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rllib_driver.py
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# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
import yaml
import ray
from ray import tune
from ray.tune.registry import register_env
def arg_parser():
parser = argparse.ArgumentParser()
''' Specification file of the expriment '''
parser.add_argument("--spec", required=True, type=str)
''' Mode for running an experiment '''
parser.add_argument("--mode", required=True, choices=['train', 'load'])
''' '''
parser.add_argument("--checkpoint", type=str, default=None)
''' '''
parser.add_argument("--num_workers", type=int, default=None)
''' '''
parser.add_argument("--num_cpus", type=int, default=1)
''' '''
parser.add_argument("--num_gpus", type=int, default=0)
''' '''
parser.add_argument("--num_envs_per_worker", type=int, default=None)
''' '''
parser.add_argument("--num_cpus_per_worker", type=int, default=None)
''' '''
parser.add_argument("--num_gpus_per_worker", type=int, default=None)
''' Directory where the environment and related files are stored '''
parser.add_argument("--project_dir", type=str, default=None)
''' Directory where intermediate results are saved '''
parser.add_argument("--local_dir", type=str, default=None)
''' Verbose '''
parser.add_argument("--verbose", action='store_true')
''' '''
parser.add_argument("--ip_head", type=str, default=None)
''' '''
parser.add_argument("--password", type=str, default=None)
return parser
if __name__ == "__main__":
args = arg_parser().parse_args()
with open(args.spec) as f:
spec = yaml.load(f, Loader=yaml.FullLoader)
config = spec['config']
'''
Register environment to learn according to the input specification file
'''
if config['env'] == "HumanoidImitation":
import rllib_env_imitation as env_module
else:
raise NotImplementedError("Unknown Environment")
register_env(config['env'], lambda config: env_module.env_cls(config))
'''
Register custom model to use if it exists
'''
framework = config.get('framework')
if config.get('model'):
custom_model = config.get('model').get('custom_model')
if custom_model:
if framework=='torch':
import rllib_model_custom_torch
else:
raise NotImplementedError("Tensorflow is not supported!")
'''
Validate configurations and overide values by arguments
'''
if args.local_dir is not None:
spec.update({'local_dir': args.local_dir})
if args.project_dir is not None:
assert os.path.exists(args.project_dir)
config['env_config']['project_dir'] = args.project_dir
if config['model'].get('custom_model_config'):
config['model']['custom_model_config'].update(
{'project_dir': config['env_config']['project_dir']})
if args.verbose:
config['env_config'].update({'verbose': args.verbose})
if args.checkpoint is not None:
assert os.path.exists(args.checkpoint)
if args.num_workers is not None:
config.update({'num_workers': args.num_workers})
if args.num_gpus is not None:
config.update({'num_gpus': args.num_gpus})
if args.num_envs_per_worker:
config.update({'num_envs_per_worker': args.num_envs_per_worker})
if args.num_cpus_per_worker:
config.update({'num_cpus_per_worker': args.num_cpus_per_worker})
if args.num_gpus_per_worker:
config.update({'num_gpus_per_worker': args.num_gpus_per_worker})
if args.mode == "train":
if not os.path.exists(spec['local_dir']):
raise Exception(
"The directory does not exist: %s"%spec['local_dir'])
config_override = env_module.config_override(spec)
config.update(config_override)
if args.ip_head:
# tmp_dir = os.path.join(spec['local_dir'], os.path.join('tmp/', spec['name']))
if args.password:
ray.init(address=args.ip_head, redis_password=args.password)
else:
ray.init(address=args.ip_head)
else:
assert args.num_cpus is not None
assert args.num_gpus is not None
ray.init(num_cpus=args.num_cpus, num_gpus=args.num_gpus)
def adjust_config_for_loading(config, alg):
config["num_workers"] = 1
config['num_envs_per_worker'] = 1
config['num_cpus_per_worker'] = 1
config['num_gpus_per_worker'] = 0
config['remote_worker_envs'] = False
def adjust_config(config, alg):
rollout_fragment_length = config.get('rollout_fragment_length')
num_workers = config.get('num_workers')
num_envs_per_worker = config.get('num_envs_per_worker')
train_batch_size = config.get('train_batch_size')
'''
Set rollout_fragment_length value so that
workers can genertate train_batch_size tuples correctly
'''
rollout_fragment_length = \
max(train_batch_size // (num_workers * num_envs_per_worker), 100)
while rollout_fragment_length * num_workers * num_envs_per_worker \
< train_batch_size:
rollout_fragment_length += 1
config['rollout_fragment_length'] = rollout_fragment_length
adjust_config(config, spec['run'])
if args.mode == "load":
adjust_config_for_loading(config, spec['run'])
if spec["run"] == "PPO":
from ray.rllib.agents.ppo import PPOTrainer as Trainer
else:
raise NotImplementedError("Not a supported algorithm")
trainer = Trainer(env=env_module.env_cls, config=config)
if args.checkpoint is not None:
trainer.restore(args.checkpoint)
env_module.rm.initialize()
env = env_module.env_cls(config['env_config'])
cam = env_module.default_cam()
renderer = env_module.EnvRenderer(trainer=trainer, env=env, cam=cam)
renderer.run()
else:
tune.run(
spec['run'],
name=spec['name'],
stop=spec['stop'],
local_dir=spec['local_dir'],
checkpoint_freq=spec['checkpoint_freq'],
checkpoint_at_end=spec['checkpoint_at_end'],
config=config,
restore=args.checkpoint,
sync_to_driver=False,
)