tfx.v1.dsl.Resolver

Definition for TFX Resolver.

tfx.v1.dsl.Resolver(
    strategy_class: Optional[Type[ResolverStrategy]] = None,
    config: Optional[Dict[str, json_utils.JsonableType]] = None,
    function: Optional[resolver_function.ResolverFunction] = None,
    **channels
)

Used in the notebooks

Used in the tutorials
TFX Estimator Component Tutorial TFX Keras Component Tutorial Model analysis using TFX Pipeline and TensorFlow Model Analysis

Resolver is a special TFX node which handles special artifact resolution logics that will be used as inputs for downstream nodes.

To use Resolver, pass the followings to the Resolver constructor:

• Name of the Resolver instance
• A subclass of ResolverStrategy
• Configs that will be used to construct an instance of ResolverStrategy
• Channels to resolve with their tag, in the form of kwargs

Here is an example:

example_gen = ImportExampleGen(...)
examples_resolver = Resolver(
      strategy_class=tfx.dsl.experimental.SpanRangeStrategy,
      config={'range_config': range_config},
      examples=Channel(type=Examples, producer_component_id=example_gen.id)
      ).with_id('Resolver.span_resolver')
trainer = Trainer(
    examples=examples_resolver.outputs['examples'],
    ...)

You can find experimental ResolverStrategy classes under tfx.v1.dsl.experimental module, including LatestArtifactStrategy, LatestBlessedModelStrategy, SpanRangeStrategy, etc.

Args
strategy_class	Optional ResolverStrategy which contains the artifact resolution logic. One of strategy_class or function argument should be set.
config	Optional dict of key to Jsonable type for constructing resolver_strategy.
function	Optional ResolverFunction which contains the artifact resolution logic. User should not use this parameter directly but use @resolver_stem decorated function instead. One of strategy_class or function argument should be set.
**channels	Input channels to the Resolver node as keyword arguments.

Attributes
outputs	Output Channel dict that contains resolved artifacts.