nvflare.app_opt.lightning.loggers.client_logger module

MLflow Logger

class ClientLogger(prefix: str = '')[source]

Bases: Logger

after_save_checkpoint(checkpoint_callback: ModelCheckpoint) → None[source]

Called after model checkpoint callback saves a new checkpoint.

Parameters:: checkpoint_callback – the model checkpoint callback instance

finalize(status: str = 'success') → None[source]

Do any processing that is necessary to finalize an experiment.

Parameters:: status – Status that the experiment finished with (e.g. success, failed, aborted)

log_hyperparams(params: dict[str, Any] | Namespace) → None[source]

Record hyperparameters.

Parameters:

params – Namespace or Dict containing the hyperparameters
args – Optional positional arguments, depends on the specific logger being used
kwargs – Optional keyword arguments, depends on the specific logger being used

log_metrics(metrics: Mapping[str, float], step: int | None = None) → None[source]

Records metrics. This method logs metrics as soon as it received them.

Parameters:

metrics – Dictionary with metric names as keys and measured quantities as values
step – Step number at which the metrics should be recorded

property name: str | None

Get the experiment id.

property save_dir: str | None: Return the root directory where experiment logs get saved, or None if the logger does not save data locally.