mirror of
https://github.com/gryf/coach.git
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Moved main module to cli
This commit is contained in:
Roman Dobosz
334
coach/cli.py
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334
coach/cli.py
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@@ -0,0 +1,334 @@
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#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import argparse
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import atexit
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import json
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import os
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import re
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import subprocess
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import sys
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import time
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from coach import agents # noqa
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from coach import configurations as conf
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from coach import environments
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from coach import logger
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from coach import presets
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from coach import utils
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if len(set(logger.failed_imports)) > 0:
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logger.screen.warning("Warning: failed to import the following packages - {}".format(', '.join(set(logger.failed_imports))))
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def set_framework(framework_type):
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# choosing neural network framework
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framework = conf.Frameworks().get(framework_type)
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sess = None
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if framework == conf.Frameworks.TensorFlow:
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import tensorflow as tf
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config = tf.ConfigProto()
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config.allow_soft_placement = True
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config.gpu_options.allow_growth = True
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config.gpu_options.per_process_gpu_memory_fraction = 0.2
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sess = tf.Session(config=config)
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elif framework == conf.Frameworks.Neon:
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import ngraph as ng
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sess = ng.transformers.make_transformer()
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logger.screen.log_title("Using {} framework".format(conf.Frameworks().to_string(framework)))
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return sess
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def check_input_and_fill_run_dict(parser):
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args = parser.parse_args()
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# if no arg is given
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if len(sys.argv) == 1:
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parser.print_help()
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exit(0)
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# list available presets
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if args.list:
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presets_lists = utils.list_all_classes_in_module(presets)
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logger.screen.log_title("Available Presets:")
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for preset in presets_lists:
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print(preset)
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sys.exit(0)
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# check inputs
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try:
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# num_workers = int(args.num_workers)
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num_workers = int(re.match("^\d+$", args.num_workers).group(0))
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except ValueError:
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logger.screen.error("Parameter num_workers should be an integer.")
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preset_names = utils.list_all_classes_in_module(presets)
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if args.preset is not None and args.preset not in preset_names:
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logger.screen.error("A non-existing preset was selected. ")
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if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
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logger.screen.error("The requested checkpoint folder to load from does not exist. ")
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if args.save_model_sec is not None:
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try:
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args.save_model_sec = int(args.save_model_sec)
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except ValueError:
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logger.screen.error("Parameter save_model_sec should be an integer.")
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if args.preset is None and (args.agent_type is None or args.environment_type is None
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or args.exploration_policy_type is None) and not args.play:
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logger.screen.error('When no preset is given for Coach to run, the user is expected to input the desired agent_type,'
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' environment_type and exploration_policy_type to assemble a preset. '
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'\nAt least one of these parameters was not given.')
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elif args.preset is None and args.play and args.environment_type is None:
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logger.screen.error('When no preset is given for Coach to run, and the user requests human control over the environment,'
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' the user is expected to input the desired environment_type and level.'
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'\nAt least one of these parameters was not given.')
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elif args.preset is None and args.play and args.environment_type:
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args.agent_type = 'Human'
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args.exploration_policy_type = 'ExplorationParameters'
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# get experiment name and path
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experiment_name = logger.logger.get_experiment_name(args.experiment_name)
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experiment_path = logger.logger.get_experiment_path(experiment_name)
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if args.play and num_workers > 1:
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logger.screen.warning("Playing the game as a human is only available with a single worker. "
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"The number of workers will be reduced to 1")
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num_workers = 1
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# fill run_dict
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run_dict = dict()
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run_dict['agent_type'] = args.agent_type
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run_dict['environment_type'] = args.environment_type
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run_dict['exploration_policy_type'] = args.exploration_policy_type
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run_dict['level'] = args.level
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run_dict['preset'] = args.preset
