text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from contextlib import contextmanager
  import atexit
  import colorama
  import faulthandler
  import hashlib
  import inspect
  import logging
  import numpy as np
  import os
  import redis
  import signal
  from six.moves import queue
  import sys
  import threading
  import time
  import traceback
  
  # Ray modules
  import pyarrow
  import pyarrow.plasma as plasma
  import ray.cloudpickle as pickle
  import ray.experimental.state as state
  import ray.gcs_utils
  import ray.memory_monitor as memory_monitor
  import ray.remote_function
  import ray.serialization as serialization
  import ray.services as services
  import ray.signature
  import ray.tempfile_services as tempfile_services
  import ray.raylet
  import ray.plasma
  import ray.ray_constants as ray_constants
  from ray import import_thread
  from ray import profiling
  from ray.function_manager import (FunctionActorManager, FunctionDescriptor)
  from ray.utils import (
      check_oversized_pickle,
      is_cython,
      random_string,
      thread_safe_client,
  )
  
  SCRIPT_MODE = 0
  WORKER_MODE = 1
  LOCAL_MODE = 2
  PYTHON_MODE = 3
  
  ERROR_KEY_PREFIX = b"Error:"
  
  # This must match the definition of NIL_ACTOR_ID in task.h.
  NIL_ID = ray_constants.ID_SIZE * b"\xff"
  NIL_LOCAL_SCHEDULER_ID = NIL_ID
  NIL_ACTOR_ID = NIL_ID
  NIL_ACTOR_HANDLE_ID = NIL_ID
  NIL_CLIENT_ID = ray_constants.ID_SIZE * b"\xff"
  
  # Default resource requirements for actors when no resource requirements are
  # specified.
  DEFAULT_ACTOR_METHOD_CPUS_SIMPLE_CASE = 1
  DEFAULT_ACTOR_CREATION_CPUS_SIMPLE_CASE = 0
  # Default resource requirements for actors when some resource requirements are
  # specified.
  DEFAULT_ACTOR_METHOD_CPUS_SPECIFIED_CASE = 0
  DEFAULT_ACTOR_CREATION_CPUS_SPECIFIED_CASE = 1
  
  # Logger for this module. It should be configured at the entry point
  # into the program using Ray. Ray configures it by default automatically
  # using logging.basicConfig in its entry/init points.
  logger = logging.getLogger(__name__)
  
  try:
      import setproctitle
  except ImportError:
      setproctitle = None
  
  
  class RayTaskError(Exception):
      """An object used internally to represent a task that threw an exception.
  
      If a task throws an exception during execution, a RayTaskError is stored in
      the object store for each of the task's outputs. When an object is
      retrieved from the object store, the Python method that retrieved it checks
      to see if the object is a RayTaskError and if it is then an exception is
      thrown propagating the error message.
  
      Currently, we either use the exception attribute or the traceback attribute
      but not both.
  
      Attributes:
          function_name (str): The name of the function that failed and produced
              the RayTaskError.
          exception (Exception): The exception object thrown by the failed task.
          traceback_str (str): The traceback from the exception.
      """
  
      def __init__(self, function_name, traceback_str):
          """Initialize a RayTaskError."""
          if setproctitle:
              self.proctitle = setproctitle.getproctitle()
          else:
              self.proctitle = "ray_worker"
          self.pid = os.getpid()
          self.host = os.uname()[1]
          self.function_name = function_name
          self.traceback_str = traceback_str
          assert traceback_str is not None
  
      def __str__(self):
          """Format a RayTaskError as a string."""
          lines = self.traceback_str.split("\n")
          out = []
          in_worker = False
          for line in lines:
              if line.startswith("Traceback "):
                  out.append("{}{}{} (pid={}, host={})".format(
                      colorama.Fore.CYAN, self.proctitle, colorama.Fore.RESET,
                      self.pid, self.host))
              elif in_worker:
                  in_worker = False
              elif "ray/worker.py" in line or "ray/function_manager.py" in line:
                  in_worker = True
              else:
                  out.append(line)
          return "\n".join(out)
  
  
  class Worker(object):
      """A class used to define the control flow of a worker process.
  
      Note:
          The methods in this class are considered unexposed to the user. The
          functions outside of this class are considered exposed.
  
      Attributes:
          connected (bool): True if Ray has been started and False otherwise.
          mode: The mode of the worker. One of SCRIPT_MODE, LOCAL_MODE, and
              WORKER_MODE.
          cached_functions_to_run (List): A list of functions to run on all of
              the workers that should be exported as soon as connect is called.
          profiler: the profiler used to aggregate profiling information.
          state_lock (Lock):
              Used to lock worker's non-thread-safe internal states:
              1) task_index increment: make sure we generate unique task ids;
              2) Object reconstruction: because the node manager will
              recycle/return the worker's resources before/after reconstruction,
              it's unsafe for multiple threads to call object
              reconstruction simultaneously.
      """
  
      def __init__(self):
          """Initialize a Worker object."""
          self.connected = False
          self.mode = None
          self.cached_functions_to_run = []
          self.actor_init_error = None
          self.make_actor = None
          self.actors = {}
          self.actor_task_counter = 0
          # The number of threads Plasma should use when putting an object in the
          # object store.
          self.memcopy_threads = 12
          # When the worker is constructed. Record the original value of the
          # CUDA_VISIBLE_DEVICES environment variable.
          self.original_gpu_ids = ray.utils.get_cuda_visible_devices()
          self.profiler = None
          self.memory_monitor = memory_monitor.MemoryMonitor()
          self.state_lock = threading.Lock()
          # A dictionary that maps from driver id to SerializationContext
          # TODO: clean up the SerializationContext once the job finished.
          self.serialization_context_map = {}
          self.function_actor_manager = FunctionActorManager(self)
          # Reads/writes to the following fields must be protected by
          # self.state_lock.
          # Identity of the driver that this worker is processing.
          self.task_driver_id = ray.ObjectID(NIL_ID)
          self.current_task_id = ray.ObjectID(NIL_ID)
          self.task_index = 0
          self.put_index = 1
  
      def get_current_thread_task_id(self):
          """Get the current thread's task ID.
  
          This returns the assigned task ID if called on the main thread, else a
          random task ID.  This method is not thread-safe and must be called with
          self.state_lock acquired.
          """
          current_task_id = self.current_task_id
          if not ray.utils.is_main_thread():
              # If this is running on a separate thread, then the mapping
              # to the current task ID may not be correct. Generate a
              # random task ID so that the backend can differentiate
              # between different threads.
              current_task_id = ray.ObjectID(random_string())
              if not self.multithreading_warned:
                  logger.warning(
                      "Calling ray.get or ray.wait in a separate thread "
                      "may lead to deadlock if the main thread blocks on this "
                      "thread and there are not enough resources to execute "
                      "more tasks")
                  self.multithreading_warned = True
          assert not current_task_id.is_nil()
          return current_task_id
  
      def mark_actor_init_failed(self, error):
          """Called to mark this actor as failed during initialization."""
  
          self.actor_init_error = error
  
      def reraise_actor_init_error(self):
          """Raises any previous actor initialization error."""
  
          if self.actor_init_error is not None:
              raise self.actor_init_error
  
      def get_serialization_context(self, driver_id):
          """Get the SerializationContext of the driver that this worker is processing.
  
          Args:
              driver_id: The ID of the driver that indicates which driver to get
                  the serialization context for.
  
          Returns:
              The serialization context of the given driver.
          """
          if driver_id not in self.serialization_context_map:
              _initialize_serialization(driver_id)
          return self.serialization_context_map[driver_id]
  
      def check_connected(self):
          """Check if the worker is connected.
  
          Raises:
            Exception: An exception is raised if the worker is not connected.
          """
          if not self.connected:
              raise RayConnectionError("Ray has not been started yet. You can "
                                       "start Ray with 'ray.init()'.")
  
      def set_mode(self, mode):
          """Set the mode of the worker.
  
          The mode SCRIPT_MODE should be used if this Worker is a driver that is
          being run as a Python script or interactively in a shell. It will print
          information about task failures.
  
          The mode WORKER_MODE should be used if this Worker is not a driver. It
          will not print information about tasks.
  
          The mode LOCAL_MODE should be used if this Worker is a driver and if
          you want to run the driver in a manner equivalent to serial Python for
          debugging purposes. It will not send remote function calls to the
          scheduler and will insead execute them in a blocking fashion.
  
          Args:
              mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.
          """
          self.mode = mode
  
      def store_and_register(self, object_id, value, depth=100):
          """Store an object and attempt to register its class if needed.
  
          Args:
              object_id: The ID of the object to store.
              value: The value to put in the object store.
              depth: The maximum number of classes to recursively register.
  
          Raises:
              Exception: An exception is raised if the attempt to store the
                  object fails. This can happen if there is already an object
                  with the same ID in the object store or if the object store is
                  full.
          """
          counter = 0
          while True:
              if counter == depth:
                  raise Exception("Ray exceeded the maximum number of classes "
                                  "that it will recursively serialize when "
                                  "attempting to serialize an object of "
                                  "type {}.".format(type(value)))
              counter += 1
              try:
                  self.plasma_client.put(
                      value,
                      object_id=pyarrow.plasma.ObjectID(object_id.id()),
                      memcopy_threads=self.memcopy_threads,
                      serialization_context=self.get_serialization_context(
                          self.task_driver_id))
                  break
              except pyarrow.SerializationCallbackError as e:
                  try:
                      register_custom_serializer(
                          type(e.example_object), use_dict=True)
                      warning_message = ("WARNING: Serializing objects of type "
                                         "{} by expanding them as dictionaries "
                                         "of their fields. This behavior may "
                                         "be incorrect in some cases.".format(
                                             type(e.example_object)))
                      logger.debug(warning_message)
                  except (serialization.RayNotDictionarySerializable,
                          serialization.CloudPickleError,
                          pickle.pickle.PicklingError, Exception):
                      # We also handle generic exceptions here because
                      # cloudpickle can fail with many different types of errors.
                      try:
                          register_custom_serializer(
                              type(e.example_object), use_pickle=True)
                          warning_message = ("WARNING: Falling back to "
                                             "serializing objects of type {} by "
                                             "using pickle. This may be "
                                             "inefficient.".format(
                                                 type(e.example_object)))
                          logger.warning(warning_message)
                      except serialization.CloudPickleError:
                          register_custom_serializer(
                              type(e.example_object),
                              use_pickle=True,
                              local=True)
                          warning_message = ("WARNING: Pickling the class {} "
                                             "failed, so we are using pickle "
                                             "and only registering the class "
                                             "locally.".format(
                                                 type(e.example_object)))
                          logger.warning(warning_message)
  
      def put_object(self, object_id, value):
          """Put value in the local object store with object id objectid.
  
          This assumes that the value for objectid has not yet been placed in the
          local object store.
  
          Args:
              object_id (object_id.ObjectID): The object ID of the value to be
                  put.
              value: The value to put in the object store.
  
          Raises:
              Exception: An exception is raised if the attempt to store the
                  object fails. This can happen if there is already an object
                  with the same ID in the object store or if the object store is
                  full.
          """
          # Make sure that the value is not an object ID.
          if isinstance(value, ray.ObjectID):
              raise Exception("Calling 'put' on an ObjectID is not allowed "
                              "(similarly, returning an ObjectID from a remote "
                              "function is not allowed). If you really want to "
                              "do this, you can wrap the ObjectID in a list and "
                              "call 'put' on it (or return it).")
  
