Rflow-tfx

* This is a bug fix version (to resolve impossible dependency conflicts).
Major Features and Improvements

* N/A

Bug fixes and other changes

* Stopped depending on `google-resumable-media`.
* Depends on `apache-beam[gcp]>=2.24,<3`.
* Depends on `tensorflow-data-validation>=0.23.1,<0.24`.

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Deprecations

* Deprecated Python 3.5 support.

Major Features and Improvements
* Added TFX DSL IR compiler that encodes a TFX pipeline into a DSL proto.
* Supported feature based split partition in ExampleGen.
* Added the ConcatPlaceholder to tfx.dsl.component.experimental.placeholders.
* Changed Span information as a property of ExampleGen's output artifact.
Deprecated ExampleGen input (external) artifact.
* Added ModelRun artifact for Trainer for storing training related files,
e.g., Tensorboard logs. Trainer's Model artifact now only contain pure
models (check `tfx/utils/path_utils.py` for details).
* Added support for `tf.train.SequenceExample` in ExampleGen:
* ImportExampleGen now supports `tf.train.SequenceExample` importing.
* base_example_gen_executor now supports `tf.train.SequenceExample` as
output payload format, which can be utilized by custom ExampleGen.
* Added Tuner component and its integration with Google Cloud Platform as
the execution and hyperparemeter optimization backend.
* Switched Transform component to use the new TFXIO code path. Users may
potentially notice large performance improvement.
* Added support for primitive artifacts to InputValuePlaceholder.
* Supported multiple artifacts for Trainer and Tuner's input example Channel.
* Supported split configuration for Trainer and Tuner.
* Supported split configuration for Evaluator.
* Supported split configuration for StatisticsGen, SchemaGen and
ExampleValidator. SchemaGen will now use all splits to generate schema
instead of just using `train` split. ExampleValidator will now validate all
splits against given schema instead of just validating `eval` split.
* Component authors now can create a TFXIO instance to get access to the
data through `tfx.components.util.tfxio_utils`. As TFX is going to
support more data payload formats and data container formats, using
`tfxio_utils` is encouraged to avoid dealing directly with each combination.
TFXIO is the interface of [Standardized TFX Inputs](
https://github.com/tensorflow/community/blob/master/rfcs/20191017-tfx-standardized-inputs.md).
* Added experimental BaseStubExecutor and StubComponentLauncher to test TFX
pipelines.
* Added experimental TFX Pipeline Recorder to record output artifacts of the
pipeline.
* Supported multiple artifacts in an output Channel to match a certain input
Channel's artifact count. This enables Transform component to process
multiple artifacts.
* Transform component's transformed examples output is now optional (enabled
by default). This can be disabled by specifying parameter
`materialize=False` when constructing the component.
* Supported `Version` spec in input config for file based ExampleGen.
* Added custom config to Transform component and made it available to
pre-processing fn.
* Supported custom extractors in Evaluator.
* Deprecated tensorflow dependency from MLMD python client.
* Supported `Date` spec in input config for file based ExampleGen.
* Enabled analyzer cache optimization in the Transform component:
* specify `analyzer_cache` to use the cache generated from a previous run.
* specify parameter `disable_analyzer_cache=True` (False by default) to
disable cache (won't generate cache output).
* Added support for width modifiers in Span and Version specs for file based
ExampleGen.

Bug fixes and other changes
* Added Tuner component to Iris e2e example.
* Relaxed the rule that output artifact uris must be newly created. This is a
temporary workaround to make retry work. We will introduce a more
comprehensive solution for idempotent execution.
* Made evaluator output optional (while still recommended) for pusher.
* Moved BigQueryExampleGen to `tfx.extensions.google_cloud_big_query`.
* Moved BigQuery ML Pusher to `tfx.extensions.google_cloud_big_query.pusher`.
* Removed Tuner from custom_components/ as it's supported under components/
now.
* Added support of non tf.train.Example protos as internal data payload
format by ImportExampleGen.
* Used thread local storage for `label_utils.scoped_labels()` to make it
thread safe.
* Requires [Bazel](https://bazel.build/) to build TFX source code.
* Upgraded python version in TFX docker images to 3.7. Older version of
python (2.7/3.5/3.6) is not available anymore in `tensorflow/tfx` images
on docker hub. Virtualenv is not used anymore.
* Stopped requiring `avro-python3`.
* Depends on `absl-py>=0.7,<0.9`.
* Depends on `apache-beam[gcp]>=2.23,<3`.
* Depends on `pyarrow>=0.17,<0.18`.
* Depends on `attrs>=19.3.0,<20`.
* Depends on `ml-metadata>=0.23,<0.24`.
* Depends on `tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,<3`.
* Note: Dependency like `tensorflow-transform` might impose a narrower
range of `tensorflow`.
* Depends on `tensorflow-data-validation>=0.23,<0.24`.
* Depends on `tensorflow-model-analysis>=0.23,<0.24`.
* Depends on `tensorflow-serving-api>=1.15,!=2.0.*,!=2.1.*,!=2.2.*,<3`.
* Depends on `tensorflow-transform>=0.23,<0.24`.
* