agents/ddpg/tests/test_ddpg.py b/rllib/agents/ddpg/tests/test_ddpg.py
--- a/rllib/agents/ddpg/tests/test_ddpg.py
+++ b/rllib/agents/ddpg/tests/test_ddpg.py
@@ -495,7 +495,6 @@ def _ddpg_loss_helper(self, train_batch, weights, ks, fw, gamma,
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
- td_error = td_error + twin_td_error
errors = huber_loss(td_error, huber_threshold) + \
huber_loss(twin_td_error, huber_threshold)
| [rllib] DDPG TWIN_Q LOSS ERROR
The TD3 loss according to OpenAI Spinning up is:
They implement this as you would expect:
````
# MSE loss against Bellman backup
loss_q1 = ((q1 - backup)**2).mean()
loss_q2 = ((q2 - backup)**2).mean()
loss_q = loss_q1 + loss_q2
````
The rllib implementation is off and I ran into training issues when using it.
Tensorflow:
https://github.com/ray-project/ray/blob/39c598bab0929f007a49daa7262a7c9ee6492221/rllib/agents/ddpg/ddpg_tf_policy.py#L174-L184
Pytorch:
https://github.com/ray-project/ray/blob/5851e893eee491e569fe1421314172bd5b37d583/rllib/agents/ddpg/ddpg_torch_policy.py#L124-L134
The error is on line 177 and 127 respectively where the td_error and twin_td_error are added together and then squared a couple lines later. Removing that line would bring the implementation in line with the spinningup reference implementation. This worked for me to improve training on my problem. They also do not multiply by the constant (0.5) but I would not expect that to make much of a difference either way.
| Interesting, do you want to push a fix. Also cc @michaelzhiluo if you could take a look here. | 2020-09-14T13:22:52 |
ray-project/ray | 10,821 | ray-project__ray-10821 | [
"10809"
] | 0d9d34a3c1d584ce875cc121677d2683802e0ea5 | diff --git a/python/ray/utils.py b/python/ray/utils.py
--- a/python/ray/utils.py
+++ b/python/ray/utils.py
@@ -499,22 +499,55 @@ def get_system_memory():
return psutil_memory_in_bytes
-def _get_docker_cpus():
- # 1. Try using CFS Quota (https://bugs.openjdk.java.net/browse/JDK-8146115)
- # 2. Try Nproc (CPU sets)
- cpu_quota_file_name = "/sys/fs/cgroup/cpu/cpu.cfs_quota_us"
- cpu_share_file_name = "/sys/fs/cgroup/cpu/cpu.cfs_period_us"
- num_cpus = 0
+def _get_docker_cpus(
+ cpu_quota_file_name="/sys/fs/cgroup/cpu/cpu.cfs_quota_us",
+ cpu_share_file_name="/sys/fs/cgroup/cpu/cpu.cfs_period_us",
+ cpuset_file_name="/sys/fs/cgroup/cpuset/cpuset.cpus"):
+ # TODO (Alex): Don't implement this logic oursleves.
+ # Docker has 2 underyling ways of implementing CPU limits:
+ # https://docs.docker.com/config/containers/resource_constraints/#configure-the-default-cfs-scheduler
+ # 1. --cpuset-cpus 2. --cpus or --cpu-quota/--cpu-period (--cpu-shares is a
+ # soft limit so we don't worry about it). For Ray's purposes, if we use
+ # docker, the number of vCPUs on a machine is whichever is set (ties broken
+ # by smaller value).