          # Serialize and put the object in the object store.
          try:
              self.store_and_register(object_id, value)
          except pyarrow.PlasmaObjectExists:
              # The object already exists in the object store, so there is no
              # need to add it again. TODO(rkn): We need to compare the hashes
              # and make sure that the objects are in fact the same. We also
              # should return an error code to the caller instead of printing a
              # message.
              logger.info(
                  "The object with ID {} already exists in the object store."
                  .format(object_id))
          except TypeError:
              # This error can happen because one of the members of the object
              # may not be serializable for cloudpickle. So we need these extra
              # fallbacks here to start from the beginning. Hopefully the object
              # could have a `__reduce__` method.
              register_custom_serializer(type(value), use_pickle=True)
              warning_message = ("WARNING: Serializing the class {} failed, "
                                 "so are are falling back to cloudpickle."
                                 .format(type(value)))
              logger.warning(warning_message)
              self.store_and_register(object_id, value)
  
      def retrieve_and_deserialize(self, object_ids, timeout, error_timeout=10):
          start_time = time.time()
          # Only send the warning once.
          warning_sent = False
          while True:
              try:
                  # We divide very large get requests into smaller get requests
                  # so that a single get request doesn't block the store for a
                  # long time, if the store is blocked, it can block the manager
                  # as well as a consequence.
                  results = []
                  for i in range(0, len(object_ids),
                                 ray._config.worker_get_request_size()):
                      results += self.plasma_client.get(
                          object_ids[i:(
                              i + ray._config.worker_get_request_size())],
                          timeout_ms=timeout,
                          serialization_context=self.get_serialization_context(self.task_driver_id))
                  return results
              except pyarrow.lib.ArrowInvalid:
                  # TODO(ekl): the local scheduler could include relevant
                  # metadata in the task kill case for a better error message
                  invalid_error = RayTaskError(
                      "<unknown>",
                      "Invalid return value: likely worker died or was killed "
                      "while executing the task; check previous logs or dmesg "
                      "for errors.")
                  return [invalid_error] * len(object_ids)
              except pyarrow.DeserializationCallbackError:
                  # Wait a little bit for the import thread to import the class.
                  # If we currently have the worker lock, we need to release it
                  # so that the import thread can acquire it.
                  if self.mode == WORKER_MODE:
                      self.lock.release()
                  time.sleep(0.01)
                  if self.mode == WORKER_MODE:
                      self.lock.acquire()
  
                  if time.time() - start_time > error_timeout:
                      warning_message = ("This worker or driver is waiting to "
                                         "receive a class definition so that it "
                                         "can deserialize an object from the "
                                         "object store. This may be fine, or it "
                                         "may be a bug.")
                      if not warning_sent:
                          ray.utils.push_error_to_driver(
                              self,
                              ray_constants.WAIT_FOR_CLASS_PUSH_ERROR,
                              warning_message,
                              driver_id=self.task_driver_id.id())
                      warning_sent = True
  
      def get_object(self, object_ids):
          """Get the value or values in the object store associated with the IDs.
  
          Return the values from the local object store for object_ids. This will
          block until all the values for object_ids have been written to the
          local object store.
  
          Args:
              object_ids (List[object_id.ObjectID]): A list of the object IDs
                  whose values should be retrieved.
          """
          # Make sure that the values are object IDs.
          for object_id in object_ids:
              if not isinstance(object_id, ray.ObjectID):
                  raise Exception("Attempting to call `get` on the value {}, "
                                  "which is not an ObjectID.".format(object_id))
          # Do an initial fetch for remote objects. We divide the fetch into
          # smaller fetches so as to not block the manager for a prolonged period
          # of time in a single call.
          plain_object_ids = [
              plasma.ObjectID(object_id.id()) for object_id in object_ids
          ]
          for i in range(0, len(object_ids),
                         ray._config.worker_fetch_request_size()):
              self.raylet_client.fetch_or_reconstruct(
                  object_ids[i:(i + ray._config.worker_fetch_request_size())],
                  True)
  
          # Get the objects. We initially try to get the objects immediately.
          final_results = self.retrieve_and_deserialize(plain_object_ids, 0)
          # Construct a dictionary mapping object IDs that we haven't gotten yet
          # to their original index in the object_ids argument.
          unready_ids = {
              plain_object_ids[i].binary(): i
              for (i, val) in enumerate(final_results)
              if val is plasma.ObjectNotAvailable
          }
  
          if len(unready_ids) > 0:
              with self.state_lock:
                  # Get the task ID, to notify the backend which task is blocked.
                  current_task_id = self.get_current_thread_task_id()
  
                  # Try reconstructing any objects we haven't gotten yet. Try to
                  # get them until at least get_timeout_milliseconds
                  # milliseconds passes, then repeat.
                  while len(unready_ids) > 0:
                      object_ids_to_fetch = [
                          plasma.ObjectID(unready_id)
                          for unready_id in unready_ids.keys()
                      ]
                      ray_object_ids_to_fetch = [
                          ray.ObjectID(unready_id)
                          for unready_id in unready_ids.keys()
                      ]
                      fetch_request_size = (
                          ray._config.worker_fetch_request_size())
                      for i in range(0, len(object_ids_to_fetch),
                                     fetch_request_size):
                          self.raylet_client.fetch_or_reconstruct(
                              ray_object_ids_to_fetch[i:(
                                  i + fetch_request_size)], False,
                              current_task_id)
                      results = self.retrieve_and_deserialize(
                          object_ids_to_fetch,
                          max([
                              ray._config.get_timeout_milliseconds(),
                              int(0.01 * len(unready_ids))
                          ]))
                      # Remove any entries for objects we received during this
                      # iteration so we don't retrieve the same object twice.
                      for i, val in enumerate(results):
                          if val is not plasma.ObjectNotAvailable:
                              object_id = object_ids_to_fetch[i].binary()
                              index = unready_ids[object_id]
                              final_results[index] = val
                              unready_ids.pop(object_id)
  
                  # If there were objects that we weren't able to get locally,
                  # let the local scheduler know that we're now unblocked.
                  self.raylet_client.notify_unblocked(current_task_id)
  
          assert len(final_results) == len(object_ids)
          return final_results
  
      def submit_task(self,
                      function_descriptor,
                      args,
                      actor_id=None,
                      actor_handle_id=None,
                      actor_counter=0,
                      is_actor_checkpoint_method=False,
                      actor_creation_id=None,
                      actor_creation_dummy_object_id=None,
                      max_actor_reconstructions=0,
                      execution_dependencies=None,
                      num_return_vals=None,
                      resources=None,
                      placement_resources=None,
                      driver_id=None,
                      language=ray.gcs_utils.Language.PYTHON):
          """Submit a remote task to the scheduler.
  
          Tell the scheduler to schedule the execution of the function with
          function_descriptor with arguments args. Retrieve object IDs for the
          outputs of the function from the scheduler and immediately return them.
  
          Args:
              function_descriptor: The function descriptor to execute.
              args: The arguments to pass into the function. Arguments can be
                  object IDs or they can be values. If they are values, they must
                  be serializable objects.
              actor_id: The ID of the actor that this task is for.
              actor_counter: The counter of the actor task.
              is_actor_checkpoint_method: True if this is an actor checkpoint
                  task and false otherwise.
              actor_creation_id: The ID of the actor to create, if this is an
                  actor creation task.
              actor_creation_dummy_object_id: If this task is an actor method,
                  then this argument is the dummy object ID associated with the
                  actor creation task for the corresponding actor.
              execution_dependencies: The execution dependencies for this task.
              num_return_vals: The number of return values this function should
                  have.
              resources: The resource requirements for this task.
              placement_resources: The resources required for placing the task.
                  If this is not provided or if it is an empty dictionary, then
                  the placement resources will be equal to resources.
              driver_id: The ID of the relevant driver. This is almost always the
                  driver ID of the driver that is currently running. However, in
                  the exceptional case that an actor task is being dispatched to
                  an actor created by a different driver, this should be the
                  driver ID of the driver that created the actor.
  
          Returns:
              The return object IDs for this task.
          """
          with profiling.profile("submit_task", worker=self):
              if actor_id is None:
                  assert actor_handle_id is None
                  actor_id = ray.ObjectID(NIL_ACTOR_ID)
                  actor_handle_id = ray.ObjectID(NIL_ACTOR_HANDLE_ID)
              else:
                  assert actor_handle_id is not None
  
              if actor_creation_id is None:
                  actor_creation_id = ray.ObjectID(NIL_ACTOR_ID)
  
              if actor_creation_dummy_object_id is None:
                  actor_creation_dummy_object_id = (ray.ObjectID(NIL_ID))
  
              # Put large or complex arguments that are passed by value in the
              # object store first.
              args_for_local_scheduler = []
              for arg in args:
                  if isinstance(arg, ray.ObjectID):
                      args_for_local_scheduler.append(arg)
                  elif ray.raylet.check_simple_value(arg):
                      args_for_local_scheduler.append(arg)
                  else:
                      args_for_local_scheduler.append(put(arg))
  
              # By default, there are no execution dependencies.
              if execution_dependencies is None:
                  execution_dependencies = []
  
              if driver_id is None:
                  driver_id = self.task_driver_id
  
              if resources is None:
                  raise ValueError("The resources dictionary is required.")
              for value in resources.values():
                  assert (isinstance(value, int) or isinstance(value, float))
                  if value < 0:
                      raise ValueError(
                          "Resource quantities must be nonnegative.")
                  if (value >= 1 and isinstance(value, float)
                          and not value.is_integer()):
                      raise ValueError(
                          "Resource quantities must all be whole numbers.")
  
              if placement_resources is None:
                  placement_resources = {}
  
              with self.state_lock:
                  # Increment the worker's task index to track how many tasks
                  # have been submitted by the current task so far.
                  task_index = self.task_index
                  self.task_index += 1
                  # The parent task must be set for the submitted task.
                  assert not self.current_task_id.is_nil()
              # Submit the task to local scheduler.
              function_descriptor_list = (
                  function_descriptor.get_function_descriptor_list())
              task = ray.raylet.Task(
                  driver_id, function_descriptor_list, args_for_local_scheduler,
                  num_return_vals, self.current_task_id, task_index,
                  actor_creation_id, actor_creation_dummy_object_id,
                  max_actor_reconstructions, actor_id, actor_handle_id,
                  actor_counter, execution_dependencies, resources,
                  placement_resources)
              self.raylet_client.submit_task(task)
  
              return task.returns()
  
      def run_function_on_all_workers(self, function,
                                      run_on_other_drivers=False):
          """Run arbitrary code on all of the workers.
  
          This function will first be run on the driver, and then it will be
          exported to all of the workers to be run. It will also be run on any
          new workers that register later. If ray.init has not been called yet,
          then cache the function and export it later.
  
          Args:
              function (Callable): The function to run on all of the workers. It
                  takes only one argument, a worker info dict. If it returns
                  anything, its return values will not be used.
              run_on_other_drivers: The boolean that indicates whether we want to
                  run this funtion on other drivers. One case is we may need to
                  share objects across drivers.
          """
          # If ray.init has not been called yet, then cache the function and
          # export it when connect is called. Otherwise, run the function on all
          # workers.
          if self.mode is None:
              self.cached_functions_to_run.append(function)
          else:
              # Attempt to pickle the function before we need it. This could
              # fail, and it is more convenient if the failure happens before we
              # actually run the function locally.
              pickled_function = pickle.dumps(function)
  
              function_to_run_id = hashlib.sha1(pickled_function).digest()
              key = b"FunctionsToRun:" + function_to_run_id
              # First run the function on the driver.
              # We always run the task locally.
              function({"worker": self})
              # Check if the function has already been put into redis.
              function_exported = self.redis_client.setnx(b"Lock:" + key, 1)
              if not function_exported:
                  # In this case, the function has already been exported, so
                  # we don't need to export it again.
                  return
  
              check_oversized_pickle(pickled_function, function.__name__,
                                     "function", self)
  
              # Run the function on all workers.
              self.redis_client.hmset(
                  key, {
                      "driver_id": self.task_driver_id.id(),
                      "function_id": function_to_run_id,
                      "function": pickled_function,
                      "run_on_other_drivers": str(run_on_other_drivers)
                  })
              self.redis_client.rpush("Exports", key)
              # TODO(rkn): If the worker fails after it calls setnx and before it
              # successfully completes the hmset and rpush, then the program will
              # most likely hang. This could be fixed by making these three
              # operations into a transaction (or by implementing a custom
              # command that does all three things).
  
      def _get_arguments_for_execution(self, function_name, serialized_args):
          """Retrieve the arguments for the remote function.
  
          This retrieves the values for the arguments to the remote function that
          were passed in as object IDs. Arguments that were passed by value are
          not changed. This is called by the worker that is executing the remote
          function.
  
          Args:
              function_name (str): The name of the remote function whose
                  arguments are being retrieved.
              serialized_args (List): The arguments to the function. These are
                  either strings representing serialized objects passed by value
                  or they are ObjectIDs.
  
          Returns:
              The retrieved arguments in addition to the arguments that were
                  passed by value.
  
          Raises:
              RayTaskError: This exception is raised if a task that
                  created one of the arguments failed.
          """
          arguments = []
          for (i, arg) in enumerate(serialized_args):
              if isinstance(arg, ray.ObjectID):
                  # get the object from the local object store
                  argument = self.get_object([arg])[0]
                  if isinstance(argument, RayTaskError):
                      raise argument
              else:
                  # pass the argument by value
                  argument = arg
  
              arguments.append(argument)
          return arguments
  
      def _store_outputs_in_object_store(self, object_ids, outputs):
          """Store the outputs of a remote function in the local object store.
  
          This stores the values that were returned by a remote function in the
          local object store. If any of the return values are object IDs, then
          these object IDs are aliased with the object IDs that the scheduler
          assigned for the return values. This is called by the worker that
          executes the remote function.
  
          Note:
              The arguments object_ids and outputs should have the same length.
  
          Args:
              object_ids (List[ObjectID]): The object IDs that were assigned to
                  the outputs of the remote function call.
              outputs (Tuple): The value returned by the remote function. If the
                  remote function was supposed to only return one value, then its
                  output was wrapped in a tuple with one element prior to being
                  passed into this function.
          """
          for i in range(len(object_ids)):
              if isinstance(outputs[i], ray.actor.ActorHandle):
                  raise Exception("Returning an actor handle from a remote "
                                  "function is not allowed).")
  
              self.put_object(object_ids[i], outputs[i])
  
      def _process_task(self, task, function_execution_info):
          """Execute a task assigned to this worker.
  
          This method deserializes a task from the scheduler, and attempts to
          execute the task. If the task succeeds, the outputs are stored in the
          local object store. If the task throws an exception, RayTaskError
          objects are stored in the object store to represent the failed task
          (these will be retrieved by calls to get or by subsequent tasks that
          use the outputs of this task).
          """
          with self.state_lock:
              assert self.task_driver_id.is_nil()
              assert self.current_task_id.is_nil()
              assert self.task_index == 0
              assert self.put_index == 1
  
              # The ID of the driver that this task belongs to. This is needed so
              # that if the task throws an exception, we propagate the error
              # message to the correct driver.
              self.task_driver_id = task.driver_id()
              self.current_task_id = task.task_id()
  
          function_descriptor = FunctionDescriptor.from_bytes_list(
              task.function_descriptor_list())
          args = task.arguments()
          return_object_ids = task.returns()
          if (task.actor_id().id() != NIL_ACTOR_ID
                  or task.actor_creation_id().id() != NIL_ACTOR_ID):
              dummy_return_id = return_object_ids.pop()
          function_executor = function_execution_info.function
          function_name = function_execution_info.function_name
  
          # Get task arguments from the object store.
          try:
              if function_name != "__ray_terminate__":
                  self.reraise_actor_init_error()
              self.memory_monitor.raise_if_low_memory()
              with profiling.profile("task:deserialize_arguments", worker=self):
                  arguments = self._get_arguments_for_execution(
                      function_name, args)
          except RayTaskError as e:
              self._handle_process_task_failure(
                  function_descriptor, return_object_ids, e,
                  ray.utils.format_error_message(traceback.format_exc()))
              return
          except Exception as e:
              self._handle_process_task_failure(
                  function_descriptor, return_object_ids, e,
                  ray.utils.format_error_message(traceback.format_exc()))
              return
  
          # Execute the task.
          try:
              with profiling.profile("task:execute", worker=self):
                  if (task.actor_id().id() == NIL_ACTOR_ID
                          and task.actor_creation_id().id() == NIL_ACTOR_ID):
                      outputs = function_executor(*arguments)
                  else:
                      if task.actor_id().id() != NIL_ACTOR_ID:
                          key = task.actor_id().id()
                      else:
                          key = task.actor_creation_id().id()
                      outputs = function_executor(dummy_return_id,
                                                  self.actors[key], *arguments)
          except Exception as e:
              # Determine whether the exception occured during a task, not an
              # actor method.
              task_exception = task.actor_id().id() == NIL_ACTOR_ID
              traceback_str = ray.utils.format_error_message(
                  traceback.format_exc(), task_exception=task_exception)
              self._handle_process_task_failure(
                  function_descriptor, return_object_ids, e, traceback_str)
              return
  
          # Store the outputs in the local object store.
          try:
              with profiling.profile("task:store_outputs", worker=self):
                  # If this is an actor task, then the last object ID returned by
                  # the task is a dummy output, not returned by the function
                  # itself. Decrement to get the correct number of return values.
                  num_returns = len(return_object_ids)
                  if num_returns == 1:
                      outputs = (outputs, )
                  self._store_outputs_in_object_store(return_object_ids, outputs)
          except Exception as e:
              self._handle_process_task_failure(
                  function_descriptor, return_object_ids, e,
                  ray.utils.format_error_message(traceback.format_exc()))
  
      def _handle_process_task_failure(self, function_descriptor,
                                       return_object_ids, error, backtrace):
          function_name = function_descriptor.function_name
          function_id = function_descriptor.function_id
          failure_object = RayTaskError(function_name, backtrace)
          failure_objects = [
              failure_object for _ in range(len(return_object_ids))
          ]
          self._store_outputs_in_object_store(return_object_ids, failure_objects)
          # Log the error message.
          ray.utils.push_error_to_driver(
              self,
              ray_constants.TASK_PUSH_ERROR,
              str(failure_object),
              driver_id=self.task_driver_id.id(),
              data={
                  "function_id": function_id.id(),
                  "function_name": function_name,
                  "module_name": function_descriptor.module_name,
                  "class_name": function_descriptor.class_name
              })
          # Mark the actor init as failed
          if self.actor_id != NIL_ACTOR_ID and function_name == "__init__":
              self.mark_actor_init_failed(error)
  
      def _wait_for_and_process_task(self, task):
          """Wait for a task to be ready and process the task.
  
          Args:
              task: The task to execute.
          """
          function_descriptor = FunctionDescriptor.from_bytes_list(
              task.function_descriptor_list())
          driver_id = task.driver_id().id()
  
          # TODO(rkn): It would be preferable for actor creation tasks to share
          # more of the code path with regular task execution.
          if (task.actor_creation_id() != ray.ObjectID(NIL_ACTOR_ID)):
              assert self.actor_id == NIL_ACTOR_ID
              self.actor_id = task.actor_creation_id().id()
              self.function_actor_manager.load_actor(driver_id,
                                                     function_descriptor)
  
          execution_info = self.function_actor_manager.get_execution_info(
              driver_id, function_descriptor)
  
          # Execute the task.
          # TODO(rkn): Consider acquiring this lock with a timeout and pushing a
          # warning to the user if we are waiting too long to acquire the lock
          # because that may indicate that the system is hanging, and it'd be
          # good to know where the system is hanging.
          with self.lock:
              function_name = execution_info.function_name
              extra_data = {
                  "name": function_name,
                  "task_id": task.task_id().hex()
              }
              if task.actor_id().id() == NIL_ACTOR_ID:
                  if (task.actor_creation_id() == ray.ObjectID(NIL_ACTOR_ID)):
                      title = "ray_worker:{}()".format(function_name)
                      next_title = "ray_worker"
                  else:
                      actor = self.actors[task.actor_creation_id().id()]
                      title = "ray_{}:{}()".format(actor.__class__.__name__,
                                                   function_name)
                      next_title = "ray_{}".format(actor.__class__.__name__)
              else:
                  actor = self.actors[task.actor_id().id()]
                  title = "ray_{}:{}()".format(actor.__class__.__name__,
                                               function_name)
                  next_title = "ray_{}".format(actor.__class__.__name__)
              with profiling.profile("task", extra_data=extra_data, worker=self):
                  with _changeproctitle(title, next_title):
                      self._process_task(task, execution_info)
                  # Reset the state fields so the next task can run.
                  with self.state_lock:
                      self.task_driver_id = ray.ObjectID(NIL_ID)
                      self.current_task_id = ray.ObjectID(NIL_ID)
                      self.task_index = 0
                      self.put_index = 1
  
          # Increase the task execution counter.
          self.function_actor_manager.increase_task_counter(
              driver_id, function_descriptor)
  
          reached_max_executions = (self.function_actor_manager.get_task_counter(
              driver_id, function_descriptor) == execution_info.max_calls)
          if reached_max_executions:
              self.raylet_client.disconnect()
              sys.exit(0)
  
      def _get_next_task_from_local_scheduler(self):
          """Get the next task from the local scheduler.
  
          Returns:
              A task from the local scheduler.
          """
          with profiling.profile("worker_idle", worker=self):
              task = self.raylet_client.get_task()
  
          # Automatically restrict the GPUs available to this task.
          ray.utils.set_cuda_visible_devices(ray.get_gpu_ids())
  
          return task
  
      def main_loop(self):
          """The main loop a worker runs to receive and execute tasks."""
  
          def exit(signum, frame):
              shutdown(worker=self)
              sys.exit(0)
  
          signal.signal(signal.SIGTERM, exit)
  
          while True:
              task = self._get_next_task_from_local_scheduler()
              self._wait_for_and_process_task(task)
  
  
  def get_gpu_ids():
      """Get the IDs of the GPUs that are available to the worker.
  
      If the CUDA_VISIBLE_DEVICES environment variable was set when the worker
      started up, then the IDs returned by this method will be a subset of the
      IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range
      [0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.
  
      Returns:
          A list of GPU IDs.
      """
      if _mode() == LOCAL_MODE:
          raise Exception("ray.get_gpu_ids() currently does not work in PYTHON "
                          "MODE.")
  
      all_resource_ids = global_worker.raylet_client.resource_ids()
      assigned_ids = [
          resource_id for resource_id, _ in all_resource_ids.get("GPU", [])
      ]
      # If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in
      # the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be
      # returned).
      if global_worker.original_gpu_ids is not None:
          assigned_ids = [
              global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids
          ]
  
      return assigned_ids
  
  
  def get_resource_ids():
      """Get the IDs of the resources that are available to the worker.
  
      Returns:
          A dictionary mapping the name of a resource to a list of pairs, where
          each pair consists of the ID of a resource and the fraction of that
          resource reserved for this worker.
      """
      if _mode() == LOCAL_MODE:
          raise Exception(
              "ray.get_resource_ids() currently does not work in PYTHON "
              "MODE.")
  
      return global_worker.raylet_client.resource_ids()
  
  
  def _webui_url_helper(client):
      """Parsing for getting the url of the web UI.
  
      Args:
          client: A redis client to use to query the primary Redis shard.
  
      Returns:
          The URL of the web UI as a string.
      """
      result = client.hmget("webui", "url")[0]
      return ray.utils.decode(result) if result is not None else result
  
  
  def get_webui_url():
      """Get the URL to access the web UI.
  
      Note that the URL does not specify which node the web UI is on.
  
