TF 2.0 is out! Get hands-on practice at TF World, Oct 28-31. Use code TF20 for 20% off select passes. Register now

Custom TFX Component

Custom executor or custom component

If only custom processing logic is needed while the inputs, outputs, and execution properties of the component are the same as an existing component, a custom executor is sufficient. A custom component is needed when any of the inputs, outputs, or execution properties are different than any existing TFX components.

How to create a custom component?

Developing a custom component will require:

  • A defined set of input and output artifact specifications for the new component. Specially, the types for the input artifacts should be consistent with the output artifact types of the components that produce the artifacts and the types for the output artifacts should be consistent with the input artifact types of the components that consume the artifacts if any.
  • The non-artifact execution parameters that are needed for the new component.


The ComponentSpec class defines the component contract by defining the input and output artifacts to a component as well as the parameters that will be used for the component execution. There are three parts in it:

  • INPUTS: Specifications for the input artifacts that will be passed into the component executor. Input artifacts are often outputs from upstream components and thus share the same spec
  • OUTPUTS: Specifications for the output artifacts which the component will produce.
  • PARAMETERS: Specifications for the execution properties that will be passed into the component executor. These are non-artifact parameters that should be defined flexibly in the pipeline DSL and passed to the new component instance.

Here is an example of the ComponentSpec, the full example can be found in the TFX GitHub repo.

class SlackComponentSpec(types.ComponentSpec):
  """ComponentSpec for Custom TFX Slack Component."""

  INPUTS = {
      'model_export': ChannelParameter(type=standard_artifacts.Model),
      'model_blessing': ChannelParameter(type=standard_artifacts.ModelBlessing),
      'slack_blessing': ChannelParameter(type=standard_artifacts.ModelBlessing),
      'slack_token': ExecutionParameter(type=Text),
      'slack_channel_id': ExecutionParameter(type=Text),
      'timeout_sec': ExecutionParameter(type=int),


Next, write the executor code for the new component. Basically, a new subclass of base_executor.BaseExecutor needs to be created with its Do function overriden. In the Do function, the arguments input_dict, output_dict and exec_properties that are passed in map to INPUTS, OUTPUTS and PARAMETERS that are defined in ComponentSpec respectively. For exec_properties, the value can be fetched directly through a dictionary lookup. For artifacts in input_dict and output_dict, there are convenient functions available to fetch the URIs of the artifacts (see model_export_uri and model_blessing_uri in the example) or get the artifact object (see slack_blessing in the example).

class Executor(base_executor.BaseExecutor):
  """Executor for Slack component."""
  def Do(self, input_dict: Dict[Text, List[types.TfxArtifact]],
         output_dict: Dict[Text, List[types.TfxArtifact]],
         exec_properties: Dict[Text, Any]) -> None:
    # Fetch execution properties from exec_properties dict.
    slack_token = exec_properties['slack_token']
    slack_channel_id = exec_properties['slack_channel_id']
    timeout_sec = exec_properties['timeout_sec']

    # Fetch input URIs from input_dict.
    model_export_uri = types.get_single_uri(input_dict['model_export'])
    model_blessing_uri = types.get_single_uri(input_dict['model_blessing'])

    # Fetch output artifact from output_dict.
    slack_blessing =

The example above only shows the part of the implementation that uses the passed-in value. Please see the full example in the TFX GitHub repo.

Unit testing a custom executor

Unit tests for the custom executor can be created similar to this one.

Component interface

Now that the most complex part is complete, the next step is to assemble these pieces into a component interface, to enable the component to be used in a pipeline. There are several steps:

  • Make the component interface a subclass of base_component.BaseComponent
  • Assign a class variable SPEC_CLASS with the ComponentSpec class that was defined earlier
  • Assign a class variable EXECUTOR_SPEC with the Executor class that was defined earlier
  • Define the __init__() constructor function by using the arguments to the function to construct an instance of the ComponentSpec class and invoke the super function with that value, along with an optional name

When an instance of the component is created, type checking logic in the base_component.BaseComponent class will be invoked to ensure that the arguments which were passed in are compatible with the type info defined in the ComponentSpec class.

from slack_component import executor

class SlackComponent(base_component.BaseComponent):
  """Custom TFX Slack Component."""

  SPEC_CLASS = SlackComponentSpec
  EXECUTOR_SPEC = executor_spec.ExecutorClassSpec(executor.Executor)

  def __init__(self,
               model_export: channel.Channel,
               model_blessing: channel.Channel,
               slack_token: Text,
               slack_channel_id: Text,
               timeout_sec: int,
               slack_blessing: Optional[channel.Channel] = None,
               name: Optional[Text] = None):
    slack_blessing = slack_blessing or channel.Channel(
    spec = SlackComponentSpec(
    super(SlackComponent, self).__init__(spec=spec, name=name)

Assemble into a TFX pipeline

The last step is to plug the new custom component into a TFX pipeline. Besides adding an instance of the new component, the following are also needed:

  • Properly wire the upstream and downstream components of the new component to it. This is done by referencing the outputs of the upstream component in the new component and referencing the outputs of the new component in downstream components
  • Add the new component instance to the components list when constructing the pipeline.

The example below highlights the aforementioned changes. Full example can be found in the TFX GitHub repo.

def _create_pipeline():
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  slack_validator = SlackComponent(

  pusher = Pusher(

  return pipeline.Pipeline(
          ..., model_validator, slack_validator, pusher

Deploy a custom component

Beside code changes, all the newly added parts (ComponentSpec, Executor, component interface) need to be accessible in pipeline running environment in order to run the pipeline properly.