+
+ cpu_quota = None
+ # See: https://bugs.openjdk.java.net/browse/JDK-8146115
if os.path.exists(cpu_quota_file_name) and os.path.exists(
cpu_quota_file_name):
- with open(cpu_quota_file_name, "r") as f:
- num_cpus = int(f.read())
- if num_cpus != -1:
- with open(cpu_share_file_name, "r") as f:
- num_cpus /= int(f.read())
- return num_cpus
-
- return int(subprocess.check_output("nproc"))
+ try:
+ with open(cpu_quota_file_name, "r") as quota_file, open(
+ cpu_share_file_name, "r") as period_file:
+ cpu_quota = float(quota_file.read()) / float(
+ period_file.read())
+ except Exception as e:
+ logger.exception("Unexpected error calculating docker cpu quota.",
+ e)
+ if cpu_quota < 0:
+ cpu_quota = None
+
+ cpuset_num = None
+ if os.path.exists(cpuset_file_name):
+ try:
+ with open(cpuset_file_name) as cpuset_file:
+ ranges_as_string = cpuset_file.read()
+ ranges = ranges_as_string.split(",")
+ cpu_ids = []
+ for num_or_range in ranges:
+ if "-" in num_or_range:
+ start, end = num_or_range.split("-")
+ cpu_ids.extend(list(range(int(start), int(end) + 1)))
+ else:
+ cpu_ids.append(int(num_or_range))
+ cpuset_num = len(cpu_ids)
+ except Exception as e:
+ logger.exception("Unexpected error calculating docker cpuset ids.",
+ e)
+
+ if cpu_quota and cpuset_num:
+ return min(cpu_quota, cpuset_num)
+ else:
+ return cpu_quota or cpuset_num
def get_num_cpus():
@@ -531,8 +564,7 @@ def get_num_cpus():
# Not easy to get cpu count in docker, see:
# https://bugs.python.org/issue36054
docker_count = _get_docker_cpus()
- if docker_count != cpu_count:
- cpu_count = docker_count
+ if docker_count is not None and docker_count != cpu_count:
if "RAY_DISABLE_DOCKER_CPU_WARNING" not in os.environ:
logger.warning(
"Detecting docker specified CPUs. In "
@@ -543,6 +575,15 @@ def get_num_cpus():
"`RAY_USE_MULTIPROCESSING_CPU_COUNT=1` as an env var "
"before starting Ray. Set the env var: "
"`RAY_DISABLE_DOCKER_CPU_WARNING=1` to mute this warning.")
+ # TODO (Alex): We should probably add support for fractional cpus.
+ if int(docker_count) != float(docker_count):
+ logger.warning(
+ f"Ray currently does not support initializing Ray"
+ f"with fractional cpus. Your num_cpus will be "
+ f"truncated from {docker_count} to "
+ f"{int(docker_count)}.")
+ docker_count = int(docker_count)
+ cpu_count = docker_count
except Exception:
# `nproc` and cgroup are linux-only. If docker only works on linux
| diff --git a/python/ray/tests/test_advanced_3.py b/python/ray/tests/test_advanced_3.py
--- a/python/ray/tests/test_advanced_3.py
+++ b/python/ray/tests/test_advanced_3.py
@@ -4,6 +4,7 @@
import os
import sys
import socket
+import tempfile
import time
import numpy as np
@@ -736,6 +737,56 @@ def initialized(self):
assert ray.available_resources()[resource_name] < quantity
+def test_detect_docker_cpus():
+ # No limits set
+ with tempfile.NamedTemporaryFile(
