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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
331 lines
14 KiB
Python
331 lines
14 KiB
Python
#
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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 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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import agents
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import argparse
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import configurations as conf
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import environments
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import logger
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import presets
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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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if __name__ == "__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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