
"""simple docstring"""


#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist



def 	lowerCamelCase__			(							*UpperCAmelCase_				)->							Tuple:







       """simple docstring"""
       with open(UpperCAmelCase_		,      "r"				) as fh:
              fcntl.flock(UpperCAmelCase_		,      fcntl.LOCK_EX				)
              try:
                     print(*UpperCAmelCase_				)
              finally:
                     fcntl.flock(UpperCAmelCase_		,      fcntl.LOCK_UN				)


SCREAMING_SNAKE_CASE      =     int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
SCREAMING_SNAKE_CASE      =     torch.device("""cuda""", local_rank)
SCREAMING_SNAKE_CASE      =     socket.gethostname()

SCREAMING_SNAKE_CASE      =     F'''[{hostname}-{local_rank}]'''

try:
      # test distributed
      dist.init_process_group("""nccl""")
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      SCREAMING_SNAKE_CASE      =     dist.get_rank()
      SCREAMING_SNAKE_CASE      =     dist.get_world_size()

      printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

      dist.barrier()
      if rank == 0:
            printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
      printflock(F'''{gpu} is broken''')
      raise







"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str   =			None






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							str			)->  Any:







              """simple docstring"""
              raise NotImplementedError






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  List[str]:







              """simple docstring"""
              if not self.is_available():
                     raise RuntimeError(
                         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."			)






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Union[str, Any]			)->  int:







              """simple docstring"""
              return f"`pip install {cls.pip_package or cls.name}`"





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			'''optuna'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Union[str, Any]:







              """simple docstring"""
              return is_optuna_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              return run_hp_search_optuna(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_optuna(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''ray'''
       UpperCamelCase_		:					Tuple   =			'''\'ray[tune]\''''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  Any:







              """simple docstring"""
              return is_ray_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							int			)->  int:







              """simple docstring"""
              return run_hp_search_ray(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              return default_hp_space_ray(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			'''sigopt'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[str]:







              """simple docstring"""
              return is_sigopt_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return run_hp_search_sigopt(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              return default_hp_space_sigopt(UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			'''wandb'''






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  )->  List[Any]:







              """simple docstring"""
              return is_wandb_available()






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							List[str]			)->  str:







              """simple docstring"""
              return run_hp_search_wandb(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							List[Any]			)->  List[Any]:







              """simple docstring"""
              return default_hp_space_wandb(UpperCAmelCase_			)





SCREAMING_SNAKE_CASE      =     {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(UpperCAmelCase_				) > 0:
              UpperCamelCase					 =		available_backends[0].name
              if len(UpperCAmelCase_				) > 1:
                     logger.info(
                         F"{len(UpperCAmelCase_				)} hyperparameter search backends available. Using {name} as the default."				)
              return name
       raise RuntimeError(
           "No hyperparameter search backend available.\n"
           + "\n".join(
               F" - To install {backend.name} run {backend.pip_install()}"
               for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)