+ "w") as quota_file, tempfile.NamedTemporaryFile(
+ "w") as period_file, tempfile.NamedTemporaryFile(
+ "w") as cpuset_file:
+ quota_file.write("-1")
+ period_file.write("100000")
+ cpuset_file.write("0-63")
+ quota_file.flush()
+ period_file.flush()
+ cpuset_file.flush()
+ assert ray.utils._get_docker_cpus(
+ cpu_quota_file_name=quota_file.name,
+ cpu_share_file_name=period_file.name,
+ cpuset_file_name=cpuset_file.name) == 64
+
+ # No cpuset used
+ with tempfile.NamedTemporaryFile(
+ "w") as quota_file, tempfile.NamedTemporaryFile(
+ "w") as period_file, tempfile.NamedTemporaryFile(
+ "w") as cpuset_file:
+ quota_file.write("-1")
+ period_file.write("100000")
+ cpuset_file.write("0-10,20,50-63")
+ quota_file.flush()
+ period_file.flush()
+ cpuset_file.flush()
+ assert ray.utils._get_docker_cpus(
+ cpu_quota_file_name=quota_file.name,
+ cpu_share_file_name=period_file.name,
+ cpuset_file_name=cpuset_file.name) == 26
+
+ # Quota set
+ with tempfile.NamedTemporaryFile(
+ "w") as quota_file, tempfile.NamedTemporaryFile(
+ "w") as period_file, tempfile.NamedTemporaryFile(
+ "w") as cpuset_file:
+ quota_file.write("42")
+ period_file.write("100")
+ cpuset_file.write("0-63")
+ quota_file.flush()
+ period_file.flush()
+ cpuset_file.flush()
+ assert ray.utils._get_docker_cpus(
+ cpu_quota_file_name=quota_file.name,
+ cpu_share_file_name=period_file.name,
+ cpuset_file_name=cpuset_file.name) == 0.42
+
+
if __name__ == "__main__":
import pytest
sys.exit(pytest.main(["-v", __file__]))
| [docker] / [core] Regression in detection of multi CPU count after docker fix
### What is the problem?
The Ray snapshot 1.1.0 will detect only 1 CPU on certain multi-core systems **when not using docker** due to this change from https://github.com/ray-project/ray/pull/10507/files#diff-dd52f82c5ae34621334f725acaeacc81R502
```
def _detect_docker_cpus()
cpu_quota_file_name = "/sys/fs/cgroup/cpu/cpu.cfs_quota_us"
cpu_share_file_name = "/sys/fs/cgroup/cpu/cpu.cfs_period_us"
num_cpus = 0
if os.path.exists(cpu_quota_file_name) and os.path.exists(
cpu_quota_file_name):
with open(cpu_quota_file_name, "r") as f:
num_cpus = int(f.read())
if num_cpus != -1:
with open(cpu_share_file_name, "r") as f:
num_cpus /= int(f.read())
return num_cpus
```
My current cgroup output is:
```
# cat /proc/self/cgroup
11:cpuset:/
10:hugetlb:/
9:perf_event:/
8:cpu,cpuacct:/
7:devices:/
6:memory:/
5:net_cls,net_prio:/
4:blkio:/
3:freezer:/
2:pids:/
1:name=systemd:/user.slice/user-0.slice/session-362.scope
```
Code was added to python/ray/utils.py which now requires an explicit environmental variable to be set to detect the number of CPUs. This is undesirable.
The code is in the area of ``` if "RAY_USE_MULTIPROCESSING_CPU_COUNT" in os.environ:```
Please provide a better more robust way of detecting whether you are in docker and do not require a generic setup to explicitly set the env variable.
