def main() -> None:
args = parse_args()
print(
f"Converting {args.trainer_config_path} and saving to {args.output_config_path}."
)
old_config = load_config(args.trainer_config_path)
behavior_config_dict = convert_behaviors(old_config)
full_config = {"behaviors": behavior_config_dict}
# Convert curriculum and sampler. note that we don't validate these; if it was correct
# before it should be correct now.
if args.curriculum is not None:
curriculum_config_dict = load_config(args.curriculum)
full_config["curriculum"] = curriculum_config_dict
if args.sampler is not None:
old_sampler_config_dict = load_config(args.sampler)
sampler_config_dict = convert_samplers(old_sampler_config_dict)
full_config["parameter_randomization"] = sampler_config_dict
# Convert config to dict
unstructed_config = cattr.unstructure(full_config)
unstructed_config = remove_nones(unstructed_config)
write_to_yaml_file(unstructed_config, args.output_config_path)
def main() -> None:
args = parse_args()
print(
f"Converting {args.trainer_config_path} and saving to {args.output_config_path}."
)
old_config = load_config(args.trainer_config_path)
curriculum_config_dict = None
old_sampler_config_dict = None
if args.curriculum is not None:
curriculum_config_dict = load_config(args.curriculum)
if args.sampler is not None:
old_sampler_config_dict = load_config(args.sampler)
new_config = convert(old_config, curriculum_config_dict, old_sampler_config_dict)
unstructed_config = remove_nones(new_config)
write_to_yaml_file(unstructed_config, args.output_config_path)
def from_argparse(args: argparse.Namespace) -> "RunOptions":
"""
Takes an argparse.Namespace as specified in `parse_command_line`, loads input configuration files
from file paths, and converts to a RunOptions instance.
:param args: collection of command-line parameters passed to mlagents-learn
:return: RunOptions representing the passed in arguments, with trainer config, curriculum and sampler
configs loaded from files.
"""
argparse_args = vars(args)
config_path = StoreConfigFile.trainer_config_path
# Load YAML
configured_dict: Dict[str, Any] = {
"checkpoint_settings": {},
"env_settings": {},
"engine_settings": {},
"torch_settings": {},
}
_require_all_behaviors = True
if config_path is not None:
configured_dict.update(load_config(config_path))
else:
# If we're not loading from a file, we don't require all behavior names to be specified.
_require_all_behaviors = False
# Use the YAML file values for all values not specified in the CLI.
for key in configured_dict.keys():
# Detect bad config options
if key not in attr.fields_dict(RunOptions):
raise TrainerConfigError(
"The option {} was specified in your YAML file, but is invalid.".format(
key
)
)
# Override with CLI args
# Keep deprecated --load working, TODO: remove
argparse_args["resume"] = argparse_args["resume"] or argparse_args["load_model"]
for key, val in argparse_args.items():
if key in DetectDefault.non_default_args:
if key in attr.fields_dict(CheckpointSettings):
configured_dict["checkpoint_settings"][key] = val
elif key in attr.fields_dict(EnvironmentSettings):
configured_dict["env_settings"][key] = val
elif key in attr.fields_dict(EngineSettings):
configured_dict["engine_settings"][key] = val
elif key in attr.fields_dict(TorchSettings):
configured_dict["torch_settings"][key] = val
else: # Base options
configured_dict[key] = val
final_runoptions = RunOptions.from_dict(configured_dict)
final_runoptions.checkpoint_settings.prioritize_resume_init()
# Need check to bypass type checking but keep structure on dict working
if isinstance(final_runoptions.behaviors, TrainerSettings.DefaultTrainerDict):
# configure whether or not we should require all behavior names to be found in the config YAML
final_runoptions.behaviors.set_config_specified(_require_all_behaviors)
return final_runoptions
def main():
"""
Provides an alternative CLI interface to mlagents-learn, 'mlagents-run-experiment'.
Accepts a JSON/YAML formatted mlagents.trainers.learn.RunOptions object, and executes
the run loop as defined in mlagents.trainers.learn.run_cli.
"""
args = parse_command_line()
expt_config = load_config(args.experiment_config_path)
run_cli(RunOptions.from_dict(expt_config))
def from_argparse(args: argparse.Namespace) -> "RunOptions":
"""
Takes an argparse.Namespace as specified in `parse_command_line`, loads input configuration files
from file paths, and converts to a RunOptions instance.
:param args: collection of command-line parameters passed to mlagents-learn
:return: RunOptions representing the passed in arguments, with trainer config, curriculum and sampler
configs loaded from files.
"""
argparse_args = vars(args)
config_path = StoreConfigFile.trainer_config_path
# Load YAML
configured_dict: Dict[str, Any] = {
"checkpoint_settings": {},
"env_settings": {},
"engine_settings": {},
"torch_settings": {},
}
if config_path is not None:
configured_dict.update(load_config(config_path))
# Use the YAML file values for all values not specified in the CLI.
for key in configured_dict.keys():
# Detect bad config options
if key not in attr.fields_dict(RunOptions):
raise TrainerConfigError(
"The option {} was specified in your YAML file, but is invalid.".format(
key
)
)
# Override with CLI args
# Keep deprecated --load working, TODO: remove
argparse_args["resume"] = argparse_args["resume"] or argparse_args["load_model"]
for key, val in argparse_args.items():
if key in DetectDefault.non_default_args:
if key in attr.fields_dict(CheckpointSettings):
configured_dict["checkpoint_settings"][key] = val
elif key in attr.fields_dict(EnvironmentSettings):
configured_dict["env_settings"][key] = val
elif key in attr.fields_dict(EngineSettings):
configured_dict["engine_settings"][key] = val
elif key in attr.fields_dict(TorchSettings):
configured_dict["torch_settings"][key] = val
else: # Base options
configured_dict[key] = val
final_runoptions = RunOptions.from_dict(configured_dict)
return final_runoptions
def test_load_config_missing_file():
with pytest.raises(TrainerConfigError):
load_config("thisFileDefinitelyDoesNotExist.yaml")
default=None,
)
argparser.add_argument(
"--sampler",
help=
"Path to old format (<=0.16.X) parameter randomization configuration YAML.",
default=None,
)
argparser.add_argument("output_config_path",
help="Path to write converted YAML file.")
args = argparser.parse_args()
print(
f"Converting {args.trainer_config_path} and saving to {args.output_config_path}."
)
old_config = load_config(args.trainer_config_path)
behavior_config_dict = convert_behaviors(old_config)
full_config = {"behaviors": behavior_config_dict}
# Convert curriculum and sampler. note that we don't validate these; if it was correct
# before it should be correct now.
if args.curriculum is not None:
curriculum_config_dict = load_config(args.curriculum)
full_config["curriculum"] = curriculum_config_dict
if args.sampler is not None:
sampler_config_dict = load_config(args.curriculum)
full_config["parameter_randomization"] = sampler_config_dict
write_to_yaml_file(full_config, args.output_config_path)