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run_dict['custom_parameter'] = args.custom_parameter
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run_dict['experiment_path'] = experiment_path
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run_dict['framework'] = conf.Frameworks().get(args.framework)
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run_dict['play'] = args.play
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run_dict['evaluate'] = args.evaluate# or args.play
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# multi-threading parameters
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run_dict['num_threads'] = num_workers
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# checkpoints
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run_dict['save_model_sec'] = args.save_model_sec
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run_dict['save_model_dir'] = experiment_path if args.save_model_sec is not None else None
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run_dict['checkpoint_restore_dir'] = args.checkpoint_restore_dir
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# visualization
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run_dict['visualization.dump_gifs'] = args.dump_gifs
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run_dict['visualization.render'] = args.render
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run_dict['visualization.tensorboard'] = args.tensorboard
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return args, run_dict
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def run_dict_to_json(_run_dict, task_id=''):
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if task_id != '':
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json_path = os.path.join(_run_dict['experiment_path'], 'run_dict_worker{}.json'.format(task_id))
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else:
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json_path = os.path.join(_run_dict['experiment_path'], 'run_dict.json')
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with open(json_path, 'w') as outfile:
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json.dump(_run_dict, outfile, indent=2)
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return json_path
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('-p', '--preset',
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help="(string) Name of a preset to run (as configured in presets.py)",
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default=None,
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type=str)
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parser.add_argument('-l', '--list',
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help="(flag) List all available presets",
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action='store_true')
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parser.add_argument('-e', '--experiment_name',
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help="(string) Experiment name to be used to store the results.",
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default='',
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type=str)
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parser.add_argument('-r', '--render',
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help="(flag) Render environment",
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action='store_true')
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parser.add_argument('-f', '--framework',
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help="(string) Neural network framework. Available values: tensorflow, neon",
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default='tensorflow',
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type=str)
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parser.add_argument('-n', '--num_workers',
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help="(int) Number of workers for multi-process based agents, e.g. A3C",
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default='1',
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type=str)
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parser.add_argument('--play',
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help="(flag) Play as a human by controlling the game with the keyboard. "
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"This option will save a replay buffer with the game play.",
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action='store_true')
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parser.add_argument('--evaluate',
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help="(flag) Run evaluation only. This is a convenient way to disable "
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"training in order to evaluate an existing checkpoint.",
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action='store_true')
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parser.add_argument('-v', '--verbose',
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help="(flag) Don't suppress TensorFlow debug prints.",
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action='store_true')
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parser.add_argument('-s', '--save_model_sec',
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help="(int) Time in seconds between saving checkpoints of the model.",
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default=None,
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type=int)
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parser.add_argument('-crd', '--checkpoint_restore_dir',
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help='(string) Path to a folder containing a checkpoint to restore the model from.',
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type=str)
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parser.add_argument('-dg', '--dump_gifs',
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help="(flag) Enable the gif saving functionality.",
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action='store_true')
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parser.add_argument('-at', '--agent_type',
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help="(string) Choose an agent type class to override on top of the selected preset. "
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"If no preset is defined, a preset can be set from the command-line by combining settings "
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"which are set by using --agent_type, --experiment_type, --environemnt_type",
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default=None,
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type=str)
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parser.add_argument('-et', '--environment_type',
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help="(string) Choose an environment type class to override on top of the selected preset."
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"If no preset is defined, a preset can be set from the command-line by combining settings "
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"which are set by using --agent_type, --experiment_type, --environemnt_type",
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default=None,
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type=str)
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parser.add_argument('-ept', '--exploration_policy_type',
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help="(string) Choose an exploration policy type class to override on top of the selected "
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"preset."
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"If no preset is defined, a preset can be set from the command-line by combining settings "
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"which are set by using --agent_type, --experiment_type, --environemnt_type"
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,
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default=None,
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type=str)
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parser.add_argument('-lvl', '--level',
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help="(string) Choose the level that will be played in the environment that was selected."
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"This value will override the level parameter in the environment class."