I propose the following to detect docker:
```
def is_docker():
path = '/proc/self/cgroup'
return (
os.path.exists('/.dockerenv') or
os.path.isfile(path) and any('docker' in line for line in open(path))
)
```
| 2020-09-16T02:34:44 |
|
ray-project/ray | 10,840 | ray-project__ray-10840 | [
"10839"
] | 9d0a9e73de7d8daf2f0056dd7f89786db240aa5d | diff --git a/python/ray/tune/__init__.py b/python/ray/tune/__init__.py
--- a/python/ray/tune/__init__.py
+++ b/python/ray/tune/__init__.py
@@ -9,9 +9,9 @@
from ray.tune.trainable import Trainable
from ray.tune.durable_trainable import DurableTrainable
from ray.tune.suggest import grid_search
-from ray.tune.session import (report, get_trial_dir, get_trial_name,
- get_trial_id, make_checkpoint_dir,
- save_checkpoint, checkpoint_dir)
+from ray.tune.session import (
+ report, get_trial_dir, get_trial_name, get_trial_id, make_checkpoint_dir,
+ save_checkpoint, checkpoint_dir, is_session_enabled)
from ray.tune.progress_reporter import (ProgressReporter, CLIReporter,
JupyterNotebookReporter)
from ray.tune.sample import (function, sample_from, uniform, quniform, choice,
@@ -28,6 +28,7 @@
"qrandint", "randn", "qrandn", "loguniform", "qloguniform",
"ExperimentAnalysis", "Analysis", "CLIReporter", "JupyterNotebookReporter",
"ProgressReporter", "report", "get_trial_dir", "get_trial_name",
- "get_trial_id", "make_checkpoint_dir", "save_checkpoint", "checkpoint_dir",
- "SyncConfig", "create_searcher", "create_scheduler"
+ "get_trial_id", "make_checkpoint_dir", "save_checkpoint",
+ "is_session_enabled", "checkpoint_dir", "SyncConfig", "create_searcher",
+ "create_scheduler"
]
diff --git a/python/ray/tune/session.py b/python/ray/tune/session.py
--- a/python/ray/tune/session.py
+++ b/python/ray/tune/session.py
@@ -7,6 +7,12 @@
_session = None
+def is_session_enabled() -> bool:
+ """Returns True if running within an Tune process."""
+ global _session
+ return _session is not None
+
+
def get_session():
global _session
if not _session:
| diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -400,6 +400,18 @@ def train(config, checkpoint_dir=None):
trial_dfs = list(analysis.trial_dataframes.values())
assert len(trial_dfs[0]["training_iteration"]) == 10
+ def testEnabled(self):
+ def train(config, checkpoint_dir=None):
+ is_active = tune.is_session_enabled()
+ if is_active:
+ tune.report(active=is_active)
+ return is_active
+
+ assert train({}) is False
+ analysis = tune.run(train)
+ t = analysis.trials[0]
+ assert t.last_result["active"]
+
def testBlankCheckpoint(self):
def train(config, checkpoint_dir=None):
restored = bool(checkpoint_dir)
| [tune] How to test if I'm running inside a tune session?
Is there an API to test if I'm running inside a tune session? I'd like to conditionally call `tune.report()` in my code.
There are functions like `get_trial_dir`, `get_trial_name`, `get_trial_id`, that internally call `get_session()`. I guess I could use one of them see if they return `None` or not. But they also log a warning when they can't find a session which is not ideal.
| 2020-09-16T22:43:46 |
|
ray-project/ray | 10,874 | ray-project__ray-10874 | [
"10869"
] | 1b295a17cb3913c77568b214ab0ae8f964a2ee37 | diff --git a/python/ray/autoscaler/resource_demand_scheduler.py b/python/ray/autoscaler/resource_demand_scheduler.py
--- a/python/ray/autoscaler/resource_demand_scheduler.py
+++ b/python/ray/autoscaler/resource_demand_scheduler.py
@@ -56,6 +56,7 @@ def get_nodes_to_launch(self, nodes: List[NodeID],
nodes: List of existing nodes in the cluster.
pending_nodes: Summary of node types currently being launched.
resource_demands: Vector of resource demands from the scheduler.
+ usage_by_ip: Mapping from ip to available resources.
