tfm.core.train_lib.run_experiment

Runs train/eval configured by the experiment params.

tfm.core.train_lib.run_experiment(
    distribution_strategy: tf.distribute.Strategy,
    task: tfm.core.base_task.Task,
    mode: str,
    params: tfm.core.base_trainer.ExperimentConfig,
    model_dir: str,
    run_post_eval: bool = False,
    save_summary: bool = True,
    train_actions: Optional[List[orbit.Action]] = None,
    eval_actions: Optional[List[orbit.Action]] = None,
    trainer: Optional[tfm.core.base_trainer.Trainer] = None,
    controller_cls=orbit.Controller,
    summary_manager: Optional[orbit.utils.SummaryManager] = None,
    eval_summary_manager: Optional[orbit.utils.SummaryManager] = None,
    enable_async_checkpointing: bool = False
) -> Tuple[tf.keras.Model, Mapping[str, Any]]

Args
distribution_strategy	A distribution distribution_strategy.
task	A Task instance.
mode	A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval' or 'continuous_eval'.
params	ExperimentConfig instance.
model_dir	A 'str', a path to store model checkpoints and summaries.
run_post_eval	Whether to run post eval once after training, metrics logs are returned.
save_summary	Whether to save train and validation summary.
train_actions	Optional list of Orbit train actions.
eval_actions	Optional list of Orbit eval actions.
trainer	the base_trainer.Trainer instance. It should be created within the strategy.scope().
controller_cls	The controller class to manage the train and eval process. Must be a orbit.Controller subclass.
summary_manager	Instance of the summary manager to override default summary manager.
eval_summary_manager	Instance of the eval summary manager to override default eval summary manager.
enable_async_checkpointing	Optional boolean indicating whether to enable async checkpoint saving.

Returns
A 2-tuple of (model, eval_logs). model: tf.keras.Model instance. eval_logs: returns eval metrics logs when run_post_eval is set to True, otherwise, returns {}.