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,
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default=None,
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type=str)
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parser.add_argument('-cp', '--custom_parameter',
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help="(string) Semicolon separated parameters used to override specific parameters on top of"
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" the selected preset (or on top of the command-line assembled one). "
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"Whenever a parameter value is a string, it should be inputted as '\\\"string\\\"'. "
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"For ex.: "
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"\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"",
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default=None,
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type=str)
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parser.add_argument('--print_parameters',
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help="(flag) Print tuning_parameters to stdout",
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action='store_true')
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parser.add_argument('-tb', '--tensorboard',
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help="(flag) When using the TensorFlow backend, enable TensorBoard log dumps. ",
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action='store_true')
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parser.add_argument('-ns', '--no_summary',
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help="(flag) Prevent Coach from printing a summary and asking questions at the end of runs",
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action='store_true')
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args, run_dict = check_input_and_fill_run_dict(parser)
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# turn TF debug prints off
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if not args.verbose and args.framework.lower() == 'tensorflow':
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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# dump documentation
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logger.logger.set_dump_dir(run_dict['experiment_path'], add_timestamp=True)
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if not args.no_summary:
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atexit.register(logger.logger.summarize_experiment)
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logger.screen.change_terminal_title(logger.logger.experiment_name)
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# Single-threaded runs
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if run_dict['num_threads'] == 1:
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# set tuning parameters
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json_run_dict_path = run_dict_to_json(run_dict)
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tuning_parameters = presets.json_to_preset(json_run_dict_path)
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tuning_parameters.sess = set_framework(args.framework)
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if args.print_parameters:
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print('tuning_parameters', tuning_parameters)
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# Single-thread runs
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tuning_parameters.task_index = 0
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env_instance = environments.create_environment(tuning_parameters)
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agent = eval('agents.' + tuning_parameters.agent.type +
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'(env_instance, tuning_parameters)')
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# Start the training or evaluation
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if tuning_parameters.evaluate:
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agent.evaluate(sys.maxsize, keep_networks_synced=True) # evaluate forever
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else:
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agent.improve()
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# Multi-threaded runs
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else:
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assert args.framework.lower() == 'tensorflow', "Distributed training works only with TensorFlow"
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os.environ["OMP_NUM_THREADS"]="1"
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# set parameter server and workers addresses
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ps_hosts = "localhost:{}".format(utils.get_open_port())
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worker_hosts = ",".join(["localhost:{}".format(utils.get_open_port()) for i in range(run_dict['num_threads'] + 1)])
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# Make sure to disable GPU so that all the workers will use the CPU
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utils.set_cpu()
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# create a parameter server
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cmd = [
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"python3",
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"./parallel_actor.py",
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"--ps_hosts={}".format(ps_hosts),
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"--worker_hosts={}".format(worker_hosts),
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"--job_name=ps",
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]
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parameter_server = subprocess.Popen(cmd)
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logger.screen.log_title("*** Distributed Training ***")
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time.sleep(1)
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# create N training workers and 1 evaluating worker
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workers = []
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for i in range(run_dict['num_threads'] + 1):
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# this is the evaluation worker
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run_dict['task_id'] = i
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if i == run_dict['num_threads']:
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run_dict['evaluate_only'] = True
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run_dict['visualization.render'] = args.render
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else:
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run_dict['evaluate_only'] = False
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run_dict['visualization.render'] = False # #In a parallel setting, only the evaluation agent renders
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json_run_dict_path = run_dict_to_json(run_dict, i)
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workers_args = ["python3", "./parallel_actor.py",
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"--ps_hosts={}".format(ps_hosts),
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"--worker_hosts={}".format(worker_hosts),
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"--job_name=worker",
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"--load_json={}".format(json_run_dict_path)]
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p = subprocess.Popen(workers_args)
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if i != run_dict['num_threads']:
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workers.append(p)
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else:
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evaluation_worker = p
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# wait for all workers
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[w.wait() for w in workers]
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evaluation_worker.kill()
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if __name__ == "__main__":
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main()
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315
scripts/coach
315
scripts/coach
@@ -14,318 +14,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
|
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#
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import argparse
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import atexit
|
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import json
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import os
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import re
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import subprocess
|
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import sys
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import time
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from coach import agents # noqa
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from coach import configurations as conf
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from coach import environments
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from coach import logger
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from coach import presets
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from coach import utils
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from coach import cli
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if len(set(logger.failed_imports)) > 0:
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logger.screen.warning("Warning: failed to import the following packages - {}".format(', '.join(set(logger.failed_imports))))
|
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def set_framework(framework_type):
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# choosing neural network framework
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framework = conf.Frameworks().get(framework_type)
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sess = None
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if framework == conf.Frameworks.TensorFlow:
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import tensorflow as tf
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config = tf.ConfigProto()
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config.allow_soft_placement = True
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config.gpu_options.allow_growth = True
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config.gpu_options.per_process_gpu_memory_fraction = 0.2
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sess = tf.Session(config=config)
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elif framework == conf.Frameworks.Neon:
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import ngraph as ng
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sess = ng.transformers.make_transformer()
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logger.screen.log_title("Using {} framework".format(conf.Frameworks().to_string(framework)))
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return sess
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def check_input_and_fill_run_dict(parser):
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args = parser.parse_args()
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|
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# if no arg is given
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if len(sys.argv) == 1:
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parser.print_help()
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exit(0)
|
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|
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# list available presets
|
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if args.list:
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presets_lists = utils.list_all_classes_in_module(presets)
|
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logger.screen.log_title("Available Presets:")
|
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for preset in presets_lists:
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print(preset)
|
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sys.exit(0)
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|
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# check inputs
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try:
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# num_workers = int(args.num_workers)
|
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num_workers = int(re.match("^\d+$", args.num_workers).group(0))
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except ValueError:
|
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logger.screen.error("Parameter num_workers should be an integer.")