"""
if resource_demands is None:
@@ -113,7 +114,6 @@ def add_node(node_type, available_resources=None):
tags = self.provider.node_tags(node_id)
if TAG_RAY_USER_NODE_TYPE in tags:
node_type = tags[TAG_RAY_USER_NODE_TYPE]
- node_type_counts[node_type] += 1
ip = self.provider.internal_ip(node_id)
available_resources = usage_by_ip.get(ip)
add_node(node_type, available_resources)
| diff --git a/python/ray/tests/test_resource_demand_scheduler.py b/python/ray/tests/test_resource_demand_scheduler.py
--- a/python/ray/tests/test_resource_demand_scheduler.py
+++ b/python/ray/tests/test_resource_demand_scheduler.py
@@ -180,6 +180,27 @@ def test_get_nodes_to_launch_limits():
assert to_launch == []
+def test_calculate_node_resources():
+ provider = MockProvider()
+ scheduler = ResourceDemandScheduler(provider, TYPES_A, 10)
+
+ provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
+
+ nodes = provider.non_terminated_nodes({})
+
+ ips = provider.non_terminated_node_ips({})
+ # 2 free p2.8xls
+ utilizations = {ip: {"GPU": 8} for ip in ips}
+ # 1 more on the way
+ pending_nodes = {"p2.8xlarge": 1}
+ # requires 4 p2.8xls (only 3 are in cluster/pending)
+ demands = [{"GPU": 8}] * (len(utilizations) + 2)
+ to_launch = scheduler.get_nodes_to_launch(nodes, pending_nodes, demands,
+ utilizations)
+
+ assert to_launch == [("p2.8xlarge", 1)]
+
+
class LoadMetricsTest(unittest.TestCase):
def testResourceDemandVector(self):
lm = LoadMetrics()
| [autoscaler] Inconsistent reporting of worker node types
On master, it seems I have a cluster with a cpu_4_ondemand head node and cpu_64_ondemand worker (68 CPUs in total), but somehow the debug string is reporting 2 workers of cpu_64_ondemand (which is wrong, since there is only 1 worker node, and the head node is cpu_4_ondemand).
YAML: https://gist.github.com/ericl/f9c32b8bbbe4e1aed43899832730f70d (switching the Ray version to latest wheel)
```
- ResourceUsage: 0.0/68.0 CPU, 0.0/1.0 Custom1, 0.0 GiB/198.65 GiB memory, 0.0
GiB/59.19 GiB object_store_memory
- TimeSinceLastHeartbeat: Min=0 Mean=0 Max=0
Worker node types:
- cpu_64_ondemand: 2
```
cc @wuisawesome
| this is a bug in the debug string right? or are you noticing the wrong number of instances?
```
2020-09-17 23:33:35,234 INFO resource_demand_scheduler.py:68 -- Cluster resources: [{'node:172.30.2.59': 1.0, 'CPU': 4.0, 'memory': 187.0, 'object_store_memory': 64.0}, {'CPU': 64.0, 'memory': 3598.0, 'node:172.30.2.114': 1.0, 'object_store_memory': 1064.0}]
2020-09-17 23:33:35,234 INFO resource_demand_scheduler.py:69 -- Node counts: defaultdict(<class 'int'>, {'cpu_4_ondemand': 2, 'cpu_64_ondemand': 2})
2020-09-17 23:33:35,235 INFO resource_demand_scheduler.py:72 -- Resource demands: [{'accelerator_type:V100': 0.001, 'CPU': 1.0}]
2020-09-17 23:33:35,235 INFO resource_demand_scheduler.py:73 -- Unfulfilled demands: [{'accelerator_type:V100': 0.001, 'CPU': 1.0}]
2020-09-17 23:33:35,235 INFO resource_demand_scheduler.py:79 -- Node requests: []
```
Here's some more inconsistency, with the node counts dict reporting 4 nodes instead of 2.
I'm not sure if this is impacting autoscaling, but it seems likely if the node counts dict is reused for scheduling.
ok i can reproduce the logging issue, doesn't seem to impact autoscaling behavior
IIUC now the conclusion is it does impact the behavior, right?
Yeah this will impact behavior if we need to instances of a type that is already up | 2020-09-18T00:49:02 |
ray-project/ray | 10,953 | ray-project__ray-10953 | [
"10923"
] | 273015814df65ea1d187abb3d9afc5da46c145eb | diff --git a/python/ray/tune/tune.py b/python/ray/tune/tune.py
--- a/python/ray/tune/tune.py
+++ b/python/ray/tune/tune.py
@@ -230,7 +230,7 @@ def run(
max_failures (int): Try to recover a trial at least this many times.
