ray.tune.integration.mxnet.TuneReportCallback#

class ray.tune.integration.mxnet.TuneReportCallback(metrics: Optional[Union[str, List[str], Dict[str, str]]] = None)[source]#

Bases: ray.tune.integration.mxnet.TuneCallback

MXNet to Ray Tune reporting callback

Reports metrics to Ray Tune.

This has to be passed to MXNet as the eval_end_callback.

Parameters

metrics – Metrics to report to Tune. If this is a list, each item describes the metric key reported to MXNet, and it will reported under the same name to Tune. If this is a dict, each key will be the name reported to Tune and the respective value will be the metric key reported to MXNet.

Example:

from ray.tune.integration.mxnet import TuneReportCallback

# mlp_model is a MXNet model
mlp_model.fit(
    train_iter,
    # ...
    eval_metric="acc",
    eval_end_callback=TuneReportCallback({
        "mean_accuracy": "accuracy"
    }))