tfx.components.Trainer

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Class Trainer

A TFX component to train a TensorFlow model.

Inherits From: BaseComponent

Aliases: tfx.components.trainer.component.Trainer

Used in the tutorials:

The Trainer component is used to train and eval a model using given inputs and a user-supplied estimator. This component includes a custom driver to optionally grab previous model to warm start from.

Providing an estimator

The TFX executor will use the estimator provided in the module_file file to train the model. The Trainer executor will look specifically for the trainer_fn() function within that file. Before training, the executor will call that function expecting the following returned as a dictionary:

  • estimator: The estimator to be used by TensorFlow to train the model.
  • train_spec: The configuration to be used by the "train" part of the TensorFlow train_and_evaluate() call.
  • eval_spec: The configuration to be used by the "eval" part of the TensorFlow train_and_evaluate() call.
  • eval_input_receiver_fn: The configuration to be used by the ModelValidator component when validating the model.

An example of trainer_fn() can be found in the user-supplied code of the TFX Chicago Taxi pipeline example.

Note: The default executor for this component trains locally. This can be overriden to enable the model to be trained on other platforms. The Cloud AI Platform custom executor provides an example how to implement this.

Please see https://www.tensorflow.org/guide/estimators for more details.

Example 1: Training locally

# Uses user-provided Python function that implements a model using TF-Learn.
trainer = Trainer(
    module_file=module_file,
    transformed_examples=transform.outputs['transformed_examples'],
    schema=infer_schema.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000))

Example 2: Training through a cloud provider

# Train using Google Cloud AI Platform.
trainer = Trainer(
    executor_class=ai_platform_trainer_executor.Executor,
    module_file=module_file,
    transformed_examples=transform.outputs['transformed_examples'],
    schema=infer_schema.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000))

__init__

View source

__init__(
    examples=None,
    transformed_examples=None,
    transform_graph=None,
    schema=None,
    module_file=None,
    trainer_fn=None,
    train_args=None,
    eval_args=None,
    custom_config=None,
    custom_executor_spec=None,
    output=None,
    transform_output=None,
    instance_name=None
)

Construct a Trainer component.

Args:

  • examples: A Channel of 'ExamplesPath' type, serving as the source of examples that are used in training (required). May be raw or transformed.
  • transformed_examples: Deprecated field. Please set 'examples' instead.
  • transform_graph: An optional Channel of 'TransformPath' type, serving as the input transform graph if present.
  • schema: A Channel of 'SchemaPath' type, serving as the schema of training and eval data.

  • module_file: A path to python module file containing UDF model definition. The module_file must implement a function named trainer_fn at its top level. The function must have the following signature.

    def trainer_fn(tf.contrib.training.HParams, tensorflow_metadata.proto.v0.schema_pb2) -> Dict: ...

    where the returned Dict has the following key-values. 'estimator': an instance of tf.estimator.Estimator 'train_spec': an instance of tf.estimator.TrainSpec 'eval_spec': an instance of tf.estimator.EvalSpec 'eval_input_receiver_fn': an instance of tfma.export.EvalInputReceiver

    Exactly one of 'module_file' or 'trainer_fn' must be supplied.

  • trainer_fn: A python path to UDF model definition function. See 'module_file' for the required signature of the UDF. Exactly one of 'module_file' or 'trainer_fn' must be supplied.

  • train_args: A trainer_pb2.TrainArgs instance, containing args used for training. Current only num_steps is available.

  • eval_args: A trainer_pb2.EvalArgs instance, containing args used for eval. Current only num_steps is available.

  • custom_config: A dict which contains the training job parameters to be passed to Google Cloud ML Engine. For the full set of parameters supported by Google Cloud ML Engine, refer to https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job

  • custom_executor_spec: Optional custom executor spec.

  • output: Optional 'ModelExportPath' channel for result of exported models.

  • transform_output: Backwards compatibility alias for the 'transform_graph' argument.

  • instance_name: Optional unique instance name. Necessary iff multiple Trainer components are declared in the same pipeline.

Raises:

  • ValueError: - When both or neither of 'module_file' and 'trainer_fn' is supplied.
    • When both or neither of 'examples' and 'transformed_examples' is supplied.
    • When 'transformed_examples' is supplied but 'transform_output' is not supplied.

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Properties

component_id

DEPRECATED FUNCTION

component_type

DEPRECATED FUNCTION

downstream_nodes

exec_properties

id

Node id, unique across all TFX nodes in a pipeline.

If instance name is available, node_id will be: . otherwise, node_id will be:

Returns:

node id.

inputs

outputs

type

upstream_nodes

Methods

add_downstream_node

View source

add_downstream_node(downstream_node)

add_upstream_node

View source

add_upstream_node(upstream_node)

from_json_dict

View source

from_json_dict(
    cls,
    dict_data
)

Convert from dictionary data to an object.

to_json_dict

View source

to_json_dict()

Convert from an object to a JSON serializable dictionary.

Class Members

  • EXECUTOR_SPEC