Tfx

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0.28.0

Not secure
Major Features and Improvements

* Publically released TFX docker image in [tensorflow/tfx](
https://hub.docker.com/r/tensorflow/tfx) will use GPU
compatible based TensorFlow images from [Deep Learning Containers](
https://cloud.google.com/ai-platform/deep-learning-containers). This allow
these images to be used with GPU out of box.
* Added an example pipeline for a ranking model (using
[tensorflow_ranking](https://github.com/tensorflow/ranking))
at `tfx/examples/ranking`. More documentation will be available in future
releases.
* Added a [spans_resolver](
https://github.com/tensorflow/tfx/blob/master/tfx/dsl/experimental/spans_resolver.py)
that can resolve spans based on range_config.

Breaking Changes

For Pipeline Authors

* Custom arg key in `google_cloud_ai_platform.tuner.executor` is renamed to
`ai_platform_tuning_args` from `ai_platform_training_args`, to better
distinguish usage with Trainer.

For component authors

* N/A

Deprecations

* Deprecated input/output compatibility aliases for Transform and SchemaGen.

Bug Fixes and Other Changes

* Change Bigquery ML Pusher to publish the model to the user specified project
instead of the default project from run time context.
* Depends on `apache-beam[gcp]>=2.28,<3`.
* Depends on `ml-metadata>=0.28.0,<0.29.0`.
* Depends on `kfp-pipeline-spec>=0.1.6,<0.2`.
* Depends on `struct2tensor>=0.28.0,<0.29.0`.
* Depends on `tensorflow-data-validation>=0.28.0,<0.29.0`.
* Depends on `tensorflow-model-analysis>=0.28.0,<0.29.0`.
* Depends on `tensorflow-transform>=0.28.0,<0.29.0`.
* Depends on `tfx-bsl>=0.28.1,<0.29.0`.

Documentation Updates

* Published a [migration instruction](
https://github.com/tensorflow/tfx/blob/master/tfx/orchestration/launcher/README.md)
for legacy custom launcher developers.

0.27.0

Not secure
Major Features and Improvements

* Updated the `tfx.components.evaluator.Evaluator` component to support
[TFMA's "model-agnostic" evaluation](https://www.tensorflow.org/tfx/model_analysis/faq#how_do_i_setup_tfma_to_work_with_pre-calculated_ie_model-agnostic_predictions_tfrecord_and_tfexample).
The `model` channel is now optional when constructing the component, which
is useful when the `examples` channel provides tf.Examples containing both
the labels and pre-computed model predictions, i.e. "model-agnostic"
evaluation.
* Supports different types of quantizations on TFLite conversion using
TFLITE_REWRITER by setting `quantization_optimizations`,
`quantization_supported_types` and `quantization_enable_full_integer`. Flag
definitions can be found here: [Post-traning
quantization](https://www.tensorflow.org/lite/performance/post_training_quantization).
* Added automatic population of `tfdv.StatsOptions.vocab_paths` when computing
statistics within the Transform component.

Breaking changes

For pipeline authors

* `enable_quantization` from TFLITE_REWRITER is removed and setting
quantization_optimizations = [tf.lite.Optimize.DEFAULT] will perform the
same type of quantization, dynamic range quantization. Users of the
TFLITE_REWRITER who do not enable quantization should be uneffected.
* Default value for `infer_feature_shape` for SchemaGen changed from `False`
to `True`, as indicated in previous release log. The inferred schema might
change if you do not specify `infer_feature_shape`. It might leads to
changes of the type of input features in Transform and Trainer code.

For component authors

* N/A

Deprecations

* Pipeline information is not be stored on the local filesystem anymore using
Kubeflow Pipelines orchestration with CLI. Instead, CLI will always use the
latest version of the pipeline in the Kubeflow Pipeline cluster. All
operations will be executed based on the information on the Kubeflow
Pipeline cluster. There might be some left files on
`${HOME}/tfx/kubeflow` or `${HOME}/kubeflow` but those will not be used
any more.
* The `tfx.components.common_nodes.importer_node.ImporterNode` class has been
moved to `tfx.dsl.components.common.importer.Importer`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* The `tfx.components.common_nodes.resolver_node.ResolverNode` class has been
moved to `tfx.dsl.components.common.resolver.Resolver`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* The `tfx.dsl.resolvers.BaseResolver` class has been
moved to `tfx.dsl.components.common.resolver.ResolverStrategy`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* Deprecated input/output compatibility aliases for ExampleValidator,
Evaluator, Trainer and Pusher.

Bug fixes and other changes

* Add error condition checks to BulkInferrer's `output_example_spec`.
Previously, when the `output_example_spec` did not include the correct spec
definitions, the BulkInferrer would fail silently and output examples
without predictions.
* InfraValidator supports using alternative TensorFlow Serving image in case
deployed environment cannot reach the public internet (nor the docker hub).
Such alternative image should behave the same as official
`tensorflow/serving` image such as the same model volume path, serving port,
etc.
* Executor in `tfx.extensions.google_cloud_ai_platform.pusher.executor`
supported regional endpoint and machine_type.
* Starting from this version, proto files which are used to generate
component-level configs are included in the `tfx` package directly.
* The `tfx.dsl.io.fileio.NotFoundError` exception unifies handling of not-
found errors across different filesystem plugin backends.
* Fixes the serialization of zero-valued default when using `RuntimeParameter`
on Kubeflow.
* Depends on `apache-beam[gcp]>=2.27,<3`.
* Depends on `ml-metadata>=0.27.0,<0.28.0`.
* Depends on `numpy>=1.16,<1.20`.
* Depends on `pyarrow>=1,<3`.
* Depends on `kfp-pipeline-spec>=0.1.5,<0.2` in test and image.
* Depends on `tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3`.
* Depends on `tensorflow-data-validation>=0.27.0,<0.28.0`.
* Depends on `tensorflow-model-analysis>=0.27.0,<0.28.0`.
* Depends on `tensorflow-serving-api>=1.15,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3`.
* Depends on `tensorflow-transform>=0.27.0,<0.28.0`.
* Depends on `tfx-bsl>=0.27.0,<0.28.0`.

Documentation updates

* N/A

0.26.4

Not secure
* 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

* Depends on `apache-beam[gcp]>=2.25,!=2.26,<2.29`.
* Depends on `tensorflow-data-validation>=0.26.1,<0.27`.

Documentation updates

* N/A

0.26.3

Not secure
* 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

0.26.1

Not secure
* 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

0.26.0

Not secure
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

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