Rflow-tfx

* This a bug fix only version.

Major Features and Improvements

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Deprecations

* N/A

Bug fixes and other changes

* Automatic autoreload of underlying modules a single `_ModuleFinder`
registered per module.

Documentation updates

* N/A

* This a bug fix only version

Major Features and Improvements

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Deprecations

* N/A

Bug fixes and other changes

* The `tfx.version` attribute was restored.

Documentation updates

* N/A

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.
* Migrated LocalDagRunner to the new [IR](https://github.com/tensorflow/tfx/blob/master/tfx/proto/orchestration/pipeline.proto) stack.

Breaking changes

* 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.


For pipeline authors

* Added BigQueryToElwcExampleGen to take a query as input and generate
ExampleListWithContext (ELWC) examples.

For component authors

* N/A

Deprecations

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

Bug fixes and other changes

* 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.
* Fixed the component entrypoint, so that it creates the parent directory for
the output metadata file before trying to write the data.
* 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`.

Documentation updates

* N/A

Major Features and Improvements

* Supported multiple artifacts for Transform's input example and output
transformed example channels.
* Added support for processing specific spans in file-based ExampleGen with
range configuration.
* Added ContainerExecutableSpec in portable IR to support container components
portable orchestrator.
* Added Placeholder utility library. Placeholder can be used to represent
not-yet-available value at pipeline authoring time.
* Added support for the `tfx.dsl.io.fileio` pluggable filesystem interface,
with initial support for local files and the Tensorflow GFile filesystem
implementation.
* SDK and example code now uses `tfx.dsl.io.fileio` instead of `tf.io.gfile`
when possible for filesystem I/O implementation portability.
* From this release TFX will also be hosting nightly packages on
https://pypi-nightly.tensorflow.org. To install the nightly package use the
following command:


pip install --extra-index-url https://pypi-nightly.tensorflow.org/simple tfx

Note: These nightly packages are unstable and breakages are likely to happen.
The fix could often take a week or more depending on the complexity
involved for the wheels to be available on the PyPI cloud service. You can
always use the stable version of TFX available on PyPI by running the
command

pip install tfx

* Added CloudTuner KFP e2e example running on Google Cloud Platform with
distributed tuning.
* Migrated BigQueryExampleGen to the new `ReadFromBigQuery` on all runners.
* Introduced Kubeflow V2 DAG runner, which is based on
[Kubeflow IR spec](https://github.com/kubeflow/pipelines/blob/master/api/v2alpha1/pipeline_spec.proto).
Same as `KubeflowDagRunner` it will compile the DSL pipeline into a payload
but not trigger the execution locally.
* Added compile time check for schema mismatch in Kubeflow V2 runner.
* Added 'penguin' example. Penguin example uses Palmer Penguins dataset and
classify penguin species using four numeric features.
* Iris e2e examples are replaced by penguin examples.
* TFX BeamDagRunner is migrated to use the tech stack built on top of [IR](https://github.com/tensorflow/tfx/blob/master/tfx/proto/orchestration/pipeline.proto).
While this is no-op to users, it is a major step towards supporting more
flexible TFX DSL [semetic](https://github.com/tensorflow/community/blob/master/rfcs/20200601-tfx-udsl-semantics.md).
Please refer to the [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20200705-tfx-ir.md)
of IR to learn more details.
* Supports forward compatibility when evolving TFX artifact types, which
allows jobs of old release and new release run with the same MLMD instance.
* Graduated the portable/beam_dag_runner.py to beam/beam_dag_runner.py


Breaking changes

* Moved the directory that CLI stores pipeline information from
${HOME}/${ORCHESTRATOR} to ${HOME}/tfx/${ORCHESTRATOR}. For example,
"~/kubeflow" was changed to "~/tfx/kubeflow". This directory is used to
store pipeline information including pipeline ids in the Kubeflow Pipelines
cluster which are needed to create runs or update pipelines.
These files will be moved automatically when it is first used and no
separate action is needed.
See https://github.com/tensorflow/tfx/blob/master/docs/guide/cli.md for the
detail.

