TFX 1.15.0 Release

Version 1.15.0

Major Features and Improvements

• Dropped python 3.8 support.
• Extend GetPipelineRunExecutions, GetPipelineRunArtifacts APIs to support
  filtering by execution create_time, type.
• ExampleValidator and DistributionValidator now support anomalies alert
  generation. Users can use their own toolkits to extract and process the
  alerts from the execution parameter.
• Allow DistributionValidator baseStatistics input channel artifacts to be
  empty for cold start of data validation.
• ph.make_proto() allows constructing proto-valued placeholders, e.g. for
  larger config protos fed to a component.
• ph.join_path() is like os.path.join() but for placeholders.
• Support passing in experimental_debug_stripper into the Transform
  pipeline runner.

Breaking Changes

• Placeholder and all subclasses have been moved to other modules, their
  structure has been changed and they're now immutable. Most users won't care
  (the main public-facing API is unchanged and behaves the same way). If you
  do special operations like isinstance() or some kind of custom
  serialization on placeholders, you will have to update your code.
• placeholder.Placeholder.traverse() now returns more items than before,
  namely also placeholder operators like _ConcatOperator (which is the
  implementation of Python's + operator).
• The placeholder.RuntimeInfoKey enumeration was removed. Just hard-code the
  appropriate string values in your code, and reference the new Literal type
  placeholder.RuntimeInfoKeys if you want to ensure correctness.
• Arguments to @component must now be passed as kwargs and its return type
  is changed from being a Type to just being a callable that returns a new
  instance (like the type's initializer). This will allow us to instead return
  a factory function (which is not a Type) in future. For a given
  @component def C(), this means:
  • You should not use C as a type anymore. For instance, replace
    isinstance(foo, C) with something else. Depending on your use case, if
    you just want to know whether it's a component, then use
    isinstance(foo, tfx.types.BaseComponent) or
    isinstance(foo, tfx.types.BaseFunctionalComponent).
    If you want to know which component it is, check its .id instead.
    Existing such checks will break type checking today and may additionally
    break at runtime in future, if we migrate to a factory function.
  • You can continue to use C.test_call() like before, and it will
    continue to be supported in future.
  • Any type declarations using foo: C break and must be replaced with
    foo: tfx.types.BaseComponent or
    foo: tfx.types.BaseFunctionalComponent.
  • Any references to static class members like C.EXECUTOR_SPEC breaks
    type checking today and should be migrated away from. In particular, for
    .EXECUTOR_SPEC.executor_class().Do() in unit tests, use .test_call()
    instead.
  • If your code previously asserted a wrong type declaration on C, this
    can now lead to (justified) type checking errors that were previously
    hidden due to C being of type Any.
• ph.to_list() was renamed to ph.make_list() for consistency.

For Pipeline Authors

For Component Authors

Deprecations

• Deprecated python 3.8

Bug Fixes and Other Changes

• Fixed a synchronization bug in google_cloud_ai_platform tuner.
• Print best tuning trials only from the chief worker of google_cloud_ai_platform tuner.
• Add a kpf dependency in the docker-image extra packages.
• Fix BigQueryExampleGen failure without custom_config.

Dependency Updates

Package Name	Version Constraints	Previously (in v1.14.0)	Comments
keras-tuner	>=1.0.4,<2,!=1.4.0,!=1.4.1	>=1.0.4,<2
packaging	>=20,<21	>=22
attrs	19.3.0,<22	19.3.0,<24
google-cloud-bigquery	>=2.26.0,<3	>=3,<4
tensorflow	>=2.15,<2.16	>=2.13,<2.14
tensorflow-decision-forests	>=1.0.1,<1.9	>=1.0.1,<2
tensorflow-hub	>=0.9.0,<0.14	>=0.15.0,<0.16
tensorflow-serving	>=1.15,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<3	>=2.15,<2.16

Documentation Updates