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`)
`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`

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