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Fix cmd line arguments handling (#68)

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* refactoring the merging of the task parameters and the command line parameters
* removing some unused command line arguments
* fix for saving checkpoints when not passing through coach.py
This commit is contained in:
Itai Caspi
2018-11-07 15:47:02 +02:00
committed by GitHub
parent 93571306c3
commit e7a91b4dc3
4 changed files with 53 additions and 50 deletions
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2
rl_coach/architectures/tensorflow_components/distributed_tf_utils.py
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@@ -94,7 +94,7 @@ def create_monitored_session(target: tf.train.Server, task_index: int,
is_chief=is_chief,
hooks=[],
checkpoint_dir=checkpoint_dir,
checkpoint_save_secs=checkpoint_save_secs,
save_checkpoint_secs=checkpoint_save_secs,
config=config
)

18
rl_coach/base_parameters.py
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@@ -432,14 +432,17 @@ class AgentParameters(Parameters):
class TaskParameters(Parameters):
def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False,
experiment_path='/tmp', seed=None, checkpoint_save_secs=None):
experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
checkpoint_save_dir=None):
"""
:param framework_type: deep learning framework type. currently only tensorflow is supported
:param evaluate_only: the task will be used only for evaluating the model
:param use_cpu: use the cpu for this task
:param experiment_path: the path to the directory which will store all the experiment outputs
:param checkpoint_save_secs: the number of seconds between each checkpoint saving
:param seed: a seed to use for the random numbers generator
:param checkpoint_save_secs: the number of seconds between each checkpoint saving
:param checkpoint_restore_dir: the directory to restore the checkpoints from
:param checkpoint_save_dir: the directory to store the checkpoints in
"""
self.framework_type = framework_type
self.task_index = 0 # TODO: not really needed
@@ -447,6 +450,8 @@ class TaskParameters(Parameters):
self.use_cpu = use_cpu
self.experiment_path = experiment_path
self.checkpoint_save_secs = checkpoint_save_secs
self.checkpoint_restore_dir = checkpoint_restore_dir
self.checkpoint_save_dir = checkpoint_save_dir
self.seed = seed
@@ -454,7 +459,8 @@ class DistributedTaskParameters(TaskParameters):
def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str,
task_index: int, evaluate_only: bool=False, num_tasks: int=None,
num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None,
shared_memory_scratchpad=None, seed=None):
shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
checkpoint_save_dir=None):
"""
:param framework_type: deep learning framework type. currently only tensorflow is supported
:param evaluate_only: the task will be used only for evaluating the model
@@ -469,9 +475,13 @@ class DistributedTaskParameters(TaskParameters):
:param experiment_path: the path to the directory which will store all the experiment outputs
:param dnd: an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad.
:param seed: a seed to use for the random numbers generator
:param checkpoint_save_secs: the number of seconds between each checkpoint saving
:param checkpoint_restore_dir: the directory to restore the checkpoints from
:param checkpoint_save_dir: the directory to store the checkpoints in
"""
super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu,
experiment_path=experiment_path, seed=seed)
experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs,
checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir)
self.parameters_server_hosts = parameters_server_hosts
self.worker_hosts = worker_hosts
self.job_type = job_type

45
rl_coach/coach.py
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@@ -213,10 +213,7 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace:
screen.error("The requested checkpoint folder to load from does not exist.")
# no preset was given. check if the user requested to play some environment on its own
if args.preset is None and args.play:
if args.environment_type:
args.agent_type = 'Human'
else:
if args.preset is None and args.play and not args.environment_type:
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.')
@@ -428,24 +425,8 @@ def main():
parser.add_argument('-dm', '--dump_mp4',
help="(flag) Enable the mp4 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"
,
help="(string) Choose an environment type class to override on top of the selected preset.",
default=None,
type=str)
parser.add_argument('-lvl', '--level',
@@ -546,13 +527,16 @@ def main():
# Single-threaded runs
if args.num_workers == 1:
# Start the training or evaluation
task_parameters = TaskParameters(framework_type=args.framework,
task_parameters = TaskParameters(
framework_type=args.framework,
evaluate_only=args.evaluate,
experiment_path=args.experiment_path,
seed=args.seed,
use_cpu=args.use_cpu,
checkpoint_save_secs=args.checkpoint_save_secs)
task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__)
checkpoint_save_secs=args.checkpoint_save_secs,
checkpoint_restore_dir=args.checkpoint_restore_dir,
checkpoint_save_dir=args.checkpoint_save_dir
)
start_graph(graph_manager=graph_manager, task_parameters=task_parameters)
@@ -575,7 +559,8 @@ def main():
def start_distributed_task(job_type, task_index, evaluation_worker=False,
shared_memory_scratchpad=shared_memory_scratchpad):
task_parameters = DistributedTaskParameters(framework_type=args.framework,
task_parameters = DistributedTaskParameters(
framework_type=args.framework,
parameters_server_hosts=ps_hosts,
worker_hosts=worker_hosts,
job_type=job_type,
@@ -586,8 +571,12 @@ def main():
num_training_tasks=args.num_workers,
experiment_path=args.experiment_path,
shared_memory_scratchpad=shared_memory_scratchpad,
seed=args.seed+task_index if args.seed is not None else None) # each worker gets a different seed
task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__)
seed=args.seed+task_index if args.seed is not None else None, # each worker gets a different seed
checkpoint_save_secs=args.checkpoint_save_secs,
checkpoint_restore_dir=args.checkpoint_restore_dir,
checkpoint_save_dir=args.checkpoint_save_dir
)
# we assume that only the evaluation workers are rendering
graph_manager.visualization_parameters.render = args.render and evaluation_worker
p = Process(target=start_graph, args=(graph_manager, task_parameters))
@@ -607,7 +596,7 @@ def main():
workers.append(start_distributed_task("worker", task_index))
# evaluation worker
if args.evaluation_worker:
if args.evaluation_worker or args.render:
evaluation_worker = start_distributed_task("worker", args.num_workers, evaluation_worker=True)
# wait for all workers

4
rl_coach/graph_managers/graph_manager.py
View File

@@ -100,6 +100,8 @@ class GraphManager(object):
self.preset_validation_params = PresetValidationParameters()
self.reset_required = False
# timers
self.graph_creation_time = None
self.last_checkpoint_saving_time = time.time()
# counters
@@ -520,6 +522,8 @@ class GraphManager(object):
self.save_checkpoint()
def save_checkpoint(self):
if self.task_parameters.checkpoint_save_dir is None:
self.task_parameters.checkpoint_save_dir = os.path.join(self.task_parameters.experiment_path, 'checkpoint')
checkpoint_path = os.path.join(self.task_parameters.checkpoint_save_dir,
"{}_Step-{}.ckpt".format(
self.checkpoint_id,