o $i @svddlmZddlZddlmZddlmZddlm Z dZ GdddZ Gd d d Z e d d Gd ddeZ dS))DictN)TensorflowCheckpoint)RayReportCallback) PublicAPIzxThe `ray.tune.integration.keras` module is deprecated in favor of `ray.train.tensorflow.keras.ReportCheckpointCallback`.c@eZdZdZddZdS)TuneReportCallbackYDeprecated. Use :class:`ray.train.tensorflow.keras.ReportCheckpointCallback` instead.cOttNDeprecationWarning_DEPRECATION_MESSAGEclsargskwargsrW/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/ray/tune/integration/keras.py__new__zTuneReportCallback.__new__N__name__ __module__ __qualname____doc__rrrrrr rc@r)_TuneCheckpointCallbackrcOr r r rrrrrrz_TuneCheckpointCallback.__new__Nrrrrrrrralpha) stabilityc@s0eZdZdZdedefddZdefddZdS) TuneReportCheckpointCallbackaWKeras callback for Ray Tune reporting and checkpointing. .. note:: Metrics are always reported with checkpoints, even if the event isn't specified in ``report_metrics_on``. Example: .. code-block:: python ############# Using it in Ray Tune ############### from ray.tune.integrations.keras import TuneReportCheckpointCallback def train_fn(): model = build_model() model.fit(dataset_shard, callbacks=[TuneReportCheckpointCallback()]) tuner = tune.Tuner(train_fn) results = tuner.fit() Args: metrics: Metrics to report. If this is a list, each item describes the metric key reported to Keras, and it's reported under the same name. If this is a dict, each key is the name reported and the respective value is the metric key reported to Keras. If this is None, all Keras logs are reported. report_metrics_on: When to report metrics. Must be one of the Keras event hooks (less the ``on_``), e.g. "train_start" or "predict_end". Defaults to "epoch_end". checkpoint_on: When to save checkpoints. Must be one of the Keras event hooks (less the ``on_``), e.g. "train_start" or "predict_end". Defaults to "epoch_end". metrics checkpointcCstjj||ddS)N)r!raytunereport)selfr r!rrr_save_and_report_checkpointBsz8TuneReportCheckpointCallback._save_and_report_checkpointcCstj|dSr r")r&r rrr_report_metricsGsz,TuneReportCheckpointCallback._report_metricsN)rrrrrrr'r(rrrrrs" r)typingrray.tuner#ray.train.tensorflowrray.train.tensorflow.kerasrray.util.annotationsrr rrrrrrrs