def test_sanitize_callable_params():
"""Callback function are not serializiable.
Therefore, we get them a chance to return something and if the returned type is not accepted, return None.
"""
opt = "--max_epochs 1".split(" ")
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parent_parser=parser)
params = parser.parse_args(opt)
def return_something():
return "something"
params.something = return_something
def wrapper_something():
return return_something
params.wrapper_something_wo_name = lambda: lambda: "1"
params.wrapper_something = wrapper_something
params = _convert_params(params)
params = _flatten_dict(params)
params = _sanitize_callable_params(params)
assert params["gpus"] == "None"
assert params["something"] == "something"
assert params["wrapper_something"] == "wrapper_something"
assert params["wrapper_something_wo_name"] == "<lambda>"
def log_hyperparams(
self, params: Union[Dict[str, Any],
Namespace]) -> None: # skipcq: PYL-W0221
r"""
Log hyper-parameters to the run.
Hyperparams will be logged under the "<prefix>/hyperparams" namespace.
Note:
You can also log parameters by directly using the logger instance:
``neptune_logger.experiment["model/hyper-parameters"] = params_dict``.
In this way you can keep hierarchical structure of the parameters.
Args:
params: `dict`.
Python dictionary structure with parameters.
Example::
from pytorch_lightning.loggers import NeptuneLogger
PARAMS = {
"batch_size": 64,
"lr": 0.07,
"decay_factor": 0.97
}
neptune_logger = NeptuneLogger(
api_key="ANONYMOUS",
project="common/pytorch-lightning-integration"
)
neptune_logger.log_hyperparams(PARAMS)
"""
params = _convert_params(params)
params = _sanitize_callable_params(params)
parameters_key = self.PARAMETERS_KEY
parameters_key = self._construct_path_with_prefix(parameters_key)
self.run[parameters_key] = params
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = _convert_params(params)
params = _flatten_dict(params)
params = _sanitize_callable_params(params)
self.experiment.config.update(params, allow_val_change=True)