def parse_args():
from docopt import docopt
args = docopt(__doc__)
args = {k.strip("--").replace("-", "_"): v for k, v in args.items()}
del args["h"]
del args["help"]
args = DotDict(args)
args.nb_node = int(args.nb_node)
args.node_rank = int(args.node_rank)
args.nb_proc = int(args.nb_proc)
args.master_port = int(args.master_port)
if args.resume:
args.resume = parse_path(args.resume)
return args
if args.config:
args.config = parse_path(args.config)
args.logdir = parse_path(args.logdir)
args.nb_env = int(args.nb_env)
args.seed = int(args.seed)
args.nb_step = int(float(args.nb_step))
args.tag = parse_none(args.tag)
args.nb_eval_env = int(args.nb_eval_env)
args.summary_freq = int(args.summary_freq)
args.lr = float(args.lr)
args.epoch_len = int(float(args.epoch_len))
args.profile = bool(args.profile)
return args
def from_resume(mode, args):
"""
:param mode: Script name
:param args: Dict[str, Any], static args
:return: args, log_id, initial_step_count
"""
resume = args.resume
log_dir_helper = LogDirHelper(args.resume)
with open(log_dir_helper.args_file_path(), "r") as args_file:
args = DotDict(json.load(args_file))
args.resume = resume
args.load_network = log_dir_helper.latest_network_path()
args.load_optim = log_dir_helper.latest_optim_path()
initial_step_count = log_dir_helper.latest_epoch()
if args.agent:
name = args.agent
else:
name = args.actor_host
log_id = Init.make_log_id(
args.tag,
mode,
name,
args.netbody,
timestamp=log_dir_helper.timestamp(),
)
log_id_path = Init.log_id_dir(args.logdir, args.env, log_id)
return args, log_id_path, initial_step_count
def parse_args():
from docopt import docopt
args = docopt(__doc__)
args = {k.strip('--').replace('-', '_'): v for k, v in args.items()}
del args['h']
del args['help']
args = DotDict(args)
# Ignore other args if resuming
if args.resume:
args.resume = parse_path(args.resume)
return args
if args.config:
args.config = parse_path(args.config)
args.logdir = parse_path(args.logdir)
args.gpu_id = int(args.gpu_id)
args.nb_env = int(args.nb_env)
args.seed = int(args.seed)
args.nb_step = int(float(args.nb_step))
args.tag = parse_none(args.tag)
args.nb_eval_env = int(args.nb_eval_env)
args.summary_freq = int(args.summary_freq)
args.lr = float(args.lr)
args.warmup = int(float(args.warmup))
args.epoch_len = int(float(args.epoch_len))
args.profile = bool(args.profile)
return args
def parse_args():
from docopt import docopt
args = docopt(__doc__)
args = {k.strip('--').replace('-', '_'): v for k, v in args.items()}
del args['h']
del args['help']
args = DotDict(args)
# Ignore other args if resuming
if args.resume:
args.resume = parse_path(args.resume)
return args
if args.config:
args.config = parse_path(args.config)
args.logdir = parse_path(args.logdir)
args.nb_env = int(args.nb_env)
args.seed = int(args.seed)
args.nb_step = int(float(args.nb_step))
args.tag = parse_none(args.tag)
args.summary_freq = int(args.summary_freq)
args.lr = float(args.lr)
args.epoch_len = int(float(args.epoch_len))
args.profile = bool(args.profile)
args.ray_addr = parse_none(args.ray_addr)
args.nb_learners = int(args.nb_learners)
args.nb_workers = int(args.nb_workers)
args.learner_cpu_alloc = int(args.learner_cpu_alloc)
args.learner_gpu_alloc = float(args.learner_gpu_alloc)
args.worker_cpu_alloc = int(args.worker_cpu_alloc)
args.worker_gpu_alloc = float(args.worker_gpu_alloc)
args.nb_learn_batch = int(args.nb_learn_batch)
args.rollout_queue_size = int(args.rollout_queue_size)
# arg checking
assert args.nb_learn_batch <= args.nb_workers, 'WARNING: nb_learn_batch must be <= nb_workers. Got {} <= {}' \
.format(args.nb_learn_batch, args.nb_workers)
return args