For pipeline authors

* N/A

For component authors

* N/A

Deprecations

* Modules under `tfx.components.base` have been deprecated and moved to
`tfx.dsl.components.base` in preparation for releasing a pipeline authoring
package without explicit Tensorflow dependency.
* Deprecated setting `instance_name` at pipeline node level. Instead, users
are encouraged to set `id` explicitly of any pipeline node through newly
added APIs.

Bug fixes and other changes

* Added the LocalDagRunner to allow local pipeline execution without using
Apache Beam. This functionality is in development.
* Introduced dependency to `tensorflow-cloud` Python package, with intention
to separate out Google Cloud Platform specific extensions.
* Depends on `mmh>=2.2,<3` in container image for potential performance
improvement for Beam based hashes.
* New extra dependencies `[examples]` is required to use codes inside
tfx/examples.
* Fixed the run_component script.
* Stopped depending on `WTForms`.
* Fixed an issue with Transform cache and beam 2.24-2.25 in an interactive
notebook that caused it to fail.
* Scripts - run_component - Added a way to output artifact properties.
* Fixed an issue resulting in incorrect cache miss to ExampleGen when no
`beam_pipeline_args` is provided.
* Changed schema as an optional input channel of Trainer as schema can be
accessed from TFT graph too.
* Fixed an issue during recording of a component's execution where
"missing or modified key in exec_properties" was raised from MLMD when
`exec_properties` both omitted an existing property and added a new
property.
* Supported users to set `id` of pipeline nodes directly.
* Added a new template, 'penguin' which is simple subset of
[penguin examples](https://github.com/tensorflow/tfx/tree/master/tfx/examples/penguin),
and uses the same
[Palmer Penguins](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)
dataset. The new template focused on easy ingestion of user's own data.
* Changed default data path for the taxi template from `tfx-template/data`
to `tfx-template/data/taxi`.
* Fixed a bug which crashes the pusher when infra validation did not pass.
* Depends on `apache-beam[gcp]>=2.25,<3`.
* Depends on `attrs>=19.3.0,<21`.
* Depends on `kfp-pipeline-spec>=0.1.2,<0.2`.
* Depends on `kfp>=1.1.0,<2`.
* Depends on `ml-metadata>=0.25,<0.26`.
* Depends on `tensorflow-cloud>=0.1,<0.2`.
* Depends on `tensorflow-data-validation>=0.25,<0.26`.
* Depends on `tensorflow-hub>=0.9.0,<0.10`.
* Depends on `tensorflow-model-analysis>=0.25,<0.26`.
* Depends on `tensorflow-transform>=0.25,<0.26`.
* Depends on `tfx-bsl>=0.25,<0.26`.

Documentation updates

* N/A

Major Features and Improvements

* N/A

Bug fixes and other changes

* Fixes issues where custom property access of a missing property created an invalid MLMD Artifact protobuf message.

Deprecations

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements

* Use TFXIO and batched extractors by default in Evaluator.
* Supported custom split configuration for ExampleGen and its downstream
components. Instead of hardcoded 'train' and 'eval' splits, TFX components
now can process the custom splits generated by ExampleGen. For details,
please refer to [ExampleGen doc](https://github.com/tensorflow/tfx/blob/r0.24.0/docs/guide/examplegen.md#custom-examplegen)
* Added python 3.8 support.

Bug fixes and other changes

* Supported CAIP Runtime 2.2 for online prediction pusher.
* Used 'python -m ' style for container entrypoints.
* Stopped depending on `google-resumable-media`.
* Stopped depending on `Werkzeug`.
* Depends on `absl-py>=0.9,<0.11`.
* Depends on `apache-beam[gcp]>=2.24,<3`.
* Depends on `ml-metadata>=0.24,<0.25`.
* Depends on `protobuf>=3.12.2,<4`.
* Depends on `tensorflow-data-validation>=0.24.1,<0.25`.
* Depends on `tensorflow-model-analysis>=0.24.3,<0.25`.
* Depends on `tensorflow-transform>=0.24.1,<0.25`.
* Depends on `tfx-bsl>=0.24.1,<0.25`.

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Deprecations

* Deprecated python 3.5 support.