AzureMLLogger

class health_ml.utils.AzureMLLogger

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

A Pytorch Lightning logger that stores metrics in the current AzureML run. If the present run is not inside AzureML, nothing gets logged.

Methods Summary

experiment()	Return the experiment object associated with this logger.
log_hyperparams(params)	Record hyperparameters.
log_metrics(metrics[, step])	Records metrics.
name()	Return the experiment name.
version()	Return the experiment version.

Methods Documentation

experiment()

Return the experiment object associated with this logger.

Return type

Any

log_hyperparams(params)

Record hyperparameters.

Parameters

• params (Any) – Namespace containing the hyperparameters

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keywoard arguments, depends on the specific logger being used

Return type

None

log_metrics(metrics, step=None)

Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

Parameters

• metrics (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values

• step (Optional[int]) – Step number at which the metrics should be recorded

Return type

None

name()

Return the experiment name.

Return type

Any

version()

Return the experiment version.

Return type

int