Ray will recover from the latest checkpoint if present.
Setting to -1 will lead to infinite recovery retries.
- Setting to 0 will disable retries. Defaults to 3.
+ Setting to 0 will disable retries. Defaults to 0.
fail_fast (bool | str): Whether to fail upon the first error.
If fail_fast='raise' provided, Tune will automatically
raise the exception received by the Trainable. fail_fast='raise'
| Issue on page /tune/api_docs/execution.html [tune]
Documentation regarding `max_failures`:
```
max_failures (int): Try to recover a trial at least this many times.
Ray will recover from the latest checkpoint if present.
Setting to -1 will lead to infinite recovery retries.
Setting to 0 will disable retries. Defaults to 3.
```
However, if you look into `tune.run`, the default is `max_failure=0`. I think that the doc is outdated, because the observed behavior is as if `max_failure=0` is true.
| 2020-09-22T14:26:40 |
||
ray-project/ray | 10,979 | ray-project__ray-10979 | [
"10902"
] | a260e660168fe32a2b7e7084666bcc2e760f556b | diff --git a/python/ray/tune/trial_runner.py b/python/ray/tune/trial_runner.py
--- a/python/ray/tune/trial_runner.py
+++ b/python/ray/tune/trial_runner.py
@@ -454,7 +454,10 @@ def _get_next_trial(self):
"""
trials_done = all(trial.is_finished() for trial in self._trials)
wait_for_trial = trials_done and not self._search_alg.is_finished()
- self._update_trial_queue(blocking=wait_for_trial)
+ # Only fetch a new trial if we have no pending trial
+ if not any(trial.status == Trial.PENDING for trial in self._trials) \
+ or wait_for_trial:
+ self._update_trial_queue(blocking=wait_for_trial)
with warn_if_slow("choose_trial_to_run"):
trial = self._scheduler_alg.choose_trial_to_run(self)
logger.debug("Running trial {}".format(trial))
| diff --git a/python/ray/tune/tests/test_trial_runner.py b/python/ray/tune/tests/test_trial_runner.py
--- a/python/ray/tune/tests/test_trial_runner.py
+++ b/python/ray/tune/tests/test_trial_runner.py
@@ -288,6 +288,44 @@ def on_trial_result(self, trial_runner, trial, result):
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(runner.trial_executor._committed_resources.cpu, 2)
+ def testQueueFilling(self):
+ ray.init(num_cpus=4)
+
+ def f1(config):
+ for i in range(10):
+ yield i
+
+ tune.register_trainable("f1", f1)
+
+ search_alg = BasicVariantGenerator()
+ search_alg.add_configurations({
+ "foo": {
+ "run": "f1",
+ "num_samples": 100,
+ "config": {
+ "a": tune.sample_from(lambda spec: 5.0 / 7),
+ "b": tune.sample_from(lambda spec: "long" * 40)
+ },
+ "resources_per_trial": {
+ "cpu": 2
+ }
+ }
+ })
+
+ runner = TrialRunner(search_alg=search_alg)
+
+ runner.step()
+ runner.step()
+ runner.step()
+ self.assertEqual(len(runner._trials), 3)
+
+ runner.step()
+ self.assertEqual(len(runner._trials), 3)
+
+ self.assertEqual(runner._trials[0].status, Trial.RUNNING)
+ self.assertEqual(runner._trials[1].status, Trial.RUNNING)
+ self.assertEqual(runner._trials[2].status, Trial.PENDING)
+
if __name__ == "__main__":
import pytest
| [tune] The search algorithms generate more trials than necessary
### What is the problem?
I was under the impression that #10802 should avoid producing too many trials before resources are ready. However, on master, it seems like many trials are generated (especially when using search algorithms) before resources are ready, and this happens far into the experiment.
cc @krfricke
| 2020-09-23T15:36:06 |
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