def test_convert_params():
"""Test conversion of params to a dict."""
# Test normal dict, make sure it is unchanged
params = {"foo": "bar", 1: 23}
assert type(params) == dict
params = _convert_params(params)
assert type(params) == dict
assert params["foo"] == "bar"
assert params[1] == 23
# Test None conversion
params = None
assert type(params) != dict
params = _convert_params(params)
assert type(params) == dict
assert params == {}
# Test conversion of argparse Namespace
opt = "--max_epochs 1".split(" ")
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parent_parser=parser)
params = parser.parse_args(opt)
assert type(params) == Namespace
params = _convert_params(params)
assert type(params) == dict
assert params["gpus"] is None
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
params = _convert_params(params)
params = _flatten_dict(params)
self.experiment.argparse(Namespace(**params))
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:
params = _convert_params(params)
params = _flatten_dict(params)
for k, v in params.items():
if len(str(v)) > 250:
rank_zero_warn(
f"Mlflow only allows parameters with up to 250 characters. Discard {k}={v}",
category=RuntimeWarning)
continue
self.experiment.log_param(self.run_id, k, v)
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],
metrics: Optional[Dict[str, Any]] = None) -> None:
"""Record hyperparameters. TensorBoard logs with and without saved hyperparameters are incompatible, the
hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs
to display the new ones with hyperparameters.
Args:
params: a dictionary-like container with the hyperparameters
metrics: Dictionary with metric names as keys and measured quantities as values
"""
params = _convert_params(params)
# store params to output
if _OMEGACONF_AVAILABLE and isinstance(params, Container):
self.hparams = OmegaConf.merge(self.hparams, params)
else:
self.hparams.update(params)
# format params into the suitable for tensorboard
params = _flatten_dict(params)
params = self._sanitize_params(params)
if metrics is None:
if self._default_hp_metric:
metrics = {"hp_metric": -1}
elif not isinstance(metrics, dict):
metrics = {"hp_metric": metrics}
if metrics:
self.log_metrics(metrics, 0)
exp, ssi, sei = hparams(params, metrics)
writer = self.experiment._get_file_writer()
writer.add_summary(exp)
writer.add_summary(ssi)
writer.add_summary(sei)
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = _convert_params(params)
self.experiment.log_hparams(params)
def log_hyperparams(self, params):
params = _convert_params(params)
params = _sanitize_params(params)
self.logged_params = 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)