      Returns:
          The URL of the web UI as a string.
      """
      if _mode() == LOCAL_MODE:
          raise Exception("ray.get_webui_url() currently does not work in "
                          "PYTHON MODE.")
      return _webui_url_helper(global_worker.redis_client)
  
  
  global_worker = Worker()
  """Worker: The global Worker object for this worker process.
  
  We use a global Worker object to ensure that there is a single worker object
  per worker process.
  """
  
  global_state = state.GlobalState()
  
  
  class RayConnectionError(Exception):
      pass
  
  
  def print_failed_task(task_status):
      """Print information about failed tasks.
  
      Args:
          task_status (Dict): A dictionary containing the name, operationid, and
              error message for a failed task.
      """
      logger.error("""
        Error: Task failed
          Function Name: {}
          Task ID: {}
          Error Message: \n{}
      """.format(task_status["function_name"], task_status["operationid"],
                 task_status["error_message"]))
  
  
  def error_applies_to_driver(error_key, worker=global_worker):
      """Return True if the error is for this driver and false otherwise."""
      # TODO(rkn): Should probably check that this is only called on a driver.
      # Check that the error key is formatted as in push_error_to_driver.
      assert len(error_key) == (len(ERROR_KEY_PREFIX) + ray_constants.ID_SIZE + 1
                                + ray_constants.ID_SIZE), error_key
      # If the driver ID in the error message is a sequence of all zeros, then
      # the message is intended for all drivers.
      driver_id = error_key[len(ERROR_KEY_PREFIX):(
          len(ERROR_KEY_PREFIX) + ray_constants.ID_SIZE)]
      return (driver_id == worker.task_driver_id.id()
              or driver_id == ray.ray_constants.NIL_JOB_ID.id())
  
  
  def error_info(worker=global_worker):
      """Return information about failed tasks."""
      worker.check_connected()
      return (global_state.error_messages(job_id=worker.task_driver_id) +
              global_state.error_messages(job_id=ray_constants.NIL_JOB_ID))
  
  
  def _initialize_serialization(driver_id, worker=global_worker):
      """Initialize the serialization library.
  
      This defines a custom serializer for object IDs and also tells ray to
      serialize several exception classes that we define for error handling.
      """
      serialization_context = pyarrow.default_serialization_context()
      # Tell the serialization context to use the cloudpickle version that we
      # ship with Ray.
      serialization_context.set_pickle(pickle.dumps, pickle.loads)
      pyarrow.register_torch_serialization_handlers(serialization_context)
  
      # Define a custom serializer and deserializer for handling Object IDs.
      def object_id_custom_serializer(obj):
          return obj.id()
  
      def object_id_custom_deserializer(serialized_obj):
          return ray.ObjectID(serialized_obj)
  
      # We register this serializer on each worker instead of calling
      # register_custom_serializer from the driver so that isinstance still
      # works.
      serialization_context.register_type(
          ray.ObjectID,
          "ray.ObjectID",
          pickle=False,
          custom_serializer=object_id_custom_serializer,
          custom_deserializer=object_id_custom_deserializer)
  
      def actor_handle_serializer(obj):
          return obj._serialization_helper(True)
  
      def actor_handle_deserializer(serialized_obj):
          new_handle = ray.actor.ActorHandle.__new__(ray.actor.ActorHandle)
          new_handle._deserialization_helper(serialized_obj, True)
          return new_handle
  
      # We register this serializer on each worker instead of calling
      # register_custom_serializer from the driver so that isinstance still
      # works.
      serialization_context.register_type(
          ray.actor.ActorHandle,
          "ray.ActorHandle",
          pickle=False,
          custom_serializer=actor_handle_serializer,
          custom_deserializer=actor_handle_deserializer)
  
      worker.serialization_context_map[driver_id] = serialization_context
  
      register_custom_serializer(
          RayTaskError,
          use_dict=True,
          local=True,
          driver_id=driver_id,
          class_id="ray.RayTaskError")
      # Tell Ray to serialize lambdas with pickle.
      register_custom_serializer(
          type(lambda: 0),
          use_pickle=True,
          local=True,
          driver_id=driver_id,
          class_id="lambda")
      # Tell Ray to serialize types with pickle.
      register_custom_serializer(
          type(int),
          use_pickle=True,
          local=True,
          driver_id=driver_id,
          class_id="type")
      # Tell Ray to serialize FunctionSignatures as dictionaries. This is
      # used when passing around actor handles.
      register_custom_serializer(
          ray.signature.FunctionSignature,
          use_dict=True,
          local=True,
          driver_id=driver_id,
          class_id="ray.signature.FunctionSignature")
  
  
  def get_address_info_from_redis_helper(redis_address,
                                         node_ip_address,
                                         redis_password=None):
      redis_ip_address, redis_port = redis_address.split(":")
      # For this command to work, some other client (on the same machine as
      # Redis) must have run "CONFIG SET protected-mode no".
      redis_client = redis.StrictRedis(
          host=redis_ip_address, port=int(redis_port), password=redis_password)
  
      # In the raylet code path, all client data is stored in a zset at the
      # key for the nil client.
      client_key = b"CLIENT" + NIL_CLIENT_ID
      clients = redis_client.zrange(client_key, 0, -1)
      raylets = []
      for client_message in clients:
          client = ray.gcs_utils.ClientTableData.GetRootAsClientTableData(
              client_message, 0)
          client_node_ip_address = ray.utils.decode(client.NodeManagerAddress())
          if (client_node_ip_address == node_ip_address or
              (client_node_ip_address == "127.0.0.1"
               and redis_ip_address == ray.services.get_node_ip_address())):
              raylets.append(client)
      # Make sure that at least one raylet has started locally.
      # This handles a race condition where Redis has started but
      # the raylet has not connected.
      if len(raylets) == 0:
          raise Exception(
              "Redis has started but no raylets have registered yet.")
      object_store_addresses = [
          ray.utils.decode(raylet.ObjectStoreSocketName()) for raylet in raylets
      ]
      raylet_socket_names = [
          ray.utils.decode(raylet.RayletSocketName()) for raylet in raylets
      ]
      return {
          "node_ip_address": node_ip_address,
          "redis_address": redis_address,
          "object_store_addresses": object_store_addresses,
          "raylet_socket_names": raylet_socket_names,
          # Web UI should be running.
          "webui_url": _webui_url_helper(redis_client)
      }
  
  
  def get_address_info_from_redis(redis_address,
                                  node_ip_address,
                                  num_retries=5,
                                  redis_password=None):
      counter = 0
      while True:
          try:
              return get_address_info_from_redis_helper(
                  redis_address, node_ip_address, redis_password=redis_password)
          except Exception:
              if counter == num_retries:
                  raise
              # Some of the information may not be in Redis yet, so wait a little
              # bit.
              logger.warning(
                  "Some processes that the driver needs to connect to have "
                  "not registered with Redis, so retrying. Have you run "
                  "'ray start' on this node?")
              time.sleep(1)
          counter += 1
  
  
  def _normalize_resource_arguments(num_cpus, num_gpus, resources,
                                    num_local_schedulers):
      """Stick the CPU and GPU arguments into the resources dictionary.
  
      This also checks that the arguments are well-formed.
  
      Args:
          num_cpus: Either a number of CPUs or a list of numbers of CPUs.
          num_gpus: Either a number of CPUs or a list of numbers of CPUs.
          resources: Either a dictionary of resource mappings or a list of
              dictionaries of resource mappings.
          num_local_schedulers: The number of local schedulers.
  
      Returns:
          A list of dictionaries of resources of length num_local_schedulers.
      """
      if resources is None:
          resources = {}
      if not isinstance(num_cpus, list):
          num_cpus = num_local_schedulers * [num_cpus]
      if not isinstance(num_gpus, list):
          num_gpus = num_local_schedulers * [num_gpus]
      if not isinstance(resources, list):
          resources = num_local_schedulers * [resources]
  
      new_resources = [r.copy() for r in resources]
  
      for i in range(num_local_schedulers):
          assert "CPU" not in new_resources[i], "Use the 'num_cpus' argument."
          assert "GPU" not in new_resources[i], "Use the 'num_gpus' argument."
          if num_cpus[i] is not None:
              new_resources[i]["CPU"] = num_cpus[i]
          if num_gpus[i] is not None:
              new_resources[i]["GPU"] = num_gpus[i]
  
      return new_resources
  
  
  def _init(address_info=None,
            start_ray_local=False,
            object_id_seed=None,
            num_workers=None,
            num_local_schedulers=None,
            object_store_memory=None,
            redis_max_memory=None,
            collect_profiling_data=True,
            local_mode=False,
            driver_mode=None,
            redirect_worker_output=False,
            redirect_output=True,
            start_workers_from_local_scheduler=True,
            num_cpus=None,
            num_gpus=None,
            resources=None,
            num_redis_shards=None,
            redis_max_clients=None,
            redis_password=None,
            plasma_directory=None,
            huge_pages=False,
            include_webui=True,
            driver_id=None,
            plasma_store_socket_name=None,
            plasma_use_one_memory_mapped_file=True,
            plasma_external_store=None,
            raylet_socket_name=None,
            temp_dir=None,
            _internal_config=None):
      """Helper method to connect to an existing Ray cluster or start a new one.
  
      This method handles two cases. Either a Ray cluster already exists and we
      just attach this driver to it, or we start all of the processes associated
      with a Ray cluster and attach to the newly started cluster.
  
      Args:
          address_info (dict): A dictionary with address information for
              processes in a partially-started Ray cluster. If
              start_ray_local=True, any processes not in this dictionary will be
              started. If provided, an updated address_info dictionary will be
              returned to include processes that are newly started.
          start_ray_local (bool): If True then this will start any processes not
              already in address_info, including Redis, a global scheduler, local
              scheduler(s), object store(s), and worker(s). It will also kill
              these processes when Python exits. If False, this will attach to an
              existing Ray cluster.
          object_id_seed (int): Used to seed the deterministic generation of
              object IDs. The same value can be used across multiple runs of the
              same job in order to generate the object IDs in a consistent
              manner. However, the same ID should not be used for different jobs.
          num_local_schedulers (int): The number of local schedulers to start.
              This is only provided if start_ray_local is True.
          object_store_memory: The maximum amount of memory (in bytes) to
              allow the object store to use.
          redis_max_memory: The max amount of memory (in bytes) to allow redis
              to use, or None for no limit. Once the limit is exceeded, redis
              will start LRU eviction of entries. This only applies to the
              sharded redis tables (task and object tables).
          collect_profiling_data: Whether to collect profiling data from workers.
          local_mode (bool): True if the code should be executed serially
              without Ray. This is useful for debugging.
          redirect_worker_output: True if the stdout and stderr of worker
              processes should be redirected to files.
          redirect_output (bool): True if stdout and stderr for non-worker
              processes should be redirected to files and false otherwise.
          start_workers_from_local_scheduler (bool): If this flag is True, then
              start the initial workers from the local scheduler. Else, start
              them from Python. The latter case is for debugging purposes only.
          num_cpus (int): Number of cpus the user wishes all local schedulers to
              be configured with.
          num_gpus (int): Number of gpus the user wishes all local schedulers to
              be configured with. If unspecified, Ray will attempt to autodetect
              the number of GPUs available on the node (note that autodetection
              currently only works for Nvidia GPUs).
          resources: A dictionary mapping resource names to the quantity of that
              resource available.
          num_redis_shards: The number of Redis shards to start in addition to
              the primary Redis shard.
          redis_max_clients: If provided, attempt to configure Redis with this
              maxclients number.
          redis_password (str): Prevents external clients without the password
              from connecting to Redis if provided.
          plasma_directory: A directory where the Plasma memory mapped files will
              be created.
          huge_pages: Boolean flag indicating whether to start the Object
              Store with hugetlbfs support. Requires plasma_directory.
          include_webui: Boolean flag indicating whether to start the web
              UI, which is a Jupyter notebook.
          driver_id: The ID of driver.
          plasma_store_socket_name (str): If provided, it will specify the socket
              name used by the plasma store.
          plasma_use_one_memory_mapped_file: True if plasma store should use one
              memory mapped file
          plasma_external_store (str) : If provided, it will specify the external
              store to use for evicted objects.
          raylet_socket_name (str): If provided, it will specify the socket path
              used by the raylet process.
          temp_dir (str): If provided, it will specify the root temporary
              directory for the Ray process.
          _internal_config (str): JSON configuration for overriding
              RayConfig defaults. For testing purposes ONLY.
  
