Version 0.26.0

Major Features and Improvements

Supported output examples artifact for BulkInferrer which can be used to link with downstream training.
TFX Transform switched to a (notably) faster and more accurate implementation of tft.quantiles analyzer.
Added native TF 2 implementation of Transform. The default behavior will continue to use Tensorflow's compat.v1 APIs. This can be overriden by passing force_tf_compat_v1=False and enabling TF 2 behaviors. The default behavior for TF 2 will be switched to the new native implementation in a future release.
Added support for passing a callable to set pre/post transform statistic generation options.
In addition to the "tfx" pip package, a dependency-light distribution of the core pipeline authoring functionality of TFX is now available as the "ml-pipelines-sdk" pip package. This package does not include first-party TFX components. The "tfx" pip package is still the recommended installation path for TFX.

Wheel package building for TFX has changed, and users need to follow the [new TFX package build instructions] (https://github.com/tensorflow/tfx/blob/master/package_build/README.md) to build wheels for TFX.

TrainerFnArgs is deprecated by FnArgs.
Deprecated DockerComponentConfig class: user should set a DockerPlatformConfig proto in platform_config using with_platform_config() API instead.

Official TFX container image's entrypoint is changed so the image can be used as a custom worker for Dataflow.
In the published TFX container image, wheel files are now used to install TFX, and the TFX source code has been moved to /tfx/src.
Added a skeleton of CLI support for Kubeflow V2 runner, and implemented support for pipeline operations.
Added an experimental template to use with Kubeflow V2 runner.
Added sanitization of user-specified pipeline name in Kubeflow V2 runner.
Migrated deployment_config in Kubeflow V2 runner from Any proto message to Struct, to ensure compatibility across different copies of the proto libraries.
The tfx.dsl.io.fileio filesystem handler will delegate to tensorflow.io.gfile for any unknown filesystem schemes if TensorFlow is installed.
Skipped ephemeral package when the beam flag 'worker_harness_container_image' is set.
The tfx.dsl.io.makedirs call now succeeds if the directory already exists.
Depends on apache-beam[gcp]>=2.25,!=2.26,<3.
Depends on keras-tuner>=1,<1.0.2.
Depends on kfp-pipeline-spec>=0.1.3,<0.2.
Depends on ml-metadata>=0.26.0,<0.27.0.
Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.4.*,<3.
Depends on tensorflow-data-validation>=0.26,<0.27.
Depends on tensorflow-model-analysis>=0.26,<0.27.
Depends on tensorflow-serving>=1.15,!=2.0.*,!=2.1.*,!=2.2.*,!=2.4.*,<3.
Depends on tensorflow-transform>=0.26,<0.27.
Depends on tfx-bsl>=0.26.1,<0.27.