def start():
r"""
Starts the experiment.
Run it using ``with`` statement and it will monitor and report, experiment completion
and exceptions.
"""
global _load_run_uuid
global _load_checkpoint
return _experiment_singleton().start(run_uuid=_load_run_uuid, checkpoint=_load_checkpoint)
def save_numpy(name: str, array: np.ndarray):
r"""
Saves a single numpy array. This is used to save processed data.
"""
numpy_path = Path(_experiment_singleton().run.numpy_path)
if not numpy_path.exists():
numpy_path.mkdir(parents=True)
file_name = name + ".npy"
np.save(str(numpy_path / file_name), array)
def get_uuid():
r"""
Returns the UUID of the current experiment run
"""
return _experiment_singleton().run.uuid
def save_checkpoint():
r"""
Saves model checkpoints
"""
_experiment_singleton().save_checkpoint()
def add_model_savers(savers: Dict[str, ModelSaver]):
_experiment_singleton().checkpoint_saver.add_savers(savers)
def distributed(rank: int, world_size: int):
_experiment_singleton().distributed(rank, world_size)
def start():
r"""
Starts the experiment.
"""
_experiment_singleton().start()
def calculate_configs(*args):
r"""
Calculate configurations
This has multiple overloads
.. function:: calculate_configs(configs_dict: Dict[str, any])
:noindex:
.. function:: calculate_configs(configs_dict: Dict[str, any], configs_override: Dict[str, any])
:noindex:
.. function:: calculate_configs(configs: BaseConfigs)
:noindex:
.. function:: calculate_configs(configs: BaseConfigs, run_order: List[Union[List[str], str]])
:noindex:
.. function:: calculate_configs(configs: BaseConfigs, *run_order: str)
:noindex:
.. function:: calculate_configs(configs: BaseConfigs, configs_override: Dict[str, any])
:noindex:
.. function:: calculate_configs(configs: BaseConfigs, configs_override: Dict[str, any], run_order: List[Union[List[str], str]])
:noindex:
.. function:: calculate_configs(configs: BaseConfigs, configs_override: Dict[str, any], *run_order: str)
:noindex:
Arguments:
configs (BaseConfigs, optional): configurations object
configs_dict (Dict[str, any], optional): a dictionary of configs
configs_override (Dict[str, any], optional): a dictionary of
configs to be overridden
run_order (List[Union[str, List[str]]], optional): list of
configs to be calculated and the order in which they should be
calculated. If not provided all configs will be calculated.
"""
configs_override: Optional[Dict[str, any]] = None
run_order: Optional[List[Union[List[str], str]]] = None
idx = 1
if isinstance(args[0], BaseConfigs):
if idx < len(args) and isinstance(args[idx], dict):
configs_override = args[idx]
idx += 1
if idx < len(args) and isinstance(args[idx], list):
run_order = args[idx]
if len(args) != idx + 1:
raise RuntimeError("Invalid call to calculate configs")
_experiment_singleton().calc_configs(args[0], configs_override,
run_order)
else:
if idx == len(args):
_experiment_singleton().calc_configs(args[0], configs_override,
run_order)
else:
run_order = list(args[idx:])
for key in run_order:
if not isinstance(key, str):
raise RuntimeError("Invalid call to calculate configs")
_experiment_singleton().calc_configs(args[0], configs_override,
run_order)
elif isinstance(args[0], dict):
if idx < len(args) and isinstance(args[idx], dict):
configs_override = args[idx]
idx += 1
if idx != len(args):
raise RuntimeError("Invalid call to calculate configs")
_experiment_singleton().calc_configs_dict(args[0], configs_override)
else:
raise RuntimeError("Invalid call to calculate configs")