      Returns:
          Address information about the started processes.
  
      Raises:
          Exception: An exception is raised if an inappropriate combination of
              arguments is passed in.
      """
      if driver_mode is not None:
          raise Exception("The 'driver_mode' argument has been deprecated. "
                          "To run Ray in local mode, pass in local_mode=True.")
      if local_mode:
          driver_mode = LOCAL_MODE
      else:
          driver_mode = SCRIPT_MODE
  
      if redis_max_memory and collect_profiling_data:
          logger.warning(
              "Profiling data cannot be LRU evicted, so it is disabled "
              "when redis_max_memory is set.")
          collect_profiling_data = False
  
      # Get addresses of existing services.
      if address_info is None:
          address_info = {}
      else:
          assert isinstance(address_info, dict)
      node_ip_address = address_info.get("node_ip_address")
      redis_address = address_info.get("redis_address")
  
      # Start any services that do not yet exist.
      if driver_mode == LOCAL_MODE:
          # If starting Ray in LOCAL_MODE, don't start any other processes.
          pass
      elif start_ray_local:
          # In this case, we launch a scheduler, a new object store, and some
          # workers, and we connect to them. We do not launch any processes that
          # are already registered in address_info.
          if node_ip_address is None:
              node_ip_address = ray.services.get_node_ip_address()
          # Use 1 local scheduler if num_local_schedulers is not provided. If
          # existing local schedulers are provided, use that count as
          # num_local_schedulers.
          if num_local_schedulers is None:
              num_local_schedulers = 1
          # Use 1 additional redis shard if num_redis_shards is not provided.
          num_redis_shards = 1 if num_redis_shards is None else num_redis_shards
  
          # Stick the CPU and GPU resources into the resource dictionary.
          resources = _normalize_resource_arguments(
              num_cpus, num_gpus, resources, num_local_schedulers)
  
          # Start the scheduler, object store, and some workers. These will be
          # killed by the call to shutdown(), which happens when the Python
          # script exits.
          address_info = services.start_ray_head(
              address_info=address_info,
              node_ip_address=node_ip_address,
              num_workers=num_workers,
              num_local_schedulers=num_local_schedulers,
              object_store_memory=object_store_memory,
              redis_max_memory=redis_max_memory,
              collect_profiling_data=collect_profiling_data,
              redirect_worker_output=redirect_worker_output,
              redirect_output=redirect_output,
              start_workers_from_local_scheduler=(
                  start_workers_from_local_scheduler),
              resources=resources,
              num_redis_shards=num_redis_shards,
              redis_max_clients=redis_max_clients,
              redis_password=redis_password,
              plasma_directory=plasma_directory,
              huge_pages=huge_pages,
              include_webui=include_webui,
              plasma_store_socket_name=plasma_store_socket_name,
              plasma_use_one_memory_mapped_file=plasma_use_one_memory_mapped_file,
              plasma_external_store=plasma_external_store,
              raylet_socket_name=raylet_socket_name,
              temp_dir=temp_dir,
              _internal_config=_internal_config)
      else:
          if redis_address is None:
              raise Exception("When connecting to an existing cluster, "
                              "redis_address must be provided.")
          if num_workers is not None:
              raise Exception("When connecting to an existing cluster, "
                              "num_workers must not be provided.")
          if num_local_schedulers is not None:
              raise Exception("When connecting to an existing cluster, "
                              "num_local_schedulers must not be provided.")
          if num_cpus is not None or num_gpus is not None:
              raise Exception("When connecting to an existing cluster, num_cpus "
                              "and num_gpus must not be provided.")
          if resources is not None:
              raise Exception("When connecting to an existing cluster, "
                              "resources must not be provided.")
          if num_redis_shards is not None:
              raise Exception("When connecting to an existing cluster, "
                              "num_redis_shards must not be provided.")
          if redis_max_clients is not None:
              raise Exception("When connecting to an existing cluster, "
                              "redis_max_clients must not be provided.")
          if object_store_memory is not None:
              raise Exception("When connecting to an existing cluster, "
                              "object_store_memory must not be provided.")
          if redis_max_memory is not None:
              raise Exception("When connecting to an existing cluster, "
                              "redis_max_memory must not be provided.")
          if plasma_directory is not None:
              raise Exception("When connecting to an existing cluster, "
                              "plasma_directory must not be provided.")
          if huge_pages:
              raise Exception("When connecting to an existing cluster, "
                              "huge_pages must not be provided.")
          if temp_dir is not None:
              raise Exception("When connecting to an existing cluster, "
                              "temp_dir must not be provided.")
          if plasma_store_socket_name is not None:
              raise Exception("When connecting to an existing cluster, "
                              "plasma_store_socket_name must not be provided.")
          if raylet_socket_name is not None:
              raise Exception("When connecting to an existing cluster, "
                              "raylet_socket_name must not be provided.")
          if _internal_config is not None:
              raise Exception("When connecting to an existing cluster, "
                              "_internal_config must not be provided.")
  
          # Get the node IP address if one is not provided.
          if node_ip_address is None:
              node_ip_address = services.get_node_ip_address(redis_address)
          # Get the address info of the processes to connect to from Redis.
          address_info = get_address_info_from_redis(
              redis_address, node_ip_address, redis_password=redis_password)
  
      # Connect this driver to Redis, the object store, and the local scheduler.
      # Choose the first object store and local scheduler if there are multiple.
      # The corresponding call to disconnect will happen in the call to
      # shutdown() when the Python script exits.
      if driver_mode == LOCAL_MODE:
          driver_address_info = {}
      else:
          driver_address_info = {
              "node_ip_address": node_ip_address,
              "redis_address": address_info["redis_address"],
              "store_socket_name": address_info["object_store_addresses"][0],
              "webui_url": address_info["webui_url"],
          }
          driver_address_info["raylet_socket_name"] = (
              address_info["raylet_socket_names"][0])
  
      # We only pass `temp_dir` to a worker (WORKER_MODE).
      # It can't be a worker here.
      connect(
          driver_address_info,
          object_id_seed=object_id_seed,
          mode=driver_mode,
          worker=global_worker,
          driver_id=driver_id,
          redis_password=redis_password,
          collect_profiling_data=collect_profiling_data)
      return address_info
  
  
  def init(redis_address=None,
           num_cpus=None,
           num_gpus=None,
           resources=None,
           object_store_memory=None,
           redis_max_memory=None,
           collect_profiling_data=True,
           node_ip_address=None,
           object_id_seed=None,
           num_workers=None,
           local_mode=False,
           driver_mode=None,
           redirect_worker_output=False,
           redirect_output=True,
           ignore_reinit_error=False,
           num_redis_shards=None,
           redis_max_clients=None,
           redis_password=None,
           plasma_directory=None,
           huge_pages=False,
           include_webui=True,
           driver_id=None,
           configure_logging=True,
           logging_level=logging.INFO,
           logging_format=ray_constants.LOGGER_FORMAT,
           plasma_store_socket_name=None,
           plasma_external_store=None,
           raylet_socket_name=None,
           temp_dir=None,
           _internal_config=None,
           use_raylet=None):
      """Connect to an existing Ray cluster or start one and connect to it.
  
      This method handles two cases. Either a Ray cluster already exists and we
      just attach this driver to it, or we start all of the processes associated
      with a Ray cluster and attach to the newly started cluster.
  
      To start Ray and all of the relevant processes, use this as follows:
  
      .. code-block:: python
  
          ray.init()
  
      To connect to an existing Ray cluster, use this as follows (substituting
      in the appropriate address):
  
      .. code-block:: python
  
          ray.init(redis_address="123.45.67.89:6379")
  
      Args:
          redis_address (str): The address of the Redis server to connect to. If
              this address is not provided, then this command will start Redis, a
              global scheduler, a local scheduler, a plasma store, a plasma
              manager, and some workers. It will also kill these processes when
              Python exits.
          num_cpus (int): Number of cpus the user wishes all local schedulers to
              be configured with.
          num_gpus (int): Number of gpus the user wishes all local schedulers to
              be configured with.
          resources: A dictionary mapping the name of a resource to the quantity
              of that resource available.
          object_store_memory: The amount of memory (in bytes) to start the
              object store with.
          redis_max_memory: The max amount of memory (in bytes) to allow redis
              to use, or None for no limit. Once the limit is exceeded, redis
              will start LRU eviction of entries. This only applies to the
              sharded redis tables (task and object tables).
          collect_profiling_data: Whether to collect profiling data from workers.
          node_ip_address (str): The IP address of the node that we are on.
          object_id_seed (int): Used to seed the deterministic generation of
              object IDs. The same value can be used across multiple runs of the
              same job in order to generate the object IDs in a consistent
              manner. However, the same ID should not be used for different jobs.
          local_mode (bool): True if the code should be executed serially
              without Ray. This is useful for debugging.
          redirect_worker_output: True if the stdout and stderr of worker
              processes should be redirected to files.
          redirect_output (bool): True if stdout and stderr for non-worker
              processes should be redirected to files and false otherwise.
          ignore_reinit_error: True if we should suppress errors from calling
              ray.init() a second time.
          num_redis_shards: The number of Redis shards to start in addition to
              the primary Redis shard.
          redis_max_clients: If provided, attempt to configure Redis with this
              maxclients number.
          redis_password (str): Prevents external clients without the password
              from connecting to Redis if provided.
          plasma_directory: A directory where the Plasma memory mapped files will
              be created.
          huge_pages: Boolean flag indicating whether to start the Object
              Store with hugetlbfs support. Requires plasma_directory.
          include_webui: Boolean flag indicating whether to start the web
              UI, which is a Jupyter notebook.
          driver_id: The ID of driver.
          configure_logging: True if allow the logging cofiguration here.
              Otherwise, the users may want to configure it by their own.
          logging_level: Logging level, default will be logging.INFO.
          logging_format: Logging format, default will be "%(message)s"
              which means only contains the message.
          plasma_store_socket_name (str): If provided, it will specify the socket
              name used by the plasma store.
          plasma_external_store (str) : If provided, it will specify the external
              store to use for evicted objects.
          raylet_socket_name (str): If provided, it will specify the socket path
              used by the raylet process.
          temp_dir (str): If provided, it will specify the root temporary
              directory for the Ray process.
          _internal_config (str): JSON configuration for overriding
              RayConfig defaults. For testing purposes ONLY.
  
      Returns:
          Address information about the started processes.
  
      Raises:
          Exception: An exception is raised if an inappropriate combination of
              arguments is passed in.
      """
  
      if configure_logging:
          logging.basicConfig(level=logging_level, format=logging_format)
  
      # Add the use_raylet option for backwards compatibility.
      if use_raylet is not None:
          if use_raylet:
              logger.warning("WARNING: The use_raylet argument has been "
                             "deprecated. Please remove it.")
          else:
              raise DeprecationWarning("The use_raylet argument is deprecated. "
                                       "Please remove it.")
  
      if setproctitle is None:
          logger.warning(
              "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.")
  
      if global_worker.connected:
          if ignore_reinit_error:
              logger.error("Calling ray.init() again after it has already been "
                           "called.")
              return
          else:
              raise Exception("Perhaps you called ray.init twice by accident?")
  
