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Local generic trainer executor for the TFX Trainer component.
tfx.components.trainer.executor.GenericExecutor( context=None )
The Trainer executor supplements TensorFlow training with a component to enable warm-start training of any user-specified TF model. The Trainer is a library built on top of TensorFlow that is expected to be integrated into a custom user-specified binary.
To include Trainer in a TFX pipeline, configure your pipeline similar to https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_simple.py#L104.
For more details on the Trainer component itself, please refer to https://tensorflow.org/tfx/guide/trainer. For a tutorial on Tensorflow, please refer to https://www.tensorflow.org/tutorials.
How to create a trainer callback function to be used by this Trainer executor: A model training can be executed by TFX by first creating a run_fn callback method that defines, trains an TF Model and saves it to the provided location, This becomes the basis of the Executor for GenericTrainer. This Executor will then execute the run_fn with correct parameters by resolving the input artifacts, output artifacts and execution properties.
Do( input_dict, output_dict, exec_properties )
Uses a user-supplied run_fn to train a TensorFlow model locally.
The Trainer Executor invokes a run_fn callback function provided by the user via the module_file parameter. In this function, user defines the model and train it, then save the model to the provided location.
input_dict: Input dict from input key to a list of ML-Metadata Artifacts.
- examples: Examples used for training, must include 'train' and 'eval' splits.
- transform_output: Optional input transform graph.
- schema: Schema of the data.
output_dict: Output dict from output key to a list of Artifacts.
- output: Exported model.
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. Additional parameters to pass to trainer function.
ValueError: When neither or both of 'module_file' and 'run_fn' are present in 'exec_properties'.
RuntimeError: If run_fn failed to generate model in desired location.