tfx.components.trainer.executor.Executor

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

Inherits From: GenericExecutor

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 https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_utils.py#L285 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.

Args
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_output: 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
    None

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