|
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|
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preset_names = utils.list_all_classes_in_module(presets)
|
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if args.preset is not None and args.preset not in preset_names:
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logger.screen.error("A non-existing preset was selected. ")
|
||||
|
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if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
|
||||
logger.screen.error("The requested checkpoint folder to load from does not exist. ")
|
||||
|
||||
if args.save_model_sec is not None:
|
||||
try:
|
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args.save_model_sec = int(args.save_model_sec)
|
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except ValueError:
|
||||
logger.screen.error("Parameter save_model_sec should be an integer.")
|
||||
|
||||
if args.preset is None and (args.agent_type is None or args.environment_type is None
|
||||
or args.exploration_policy_type is None) and not args.play:
|
||||
logger.screen.error('When no preset is given for Coach to run, the user is expected to input the desired agent_type,'
|
||||
' environment_type and exploration_policy_type to assemble a preset. '
|
||||
'\nAt least one of these parameters was not given.')
|
||||
elif args.preset is None and args.play and args.environment_type is None:
|
||||
logger.screen.error('When no preset is given for Coach to run, and the user requests human control over the environment,'
|
||||
' the user is expected to input the desired environment_type and level.'
|
||||
'\nAt least one of these parameters was not given.')
|
||||
elif args.preset is None and args.play and args.environment_type:
|
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args.agent_type = 'Human'
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args.exploration_policy_type = 'ExplorationParameters'
|
||||
|
||||
# get experiment name and path
|
||||
experiment_name = logger.logger.get_experiment_name(args.experiment_name)
|
||||
experiment_path = logger.logger.get_experiment_path(experiment_name)
|
||||
|
||||
if args.play and num_workers > 1:
|
||||
logger.screen.warning("Playing the game as a human is only available with a single worker. "
|
||||
"The number of workers will be reduced to 1")
|
||||
num_workers = 1
|
||||
|
||||
# fill run_dict
|
||||
run_dict = dict()
|
||||
run_dict['agent_type'] = args.agent_type
|
||||
run_dict['environment_type'] = args.environment_type
|
||||
run_dict['exploration_policy_type'] = args.exploration_policy_type
|
||||
run_dict['level'] = args.level
|
||||
run_dict['preset'] = args.preset
|
||||
run_dict['custom_parameter'] = args.custom_parameter
|
||||
run_dict['experiment_path'] = experiment_path
|
||||
run_dict['framework'] = conf.Frameworks().get(args.framework)
|
||||
run_dict['play'] = args.play
|
||||
run_dict['evaluate'] = args.evaluate# or args.play
|
||||
|
||||
# multi-threading parameters
|
||||
run_dict['num_threads'] = num_workers
|
||||
|
||||
# checkpoints
|
||||
run_dict['save_model_sec'] = args.save_model_sec
|
||||
run_dict['save_model_dir'] = experiment_path if args.save_model_sec is not None else None
|
||||
run_dict['checkpoint_restore_dir'] = args.checkpoint_restore_dir
|
||||
|
||||
# visualization
|
||||
run_dict['visualization.dump_gifs'] = args.dump_gifs
|
||||
run_dict['visualization.render'] = args.render
|
||||
run_dict['visualization.tensorboard'] = args.tensorboard
|
||||
|
||||
return args, run_dict
|
||||
|
||||
|
||||
def run_dict_to_json(_run_dict, task_id=''):
|
||||