      # Convert hostnames to numerical IP address.
      if node_ip_address is not None:
          node_ip_address = services.address_to_ip(node_ip_address)
      if redis_address is not None:
          redis_address = services.address_to_ip(redis_address)
  
      info = {"node_ip_address": node_ip_address, "redis_address": redis_address}
      ret = _init(
          address_info=info,
          start_ray_local=(redis_address is None),
          num_workers=num_workers,
          object_id_seed=object_id_seed,
          local_mode=local_mode,
          driver_mode=driver_mode,
          redirect_worker_output=redirect_worker_output,
          redirect_output=redirect_output,
          num_cpus=num_cpus,
          num_gpus=num_gpus,
          resources=resources,
          num_redis_shards=num_redis_shards,
          redis_max_clients=redis_max_clients,
          redis_password=redis_password,
          plasma_directory=plasma_directory,
          huge_pages=huge_pages,
          include_webui=include_webui,
          object_store_memory=object_store_memory,
          redis_max_memory=redis_max_memory,
          collect_profiling_data=collect_profiling_data,
          driver_id=driver_id,
          plasma_store_socket_name=plasma_store_socket_name,
          plasma_external_store=plasma_external_store,
          raylet_socket_name=raylet_socket_name,
          temp_dir=temp_dir,
          _internal_config=_internal_config)
      for hook in _post_init_hooks:
          hook()
      return ret
  
  
  # Functions to run as callback after a successful ray init
  _post_init_hooks = []
  
  
  def cleanup(worker=global_worker):
      raise DeprecationWarning(
          "The function ray.worker.cleanup() has been deprecated. Instead, "
          "please call ray.shutdown().")
  
  
  def shutdown(worker=global_worker):
      """Disconnect the worker, and terminate processes started by ray.init().
  
      This will automatically run at the end when a Python process that uses Ray
      exits. It is ok to run this twice in a row. The primary use case for this
      function is to cleanup state between tests.
  
      Note that this will clear any remote function definitions, actor
      definitions, and existing actors, so if you wish to use any previously
      defined remote functions or actors after calling ray.shutdown(), then you
      need to redefine them. If they were defined in an imported module, then you
      will need to reload the module.
      """
      disconnect(worker)
      if hasattr(worker, "raylet_client"):
          del worker.raylet_client
      if hasattr(worker, "plasma_client"):
          worker.plasma_client.disconnect()
  
      if worker.mode == SCRIPT_MODE:
          services.cleanup()
      else:
          # If this is not a driver, make sure there are no orphan processes,
          # besides possibly the worker itself.
          for process_type, processes in services.all_processes.items():
              if process_type == services.PROCESS_TYPE_WORKER:
                  assert len(processes) <= 1
              else:
                  assert len(processes) == 0
  
      worker.set_mode(None)
  
  
  atexit.register(shutdown)
  
  # Define a custom excepthook so that if the driver exits with an exception, we
  # can push that exception to Redis.
  normal_excepthook = sys.excepthook
  
  
  def custom_excepthook(type, value, tb):
      # If this is a driver, push the exception to redis.
      if global_worker.mode == SCRIPT_MODE:
          error_message = "".join(traceback.format_tb(tb))
          global_worker.redis_client.hmset(b"Drivers:" + global_worker.worker_id,
                                           {"exception": error_message})
      # Call the normal excepthook.
      normal_excepthook(type, value, tb)
  
  
  sys.excepthook = custom_excepthook
  
  # The last time we raised a TaskError in this process. We use this value to
  # suppress redundant error messages pushed from the workers.
  last_task_error_raise_time = 0
  
  # The max amount of seconds to wait before printing out an uncaught error.
  UNCAUGHT_ERROR_GRACE_PERIOD = 5
  
  
  def print_error_messages_raylet(task_error_queue):
      """Prints message received in the given output queue.
  
      This checks periodically if any un-raised errors occured in the background.
      """
  
      while True:
          error, t = task_error_queue.get()
          # Delay errors a little bit of time to attempt to suppress redundant
          # messages originating from the worker.
          while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():
              time.sleep(1)
          if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:
              logger.debug("Suppressing error from worker: {}".format(error))
          else:
              logger.error(
                  "Possible unhandled error from worker: {}".format(error))
  
  
  def listen_error_messages_raylet(worker, task_error_queue):
      """Listen to error messages in the background on the driver.
  
      This runs in a separate thread on the driver and pushes (error, time)
      tuples to the output queue.
      """
      worker.error_message_pubsub_client = worker.redis_client.pubsub(
          ignore_subscribe_messages=True)
      # Exports that are published after the call to
      # error_message_pubsub_client.subscribe and before the call to
      # error_message_pubsub_client.listen will still be processed in the loop.
  
      # Really we should just subscribe to the errors for this specific job.
      # However, currently all errors seem to be published on the same channel.
      error_pubsub_channel = str(
          ray.gcs_utils.TablePubsub.ERROR_INFO).encode("ascii")
      worker.error_message_pubsub_client.subscribe(error_pubsub_channel)
      # worker.error_message_pubsub_client.psubscribe("*")
  
      # Get the exports that occurred before the call to subscribe.
      with worker.lock:
          error_messages = global_state.error_messages(worker.task_driver_id)
          for error_message in error_messages:
              logger.error(error_message)
  
      try:
          for msg in worker.error_message_pubsub_client.listen():
  
              gcs_entry = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(
                  msg["data"], 0)
              assert gcs_entry.EntriesLength() == 1
              error_data = ray.gcs_utils.ErrorTableData.GetRootAsErrorTableData(
                  gcs_entry.Entries(0), 0)
              job_id = error_data.JobId()
              if job_id not in [
                      worker.task_driver_id.id(),
                      ray_constants.NIL_JOB_ID.id()
              ]:
                  continue
  
              error_message = ray.utils.decode(error_data.ErrorMessage())
              if (ray.utils.decode(
                      error_data.Type()) == ray_constants.TASK_PUSH_ERROR):
                  # Delay it a bit to see if we can suppress it
                  task_error_queue.put((error_message, time.time()))
              else:
                  logger.error(error_message)
  
      except redis.ConnectionError:
          # When Redis terminates the listen call will throw a ConnectionError,
          # which we catch here.
          pass
  
  
  def is_initialized():
      """Check if ray.init has been called yet.
  
      Returns:
          True if ray.init has already been called and false otherwise.
      """
      return ray.worker.global_worker.connected
  
  
  def print_error_messages(worker):
      """Print error messages in the background on the driver.
  
      This runs in a separate thread on the driver and prints error messages in
      the background.
      """
      # TODO(rkn): All error messages should have a "component" field indicating
      # which process the error came from (e.g., a worker or a plasma store).
      # Currently all error messages come from workers.
  
      worker.error_message_pubsub_client = worker.redis_client.pubsub()
      # Exports that are published after the call to
      # error_message_pubsub_client.subscribe and before the call to
      # error_message_pubsub_client.listen will still be processed in the loop.
      worker.error_message_pubsub_client.subscribe("__keyspace@0__:ErrorKeys")
      num_errors_received = 0
  
      # Get the exports that occurred before the call to subscribe.
      with worker.lock:
          error_keys = worker.redis_client.lrange("ErrorKeys", 0, -1)
          for error_key in error_keys:
              if error_applies_to_driver(error_key, worker=worker):
                  error_message = ray.utils.decode(
                      worker.redis_client.hget(error_key, "message"))
                  logger.error(error_message)
              num_errors_received += 1
  
      try:
          for msg in worker.error_message_pubsub_client.listen():
              with worker.lock:
                  for error_key in worker.redis_client.lrange(
                          "ErrorKeys", num_errors_received, -1):
                      if error_applies_to_driver(error_key, worker=worker):
                          error_message = ray.utils.decode(
                              worker.redis_client.hget(error_key, "message"))
                          logger.error(error_message)
                      num_errors_received += 1
      except redis.ConnectionError:
          # When Redis terminates the listen call will throw a ConnectionError,
          # which we catch here.
          pass
  
  
  def connect(info,
              object_id_seed=None,
              mode=WORKER_MODE,
              worker=global_worker,
              driver_id=None,
              redis_password=None,
              collect_profiling_data=True):
      """Connect this worker to the local scheduler, to Plasma, and to Redis.
  
      Args:
          info (dict): A dictionary with address of the Redis server and the
              sockets of the plasma store and raylet.
          object_id_seed: A seed to use to make the generation of object IDs
              deterministic.
          mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and
              LOCAL_MODE.
          driver_id: The ID of driver. If it's None, then we will generate one.
          redis_password (str): Prevents external clients without the password
              from connecting to Redis if provided.
          collect_profiling_data: Whether to collect profiling data from workers.
      """
      # Do some basic checking to make sure we didn't call ray.init twice.
      error_message = "Perhaps you called ray.init twice by accident?"
      assert not worker.connected, error_message
      assert worker.cached_functions_to_run is not None, error_message
  
      # Enable nice stack traces on SIGSEGV etc.
      faulthandler.enable(all_threads=False)
  
      if collect_profiling_data:
          worker.profiler = profiling.Profiler(worker)
      else:
          worker.profiler = profiling.NoopProfiler()
  
      # Initialize some fields.
      if mode is WORKER_MODE:
          worker.worker_id = random_string()
          if setproctitle:
              setproctitle.setproctitle("ray_worker")
      else:
          # This is the code path of driver mode.
          if driver_id is None:
              driver_id = ray.ObjectID(random_string())
  
          if not isinstance(driver_id, ray.ObjectID):
              raise Exception(
                  "The type of given driver id must be ray.ObjectID.")
  
          worker.worker_id = driver_id.id()
  
      # When tasks are executed on remote workers in the context of multiple
      # drivers, the task driver ID is used to keep track of which driver is
      # responsible for the task so that error messages will be propagated to
      # the correct driver.
      if mode != WORKER_MODE:
          worker.task_driver_id = ray.ObjectID(worker.worker_id)
  
      # All workers start out as non-actors. A worker can be turned into an actor
      # after it is created.
      worker.actor_id = NIL_ACTOR_ID
      worker.connected = True
      worker.set_mode(mode)
  
      # If running Ray in LOCAL_MODE, there is no need to create call
      # create_worker or to start the worker service.
      if mode == LOCAL_MODE:
          return
      # Set the node IP address.
      worker.node_ip_address = info["node_ip_address"]
      worker.redis_address = info["redis_address"]
  
      # Create a Redis client.
      redis_ip_address, redis_port = info["redis_address"].split(":")
      worker.redis_client = thread_safe_client(
          redis.StrictRedis(
              host=redis_ip_address,
              port=int(redis_port),
              password=redis_password))
  
      # For driver's check that the version information matches the version
      # information that the Ray cluster was started with.
      try:
          ray.services.check_version_info(worker.redis_client)
      except Exception as e:
          if mode == SCRIPT_MODE:
              raise e
          elif mode == WORKER_MODE:
              traceback_str = traceback.format_exc()
              ray.utils.push_error_to_driver_through_redis(
                  worker.redis_client,
                  ray_constants.VERSION_MISMATCH_PUSH_ERROR,
                  traceback_str,
                  driver_id=None)
  
      worker.lock = threading.Lock()
  
      # Check the RedirectOutput key in Redis and based on its value redirect
      # worker output and error to their own files.
      if mode == WORKER_MODE:
          # This key is set in services.py when Redis is started.
          redirect_worker_output_val = worker.redis_client.get("RedirectOutput")
          if (redirect_worker_output_val is not None
                  and int(redirect_worker_output_val) == 1):
              redirect_worker_output = 1
          else:
              redirect_worker_output = 0
          if redirect_worker_output:
              log_stdout_file, log_stderr_file = (
                  tempfile_services.new_worker_redirected_log_file(
                      worker.worker_id))
              sys.stdout = log_stdout_file
              sys.stderr = log_stderr_file
              services.record_log_files_in_redis(
                  info["redis_address"],
                  info["node_ip_address"], [log_stdout_file, log_stderr_file],
                  password=redis_password)
  
