TFX 0.22.0 Release

Major Features and Improvements

• Introduced experimental Python function component decorator (@component
  decorator under tfx.dsl.component.experimental.decorators) allowing
  Python function-based component definition.
• Added the experimental TemplatedExecutorContainerSpec executor class that
  supports structural placeholders (not Jinja placeholders).
• Added the experimental function "create_container_component" that
  simplifies creating container-based components.
• Implemented a TFJS rewriter.
• Added the scripts/run_component.py script which makes it easy to run the
  component code and executor code. (Similar to scripts/run_executor.py)
• Added support for container component execution to BeamDagRunner.
• Introduced experimental generic Artifact types for ML workflows.
• Added support for float execution properties.

Bug fixes and other changes

• Migrated BigQueryExampleGen to the new (experimental) ReadFromBigQuery
  PTramsform when not using Dataflow runner.
• Enhanced add_downstream_node / add_upstream_node to apply symmetric changes
  when being called. This method enables task-based dependencies by enforcing
  execution order for synchronous pipelines on supported platforms. Currently,
  the supported platforms are Airflow, Beam, and Kubeflow Pipelines. Note that
  this API call should be considered experimental, and may not work with
  asynchronous pipelines, sub-pipelines and pipelines with conditional nodes.
• Added the container-based sample pipeline (download, filter, print)
• Removed the incomplete cifar10 example.
• Removed python-snappy from [all] extra dependency list.
• Tests depends on apache-airflow>=1.10.10,<2;
• Removed test dependency to tzlocal.
• Fixes unintentional overriding of user-specified setup.py file for Dataflow
  jobs when running on KFP container.
• Made ComponentSpec().inputs and .outputs behave more like real dictionaries.
• Depends on kerastuner>=1,<2.
• Depends on pyyaml>=3.12,<6.
• Depends on apache-beam[gcp]>=2.21,<3.
• Depends on grpcio>=2.18.1,<3.
• Depends on kubernetes>=10.0.1,<12.
• Depends on tensorflow>=1.15,!=2.0.*,<3.
• Depends on tensorflow-data-validation>=0.22.0,<0.23.0.
• Depends on tensorflow-model-analysis>=0.22.1,<0.23.0.
• Depends on tensorflow-transform>=0.22.0,<0.23.0.
• Depends on tfx-bsl>=0.22.0,<0.23.0.
• Depends on ml-metadata>=0.22.0,<0.23.0.
• Fixed a bug in io_utils.copy_dir which prevent it to work correctly for
  nested sub-directories.

Breaking changes

For pipeline authors

• Changed custom config for the Do function of Trainer and Pusher to accept
  a JSON-serialized dict instead of a dict object. This also impacts all the
  Do functions under tfx.extensions.google_cloud_ai_platform and
  tfx.extensions.google_cloud_big_query_ml. Note that this breaking
  change occurs at the signature of the executor's Do function. Therefore, if
  the user did not customize the Do function, and the compile time SDK version
  is aligned with the run time SDK version, previous pipelines should still
  work as intended. If the user is using a custom component with customized
  Do function, custom_config should be assumed to be a JSON-serialized
  string from next release.
• For users of BigQueryExampleGen, --temp_location is now a required Beam
  argument, even for DirectRunner. Previously this argument was only required
  for DataflowRunner. Note that the specified value of --temp_location
  should point to a Google Cloud Storage bucket.
• Revert current per-component cache API (with enable_cache, which was only
  available in tfx>=0.21.3,<0.22), in preparing for a future redesign.

For component authors

• Converted the BaseNode class attributes to the constructor parameters. This
  won't affect any components derived from BaseComponent.
• Changed the encoding of the Integer and Float artifacts to be more portable.

Documentation updates

• Added concept guides for understanding TFX pipelines and components.
• Added guides to building Python function-based components and
  container-based components.
• Added BulkInferrer component and TFX CLI documentation to the table of
  contents.

Deprecations

• Deprecating Py2 support