if task_id != '':
|
||||
json_path = os.path.join(_run_dict['experiment_path'], 'run_dict_worker{}.json'.format(task_id))
|
||||
else:
|
||||
json_path = os.path.join(_run_dict['experiment_path'], 'run_dict.json')
|
||||
|
||||
with open(json_path, 'w') as outfile:
|
||||
json.dump(_run_dict, outfile, indent=2)
|
||||
|
||||
return json_path
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-p', '--preset',
|
||||
help="(string) Name of a preset to run (as configured in presets.py)",
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-l', '--list',
|
||||
help="(flag) List all available presets",
|
||||
action='store_true')
|
||||
parser.add_argument('-e', '--experiment_name',
|
||||
help="(string) Experiment name to be used to store the results.",
|
||||
default='',
|
||||
type=str)
|
||||
parser.add_argument('-r', '--render',
|
||||
help="(flag) Render environment",
|
||||
action='store_true')
|
||||
parser.add_argument('-f', '--framework',
|
||||
help="(string) Neural network framework. Available values: tensorflow, neon",
|
||||
default='tensorflow',
|
||||
type=str)
|
||||
parser.add_argument('-n', '--num_workers',
|
||||
help="(int) Number of workers for multi-process based agents, e.g. A3C",
|
||||
default='1',
|
||||
type=str)
|
||||
parser.add_argument('--play',
|
||||
help="(flag) Play as a human by controlling the game with the keyboard. "
|
||||
"This option will save a replay buffer with the game play.",
|
||||
action='store_true')
|
||||
parser.add_argument('--evaluate',
|
||||
help="(flag) Run evaluation only. This is a convenient way to disable "
|
||||
"training in order to evaluate an existing checkpoint.",
|
||||
action='store_true')
|
||||
parser.add_argument('-v', '--verbose',
|
||||
help="(flag) Don't suppress TensorFlow debug prints.",
|
||||
action='store_true')
|
||||
parser.add_argument('-s', '--save_model_sec',
|
||||
help="(int) Time in seconds between saving checkpoints of the model.",
|
||||
default=None,
|
||||
type=int)
|
||||
parser.add_argument('-crd', '--checkpoint_restore_dir',
|
||||
help='(string) Path to a folder containing a checkpoint to restore the model from.',
|
||||
type=str)
|
||||
parser.add_argument('-dg', '--dump_gifs',
|
||||
help="(flag) Enable the gif saving functionality.",
|
||||
action='store_true')
|
||||
parser.add_argument('-at', '--agent_type',
|
||||
help="(string) Choose an agent type class to override on top of the selected preset. "
|
||||
"If no preset is defined, a preset can be set from the command-line by combining settings "
|
||||
"which are set by using --agent_type, --experiment_type, --environemnt_type",
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-et', '--environment_type',
|
||||
help="(string) Choose an environment type class to override on top of the selected preset."
|
||||
"If no preset is defined, a preset can be set from the command-line by combining settings "
|
||||
"which are set by using --agent_type, --experiment_type, --environemnt_type",
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-ept', '--exploration_policy_type',
|
||||
help="(string) Choose an exploration policy type class to override on top of the selected "
|
||||
"preset."
|
||||
"If no preset is defined, a preset can be set from the command-line by combining settings "
|
||||
"which are set by using --agent_type, --experiment_type, --environemnt_type"
|
||||
,
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-lvl', '--level',
|
||||
help="(string) Choose the level that will be played in the environment that was selected."
|
||||
"This value will override the level parameter in the environment class."