      # Create an object for interfacing with the global state.
      global_state._initialize_global_state(
          redis_ip_address, int(redis_port), redis_password=redis_password)
  
      # Register the worker with Redis.
      if mode == SCRIPT_MODE:
          # The concept of a driver is the same as the concept of a "job".
          # Register the driver/job with Redis here.
          import __main__ as main
          driver_info = {
              "node_ip_address": worker.node_ip_address,
              "driver_id": worker.worker_id,
              "start_time": time.time(),
              "plasma_store_socket": info["store_socket_name"],
              "raylet_socket": info.get("raylet_socket_name")
          }
          driver_info["name"] = (main.__file__ if hasattr(main, "__file__") else
                                 "INTERACTIVE MODE")
          worker.redis_client.hmset(b"Drivers:" + worker.worker_id, driver_info)
          if (not worker.redis_client.exists("webui")
                  and info["webui_url"] is not None):
              worker.redis_client.hmset("webui", {"url": info["webui_url"]})
          is_worker = False
      elif mode == WORKER_MODE:
          # Register the worker with Redis.
          worker_dict = {
              "node_ip_address": worker.node_ip_address,
              "plasma_store_socket": info["store_socket_name"],
          }
          if redirect_worker_output:
              worker_dict["stdout_file"] = os.path.abspath(log_stdout_file.name)
              worker_dict["stderr_file"] = os.path.abspath(log_stderr_file.name)
          worker.redis_client.hmset(b"Workers:" + worker.worker_id, worker_dict)
          is_worker = True
      else:
          raise Exception("This code should be unreachable.")
  
      # Create an object store client.
      worker.plasma_client = thread_safe_client(
          plasma.connect(info["store_socket_name"]))
  
      raylet_socket = info["raylet_socket_name"]
  
      # If this is a driver, set the current task ID, the task driver ID, and set
      # the task index to 0.
      if mode == SCRIPT_MODE:
          # If the user provided an object_id_seed, then set the current task ID
          # deterministically based on that seed (without altering the state of
          # the user's random number generator). Otherwise, set the current task
          # ID randomly to avoid object ID collisions.
          numpy_state = np.random.get_state()
          if object_id_seed is not None:
              np.random.seed(object_id_seed)
          else:
              # Try to use true randomness.
              np.random.seed(None)
          worker.current_task_id = ray.ObjectID(
              np.random.bytes(ray_constants.ID_SIZE))
          # Reset the state of the numpy random number generator.
          np.random.set_state(numpy_state)
          # Set other fields needed for computing task IDs.
          worker.task_index = 0
          worker.put_index = 1
  
          # Create an entry for the driver task in the task table. This task is
          # added immediately with status RUNNING. This allows us to push errors
          # related to this driver task back to the driver.  For example, if the
          # driver creates an object that is later evicted, we should notify the
          # user that we're unable to reconstruct the object, since we cannot
          # rerun the driver.
          nil_actor_counter = 0
  
          function_descriptor = FunctionDescriptor.for_driver_task()
          driver_task = ray.raylet.Task(
              worker.task_driver_id,
              function_descriptor.get_function_descriptor_list(), [], 0,
              worker.current_task_id, worker.task_index,
              ray.ObjectID(NIL_ACTOR_ID), ray.ObjectID(NIL_ACTOR_ID), 0,
              ray.ObjectID(NIL_ACTOR_ID), ray.ObjectID(NIL_ACTOR_ID),
              nil_actor_counter, [], {"CPU": 0}, {})
  
          # Add the driver task to the task table.
          global_state._execute_command(driver_task.task_id(), "RAY.TABLE_ADD",
                                        ray.gcs_utils.TablePrefix.RAYLET_TASK,
                                        ray.gcs_utils.TablePubsub.RAYLET_TASK,
                                        driver_task.task_id().id(),
                                        driver_task._serialized_raylet_task())
  
          # Set the driver's current task ID to the task ID assigned to the
          # driver task.
          worker.current_task_id = driver_task.task_id()
      else:
          # A non-driver worker begins without an assigned task.
          worker.current_task_id = ray.ObjectID(NIL_ID)
      # A flag for making sure that we only print one warning message about
      # multithreading per worker.
      worker.multithreading_warned = False
  
      worker.raylet_client = ray.raylet.RayletClient(
          raylet_socket, worker.worker_id, is_worker, worker.current_task_id)
  
      # Start the import thread
      import_thread.ImportThread(worker, mode).start()
  
      # If this is a driver running in SCRIPT_MODE, start a thread to print error
      # messages asynchronously in the background. Ideally the scheduler would
      # push messages to the driver's worker service, but we ran into bugs when
      # trying to properly shutdown the driver's worker service, so we are
      # temporarily using this implementation which constantly queries the
      # scheduler for new error messages.
      if mode == SCRIPT_MODE:
          q = queue.Queue()
          listener = threading.Thread(
              target=listen_error_messages_raylet,
              name="ray_listen_error_messages",
              args=(worker, q))
          printer = threading.Thread(
              target=print_error_messages_raylet,
              name="ray_print_error_messages",
              args=(q, ))
          listener.daemon = True
          listener.start()
          printer.daemon = True
          printer.start()
  
      # If we are using the raylet code path and we are not in local mode, start
      # a background thread to periodically flush profiling data to the GCS.
      if mode != LOCAL_MODE:
          worker.profiler.start_flush_thread()
  
      if mode == SCRIPT_MODE:
          # Add the directory containing the script that is running to the Python
          # paths of the workers. Also add the current directory. Note that this
          # assumes that the directory structures on the machines in the clusters
          # are the same.
          script_directory = os.path.abspath(os.path.dirname(sys.argv[0]))
          current_directory = os.path.abspath(os.path.curdir)
          worker.run_function_on_all_workers(
              lambda worker_info: sys.path.insert(1, script_directory))
          worker.run_function_on_all_workers(
              lambda worker_info: sys.path.insert(1, current_directory))
          # TODO(rkn): Here we first export functions to run, then remote
          # functions. The order matters. For example, one of the functions to
          # run may set the Python path, which is needed to import a module used
          # to define a remote function. We may want to change the order to
          # simply be the order in which the exports were defined on the driver.
          # In addition, we will need to retain the ability to decide what the
          # first few exports are (mostly to set the Python path). Additionally,
          # note that the first exports to be defined on the driver will be the
          # ones defined in separate modules that are imported by the driver.
          # Export cached functions_to_run.
          for function in worker.cached_functions_to_run:
              worker.run_function_on_all_workers(function)
          # Export cached remote functions and actors to the workers.
          worker.function_actor_manager.export_cached()
      worker.cached_functions_to_run = None
  
  
  def disconnect(worker=global_worker):
      """Disconnect this worker from the scheduler and object store."""
      # Reset the list of cached remote functions and actors so that if more
      # remote functions or actors are defined and then connect is called again,
      # the remote functions will be exported. This is mostly relevant for the
      # tests.
      worker.connected = False
      worker.cached_functions_to_run = []
      worker.function_actor_manager.reset_cache()
      worker.serialization_context_map.clear()
  
  
  @contextmanager
  def _changeproctitle(title, next_title):
      if setproctitle:
          setproctitle.setproctitle(title)
      yield
      if setproctitle:
          setproctitle.setproctitle(next_title)
  
  
  def _try_to_compute_deterministic_class_id(cls, depth=5):
      """Attempt to produce a deterministic class ID for a given class.
  
      The goal here is for the class ID to be the same when this is run on
      different worker processes. Pickling, loading, and pickling again seems to
      produce more consistent results than simply pickling. This is a bit crazy
      and could cause problems, in which case we should revert it and figure out
      something better.
  
      Args:
          cls: The class to produce an ID for.
          depth: The number of times to repeatedly try to load and dump the
              string while trying to reach a fixed point.
  
      Returns:
          A class ID for this class. We attempt to make the class ID the same
              when this function is run on different workers, but that is not
              guaranteed.
  
      Raises:
          Exception: This could raise an exception if cloudpickle raises an
              exception.
      """
      # Pickling, loading, and pickling again seems to produce more consistent
      # results than simply pickling. This is a bit
      class_id = pickle.dumps(cls)
      for _ in range(depth):
          new_class_id = pickle.dumps(pickle.loads(class_id))
          if new_class_id == class_id:
              # We appear to have reached a fix point, so use this as the ID.
              return hashlib.sha1(new_class_id).digest()
          class_id = new_class_id
  
      # We have not reached a fixed point, so we may end up with a different
      # class ID for this custom class on each worker, which could lead to the
      # same class definition being exported many many times.
      logger.warning(
          "WARNING: Could not produce a deterministic class ID for class "
          "{}".format(cls))
      return hashlib.sha1(new_class_id).digest()
  
  
  def register_custom_serializer(cls,
                                 use_pickle=False,
                                 use_dict=False,
                                 serializer=None,
                                 deserializer=None,
                                 local=False,
                                 driver_id=None,
                                 class_id=None,
                                 worker=global_worker):
      """Enable serialization and deserialization for a particular class.
  
      This method runs the register_class function defined below on every worker,
      which will enable ray to properly serialize and deserialize objects of
      this class.
  
      Args:
          cls (type): The class that ray should use this custom serializer for.
          use_pickle (bool): If true, then objects of this class will be
              serialized using pickle.
          use_dict: If true, then objects of this class be serialized turning
              their __dict__ fields into a dictionary. Must be False if
              use_pickle is true.
          serializer: The custom serializer to use. This should be provided if
              and only if use_pickle and use_dict are False.
          deserializer: The custom deserializer to use. This should be provided
              if and only if use_pickle and use_dict are False.
          local: True if the serializers should only be registered on the current
              worker. This should usually be False.
          driver_id: ID of the driver that we want to register the class for.
          class_id: ID of the class that we are registering. If this is not
              specified, we will calculate a new one inside the function.
  
      Raises:
          Exception: An exception is raised if pickle=False and the class cannot
              be efficiently serialized by Ray. This can also raise an exception
              if use_dict is true and cls is not pickleable.
      """
      assert (serializer is None) == (deserializer is None), (
          "The serializer/deserializer arguments must both be provided or "
          "both not be provided.")
      use_custom_serializer = (serializer is not None)
  
      assert use_custom_serializer + use_pickle + use_dict == 1, (
          "Exactly one of use_pickle, use_dict, or serializer/deserializer must "
          "be specified.")
  
      if use_dict:
          # Raise an exception if cls cannot be serialized efficiently by Ray.
          serialization.check_serializable(cls)
  
      if class_id is None:
          if not local:
              # In this case, the class ID will be used to deduplicate the class
              # across workers. Note that cloudpickle unfortunately does not
              # produce deterministic strings, so these IDs could be different
              # on different workers. We could use something weaker like
              # cls.__name__, however that would run the risk of having
              # collisions.
              # TODO(rkn): We should improve this.
              try:
                  # Attempt to produce a class ID that will be the same on each
                  # worker. However, determinism is not guaranteed, and the
                  # result may be different on different workers.
                  class_id = _try_to_compute_deterministic_class_id(cls)
              except Exception:
                  raise serialization.CloudPickleError("Failed to pickle class "
                                                       "'{}'".format(cls))
          else:
              # In this case, the class ID only needs to be meaningful on this
              # worker and not across workers.
              class_id = random_string()
  
          # Make sure class_id is a string.
          class_id = ray.utils.binary_to_hex(class_id)
  
      if driver_id is None:
          driver_id_bytes = worker.task_driver_id.id()
      else:
          driver_id_bytes = driver_id.id()
  
      def register_class_for_serialization(worker_info):
          # TODO(rkn): We need to be more thoughtful about what to do if custom
          # serializers have already been registered for class_id. In some cases,
          # we may want to use the last user-defined serializers and ignore
          # subsequent calls to register_custom_serializer that were made by the
          # system.
  
          serialization_context = worker_info[
              "worker"].get_serialization_context(ray.ObjectID(driver_id_bytes))
          serialization_context.register_type(
              cls,
              class_id,
              pickle=use_pickle,
              custom_serializer=serializer,
              custom_deserializer=deserializer)
  
      if not local:
          worker.run_function_on_all_workers(register_class_for_serialization)
      else:
          # Since we are pickling objects of this class, we don't actually need
          # to ship the class definition.
          register_class_for_serialization({"worker": worker})
  
  
  def get(object_ids, worker=global_worker):
      """Get a remote object or a list of remote objects from the object store.
  
