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Local estimator based trainer executor used by the TFX Trainer component.

Inherits From: GenericExecutor, BaseExecutor

How to create a trainer callback function to be used by this Trainer executor: An estimator can be executed by TFX by first creating a trainer_fn callback method that returns an estimator and some additional parameters, similar to This becomes the basis of the new Executor for Trainer. This Executor will then train and evaluate this estimator using the tf.estimator.train_and_evaluate API to train locally.

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Uses a user-supplied tf.estimator to train a TensorFlow model locally.

The Trainer Executor invokes a training_fn callback function provided by the user via the module_file parameter. With the tf.estimator returned by this function, the Trainer Executor then builds a TensorFlow model using the user-provided tf.estimator.

input_dict Input dict from input key to a list of ML-Metadata Artifacts.

  • examples: Examples used for training, must include 'train' and 'eval' if custom splits is not specified in train_args and eval_args.
  • transform_graph: Optional input transform graph.
  • schema: Schema of the data.
output_dict Output dict from output key to a list of Artifacts.
  • model: Exported model.
  • model_run: Model training related outputs (e.g., Tensorboard logs)
  • exec_properties A dict of execution properties.
  • train_args: JSON string of trainer_pb2.TrainArgs instance, providing args for training.
  • eval_args: JSON string of trainer_pb2.EvalArgs instance, providing args for eval.
  • module_file: Python module file containing UDF model definition.
  • warm_starting: Whether or not we need to do warm starting.
  • warm_start_from: Optional. If warm_starting is True, this is the directory to find previous model to warm start on.
  • custom_config: Optional. JSON-serialized dict of additional parameters to pass to trainer function.
  • Returns

    ValueError When neither or both of 'module_file' and 'trainer_fn' are present in 'exec_properties'.