ray.air.Result

class ray.air.Result(metrics: Optional[Dict[str, Any]], checkpoint: Optional[ray.air.checkpoint.Checkpoint], error: Optional[Exception], log_dir: Optional[pathlib.Path], metrics_dataframe: Optional[pd.DataFrame], best_checkpoints: Optional[List[Tuple[ray.air.checkpoint.Checkpoint, Dict[str, Any]]]])

Bases: object

The final result of a ML training run or a Tune trial.

This is the class produced by Trainer.fit(). It contains a checkpoint, which can be used for resuming training and for creating a Predictor object. It also contains a metrics object describing training metrics. error is included so that non successful runs and trials can be represented as well.

The constructor is a private API.

metrics

The final metrics as reported by an Trainable.

Type

Optional[Dict[str, Any]]

checkpoint

The final checkpoint of the Trainable.

Type

Optional[ray.air.checkpoint.Checkpoint]

error

The execution error of the Trainable run, if the trial finishes in error.

Type

Optional[Exception]

log_dir

Directory where the trial logs are saved.

Type

Optional[pathlib.Path]

metrics_dataframe

The full result dataframe of the Trainable. The dataframe is indexed by iterations and contains reported metrics.

Type

Optional[pd.DataFrame]

best_checkpoints

A list of tuples of the best checkpoints saved by the Trainable and their associated metrics. The number of saved checkpoints is determined by the checkpoint_config argument of run_config (by default, all checkpoints will be saved).

Type

Optional[List[Tuple[ray.air.checkpoint.Checkpoint, Dict[str, Any]]]]

PublicAPI (beta): This API is in beta and may change before becoming stable.

property config: Optional[Dict[str, Any]]

The config associated with the result.