      This method blocks until the object corresponding to the object ID is
      available in the local object store. If this object is not in the local
      object store, it will be shipped from an object store that has it (once the
      object has been created). If object_ids is a list, then the objects
      corresponding to each object in the list will be returned.
  
      Args:
          object_ids: Object ID of the object to get or a list of object IDs to
              get.
  
      Returns:
          A Python object or a list of Python objects.
  
      Raises:
          Exception: An exception is raised if the task that created the object
              or that created one of the objects raised an exception.
      """
      worker.check_connected()
      with profiling.profile("ray.get", worker=worker):
          if worker.mode == LOCAL_MODE:
              # In LOCAL_MODE, ray.get is the identity operation (the input will
              # actually be a value not an objectid).
              return object_ids
          global last_task_error_raise_time
          if isinstance(object_ids, list):
              values = worker.get_object(object_ids)
              for i, value in enumerate(values):
                  if isinstance(value, RayTaskError):
                      last_task_error_raise_time = time.time()
                      raise value
              return values
          else:
              value = worker.get_object([object_ids])[0]
              if isinstance(value, RayTaskError):
                  # If the result is a RayTaskError, then the task that created
                  # this object failed, and we should propagate the error message
                  # here.
                  last_task_error_raise_time = time.time()
                  raise value
              return value
  
  
  def put(value, worker=global_worker):
      """Store an object in the object store.
  
      Args:
          value: The Python object to be stored.
  
      Returns:
          The object ID assigned to this value.
      """
      worker.check_connected()
      with profiling.profile("ray.put", worker=worker):
          if worker.mode == LOCAL_MODE:
              # In LOCAL_MODE, ray.put is the identity operation.
              return value
          object_id = worker.raylet_client.compute_put_id(
              worker.current_task_id, worker.put_index)
          worker.put_object(object_id, value)
          worker.put_index += 1
          return object_id
  
  
  def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
      """Return a list of IDs that are ready and a list of IDs that are not.
  
      If timeout is set, the function returns either when the requested number of
      IDs are ready or when the timeout is reached, whichever occurs first. If it
      is not set, the function simply waits until that number of objects is ready
      and returns that exact number of object IDs.
  
      This method returns two lists. The first list consists of object IDs that
      correspond to objects that are available in the object store. The second
      list corresponds to the rest of the object IDs (which may or may not be
      ready).
  
      Ordering of the input list of object IDs is preserved. That is, if A
      precedes B in the input list, and both are in the ready list, then A will
      precede B in the ready list. This also holds true if A and B are both in
      the remaining list.
  
      Args:
          object_ids (List[ObjectID]): List of object IDs for objects that may or
              may not be ready. Note that these IDs must be unique.
          num_returns (int): The number of object IDs that should be returned.
          timeout (int): The maximum amount of time in milliseconds to wait
              before returning.
  
      Returns:
          A list of object IDs that are ready and a list of the remaining object
          IDs.
      """
  
      if isinstance(object_ids, ray.ObjectID):
          raise TypeError(
              "wait() expected a list of ObjectID, got a single ObjectID")
  
      if not isinstance(object_ids, list):
          raise TypeError("wait() expected a list of ObjectID, got {}".format(
              type(object_ids)))
  
      if worker.mode != LOCAL_MODE:
          for object_id in object_ids:
              if not isinstance(object_id, ray.ObjectID):
                  raise TypeError("wait() expected a list of ObjectID, "
                                  "got list containing {}".format(
                                      type(object_id)))
  
      worker.check_connected()
      # TODO(swang): Check main thread.
      with profiling.profile("ray.wait", worker=worker):
          # When Ray is run in LOCAL_MODE, all functions are run immediately,
          # so all objects in object_id are ready.
          if worker.mode == LOCAL_MODE:
              return object_ids[:num_returns], object_ids[num_returns:]
  
          # TODO(rkn): This is a temporary workaround for
          # https://github.com/ray-project/ray/issues/997. However, it should be
          # fixed in Arrow instead of here.
          if len(object_ids) == 0:
              return [], []
  
          if len(object_ids) != len(set(object_ids)):
              raise Exception("Wait requires a list of unique object IDs.")
          if num_returns <= 0:
              raise Exception(
                  "Invalid number of objects to return %d." % num_returns)
          if num_returns > len(object_ids):
              raise Exception("num_returns cannot be greater than the number "
                              "of objects provided to ray.wait.")
  
          # Get the task ID, to notify the backend which task is blocked.
          with worker.state_lock:
              current_task_id = worker.get_current_thread_task_id()
  
          timeout = timeout if timeout is not None else 2**30
          ready_ids, remaining_ids = worker.raylet_client.wait(
              object_ids, num_returns, timeout, False, current_task_id)
          return ready_ids, remaining_ids
  
  
  def _mode(worker=global_worker):
      """This is a wrapper around worker.mode.
  
      We use this wrapper so that in the remote decorator, we can call _mode()
      instead of worker.mode. The difference is that when we attempt to serialize
      remote functions, we don't attempt to serialize the worker object, which
      cannot be serialized.
      """
      return worker.mode
  
  
  def get_global_worker():
      return global_worker
  
  
  def make_decorator(num_return_vals=None,
                     num_cpus=None,
                     num_gpus=None,
                     resources=None,
                     max_calls=None,
                     checkpoint_interval=None,
                     max_reconstructions=None,
                     worker=None):
      def decorator(function_or_class):
          if (inspect.isfunction(function_or_class)
                  or is_cython(function_or_class)):
              # Set the remote function default resources.
              if checkpoint_interval is not None:
                  raise Exception("The keyword 'checkpoint_interval' is not "
                                  "allowed for remote functions.")
              if max_reconstructions is not None:
                  raise Exception("The keyword 'max_reconstructions' is not "
                                  "allowed for remote functions.")
  
              return ray.remote_function.RemoteFunction(
                  function_or_class, num_cpus, num_gpus, resources,
                  num_return_vals, max_calls)
  
          if inspect.isclass(function_or_class):
              if num_return_vals is not None:
                  raise Exception("The keyword 'num_return_vals' is not allowed "
                                  "for actors.")
              if max_calls is not None:
                  raise Exception("The keyword 'max_calls' is not allowed for "
                                  "actors.")
  
              # Set the actor default resources.
              if num_cpus is None and num_gpus is None and resources is None:
                  # In the default case, actors acquire no resources for
                  # their lifetime, and actor methods will require 1 CPU.
                  cpus_to_use = DEFAULT_ACTOR_CREATION_CPUS_SIMPLE_CASE
                  actor_method_cpus = DEFAULT_ACTOR_METHOD_CPUS_SIMPLE_CASE
              else:
                  # If any resources are specified, then all resources are
                  # acquired for the actor's lifetime and no resources are
                  # associated with methods.
                  cpus_to_use = (DEFAULT_ACTOR_CREATION_CPUS_SPECIFIED_CASE
                                 if num_cpus is None else num_cpus)
                  actor_method_cpus = DEFAULT_ACTOR_METHOD_CPUS_SPECIFIED_CASE
  
              return worker.make_actor(function_or_class, cpus_to_use, num_gpus,
                                       resources, actor_method_cpus,
                                       checkpoint_interval, max_reconstructions)
  
          raise Exception("The @ray.remote decorator must be applied to "
                          "either a function or to a class.")
  
      return decorator
  
  
  def remote(*args, **kwargs):
      """Define a remote function or an actor class.
  
      This can be used with no arguments to define a remote function or actor as
      follows:
  
      .. code-block:: python
  
          @ray.remote
          def f():
              return 1
  
          @ray.remote
          class Foo(object):
              def method(self):
                  return 1
  
      It can also be used with specific keyword arguments:
  
      * **num_return_vals:** This is only for *remote functions*. It specifies
        the number of object IDs returned by the remote function invocation.
      * **num_cpus:** The quantity of CPU cores to reserve for this task or for
        the lifetime of the actor.
      * **num_gpus:** The quantity of GPUs to reserve for this task or for the
        lifetime of the actor.
      * **resources:** The quantity of various custom resources to reserve for
        this task or for the lifetime of the actor. This is a dictionary mapping
        strings (resource names) to numbers.
      * **max_calls:** Only for *remote functions*. This specifies the maximum
        number of times that a given worker can execute the given remote function
        before it must exit (this can be used to address memory leaks in
        third-party libraries or to reclaim resources that cannot easily be
        released, e.g., GPU memory that was acquired by TensorFlow). By
        default this is infinite.
      * **max_reconstructions**: Only for *actors*. This specifies the maximum
        number of times that the actor should be reconstructed when it dies
        unexpectedly. The minimum valid value is 0 (default), which indicates
        that the actor doesn't need to be reconstructed. And the maximum valid
        value is ray.ray_constants.INFINITE_RECONSTRUCTIONS.
  
      This can be done as follows:
  
      .. code-block:: python
  
          @ray.remote(num_gpus=1, max_calls=1, num_return_vals=2)
          def f():
              return 1, 2
  
          @ray.remote(num_cpus=2, resources={"CustomResource": 1})
          class Foo(object):
              def method(self):
                  return 1
      """
      worker = get_global_worker()
  
      if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
          # This is the case where the decorator is just @ray.remote.
          return make_decorator(worker=worker)(args[0])
  
      # Parse the keyword arguments from the decorator.
      error_string = ("The @ray.remote decorator must be applied either "
                      "with no arguments and no parentheses, for example "
                      "'@ray.remote', or it must be applied using some of "
                      "the arguments 'num_return_vals', 'num_cpus', 'num_gpus', "
                      "'resources', 'max_calls', 'checkpoint_interval',"
                      "or 'max_reconstructions', like "
                      "'@ray.remote(num_return_vals=2, "
                      "resources={\"CustomResource\": 1})'.")
      assert len(args) == 0 and len(kwargs) > 0, error_string
      for key in kwargs:
          assert key in [
              "num_return_vals", "num_cpus", "num_gpus", "resources",
              "max_calls", "checkpoint_interval", "max_reconstructions"
          ], error_string
  
      num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else None
      num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else None
      resources = kwargs.get("resources")
      if not isinstance(resources, dict) and resources is not None:
          raise Exception("The 'resources' keyword argument must be a "
                          "dictionary, but received type {}.".format(
                              type(resources)))
      if resources is not None:
          assert "CPU" not in resources, "Use the 'num_cpus' argument."
          assert "GPU" not in resources, "Use the 'num_gpus' argument."
  
      # Handle other arguments.
      num_return_vals = kwargs.get("num_return_vals")
      max_calls = kwargs.get("max_calls")
      checkpoint_interval = kwargs.get("checkpoint_interval")
      max_reconstructions = kwargs.get("max_reconstructions")
  
      return make_decorator(
          num_return_vals=num_return_vals,
          num_cpus=num_cpus,
          num_gpus=num_gpus,
          resources=resources,
          max_calls=max_calls,
          checkpoint_interval=checkpoint_interval,
          max_reconstructions=max_reconstructions,
          worker=worker)
  