Version 0.15.0rc0

Major Features and Improvements

• Offered unified CLI for tfx pipeline actions on various orchestrators
  including Apache Airflow, Apache Beam and Kubeflow.
• Polished experimental interactive notebook execution and visualizations
  so they are ready for use.
• Added BulkInferrer component to TFX pipeline, and corresponding offline
  inference taxi pipeline.
• Introduced ImporterNode as a special TFX node to register external resource
  into MLMD so that downstream nodes can use as input artifacts. An example
  taxi_pipeline_importer.py enabled by ImporterNode was added to showcase
  the user journey of user-provided schema (issue #571).
• Added experimental support for TFMA fairness indicator thresholds.
• Demonstrated DirectRunner multi-core processing in Chicago Taxi example,
  including Airflow and Beam.
• Made model validator executor forward compatible with TFMA change.
• Added Iris flowers classification example.
• Added support for serialization and deserialization of components.
• Made component launcher extensible to support launching components on
  multiple platforms.
• Added option to use fixed Schema artifact for ExampleValidator, Transform
  and Trainer.
• Simplified component package names.
• Introduced BaseNode as the base class of any node in a TFX pipeline DAG.
• Added docker component launcher to launch container component.
• Added support for specifying pipeline root in runtime when run on KubeflowDagRunner.
  A default value can be provided when constructing the TFX pipeline.
• Added basic span support in ExampleGen to ingest file based data sources
  that can be updated regularly by upstream.
• Branched serving examples under chicago_taxi_pipeline/ from
  chicago_taxi/ example.
• Supported beam arg 'direct_num_workers' for multi-processing on local.
• Improved naming of standard component inputs and outputs.
• Improved visualization functionality in the experimental TFX notebook
  interface.
• Allowed users to specify output file format when compiling TFX pipelines
  using KubeflowDagRunner.
• Introduced ResolverNode as a special TFX node to resolve input artifacts for
  downstream nodes. ResolverNode is a convenient way to wrap TFX Resolver, a
  logical unit for resolving input artifacts.
• Added cifar-10 example to demonstrate image classification.
• Added container builder feature in the CLI tool for container-based custom
  python components. This is specifically for the Kubeflow orchestration
  engine, which requires containers built with the custom python code.
• Demonstrated DirectRunner multi-core processing in Chicago Taxi example,
  including Airflow and Beam.

Bug fixes and other changes

• Bumped test dependency to kfp (Kubeflow Pipelines SDK) to
  be at version 0.1.31.2.
• Fixed trainer executor to correctly make transform_output optional.
• Updated Chicago Taxi example dependency tensorflow to version >=1.14.0.
• Updated Chicago Taxi example dependencies tensorflow-data-validation,
  tensorflow-metadata, tensorflow-model-analysis, tensorflow-serving-api, and
  tensorflow-transform to version >=0.14.
• Updated Chicago Taxi example dependencies to Beam 2.14.0, Flink 1.8.1, Spark
  2.4.3.
• Adopted new recommended way to access component inputs/outputs as
  component.outputs['output_name'] (previously, the syntax was
  component.outputs.output_name).
• Updated Iris example to skip transform and use Keras model.
• Fixed the check for input artifact existence in base driver.
• Fixed bug in AI Platform Pusher that prevents pushes after first model,
  and not being marked as default.
• Replaced all usage of deprecated tensorflow.logging with absl.logging.
• Used special user agent for all HTTP requests through
  googleapiclient and apitools.
• Transform component updated to use tf.compat.v1 according to the TF 2.0
  upgrading procedure.
• TFX updated to use tf.compat.v1 according to the TF 2.0 upgrading
  procedure.
• Added Kubeflow simple example that executes all components in-cluster.
• Fixed a bug that prevents updating execution type.
• Depended on apache-beam[gcp]>=2.16,<3
• Depended on ml-metadata>=0.15,<0.16
• Depended on tensorflow>=1.15,<3
• Depended on tensorflow-data-validation>=0.15,<0.16
• Depended on tensorflow-model-analysis>=0.15.2,<0.16
• Depended on tensorflow-transform>=0.15,<0.16
• Depended on 'tfx_bsl>=0.15.1,<0.16'

Deprecations

• Deprecated component_type in favor of type.
• Deprecated component_id in favor of id.
• Move beam_pipeline_args out of additional_pipeline_args as top level
  pipeline param
• Deprecated chicago_taxi folder, beam setup scripts and serving examples are
  moved to chicago_taxi_pipeline folder.

Breaking changes

• Moved beam setup scripts from examples/chicago_taxi/ to
  examples/chicago_taxi_pipeline/
• Moved interactive notebook classes into tfx.orchestration.experimental
  namespace.

For pipeline authors

• Deprecated the usage of tf.contrib.training.HParams in Trainer as it is
  deprecated in TF 2.0. User module relying on member method of that class
  will not be supported. Dot style property access will be the only supported
  style from now on.

For component authors

Documentation updates

• Added conceptual info on Artifacts to guide/index.md