|
||||
,
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('-cp', '--custom_parameter',
|
||||
help="(string) Semicolon separated parameters used to override specific parameters on top of"
|
||||
" the selected preset (or on top of the command-line assembled one). "
|
||||
"Whenever a parameter value is a string, it should be inputted as '\\\"string\\\"'. "
|
||||
"For ex.: "
|
||||
"\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"",
|
||||
default=None,
|
||||
type=str)
|
||||
parser.add_argument('--print_parameters',
|
||||
help="(flag) Print tuning_parameters to stdout",
|
||||
action='store_true')
|
||||
parser.add_argument('-tb', '--tensorboard',
|
||||
help="(flag) When using the TensorFlow backend, enable TensorBoard log dumps. ",
|
||||
action='store_true')
|
||||
parser.add_argument('-ns', '--no_summary',
|
||||
help="(flag) Prevent Coach from printing a summary and asking questions at the end of runs",
|
||||
action='store_true')
|
||||
|
||||
args, run_dict = check_input_and_fill_run_dict(parser)
|
||||
|
||||
# turn TF debug prints off
|
||||
if not args.verbose and args.framework.lower() == 'tensorflow':
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
||||
|
||||
# dump documentation
|
||||
logger.logger.set_dump_dir(run_dict['experiment_path'], add_timestamp=True)
|
||||
if not args.no_summary:
|
||||
atexit.register(logger.logger.summarize_experiment)
|
||||
logger.screen.change_terminal_title(logger.logger.experiment_name)
|
||||
|
||||
# Single-threaded runs
|
||||
if run_dict['num_threads'] == 1:
|
||||
# set tuning parameters
|
||||
json_run_dict_path = run_dict_to_json(run_dict)
|
||||
tuning_parameters = presets.json_to_preset(json_run_dict_path)
|
||||
tuning_parameters.sess = set_framework(args.framework)
|
||||
|
||||
if args.print_parameters:
|
||||
print('tuning_parameters', tuning_parameters)
|
||||
|
||||
# Single-thread runs
|
||||
tuning_parameters.task_index = 0
|
||||
env_instance = environments.create_environment(tuning_parameters)
|
||||
agent = eval('agents.' + tuning_parameters.agent.type +
|
||||
'(env_instance, tuning_parameters)')
|
||||
|
||||
# Start the training or evaluation
|
||||
if tuning_parameters.evaluate:
|
||||
agent.evaluate(sys.maxsize, keep_networks_synced=True) # evaluate forever
|
||||
else:
|
||||
agent.improve()
|
||||
|
||||
# Multi-threaded runs
|
||||
else:
|
||||
assert args.framework.lower() == 'tensorflow', "Distributed training works only with TensorFlow"
|
||||
os.environ["OMP_NUM_THREADS"]="1"
|
||||
# set parameter server and workers addresses
|
||||
ps_hosts = "localhost:{}".format(utils.get_open_port())
|
||||
worker_hosts = ",".join(["localhost:{}".format(utils.get_open_port()) for i in range(run_dict['num_threads'] + 1)])
|
||||
|
||||
# Make sure to disable GPU so that all the workers will use the CPU
|
||||
utils.set_cpu()
|
||||
|
||||
# create a parameter server
|
||||
cmd = [
|
||||
"python3",
|
||||
"./parallel_actor.py",
|
||||
"--ps_hosts={}".format(ps_hosts),
|
||||
"--worker_hosts={}".format(worker_hosts),
|
||||
"--job_name=ps",
|
||||
]
|
||||
parameter_server = subprocess.Popen(cmd)
|
||||
|
||||
logger.screen.log_title("*** Distributed Training ***")
|
||||
time.sleep(1)
|
||||
|
||||
# create N training workers and 1 evaluating worker
|
||||
workers = []
|
||||
|
||||
for i in range(run_dict['num_threads'] + 1):
|
||||
# this is the evaluation worker
|
||||
run_dict['task_id'] = i
|
||||
if i == run_dict['num_threads']:
|
||||
run_dict['evaluate_only'] = True
|
||||
run_dict['visualization.render'] = args.render
|
||||
else:
|
||||
run_dict['evaluate_only'] = False
|
||||
run_dict['visualization.render'] = False # #In a parallel setting, only the evaluation agent renders
|
||||
|
||||
json_run_dict_path = run_dict_to_json(run_dict, i)
|
||||
workers_args = ["python3", "./parallel_actor.py",
|
||||
"--ps_hosts={}".format(ps_hosts),
|
||||
"--worker_hosts={}".format(worker_hosts),
|
||||
"--job_name=worker",
|
||||
"--load_json={}".format(json_run_dict_path)]
|
||||
|
||||
p = subprocess.Popen(workers_args)
|
||||
|
||||
if i != run_dict['num_threads']:
|
||||
workers.append(p)
|
||||
else:
|
||||
evaluation_worker = p
|
||||
|
||||
# wait for all workers
|
||||
[w.wait() for w in workers]
|
||||
evaluation_worker.kill()
|
||||
cli.main()
|
||||
|
||||
Reference in New Issue
Block a user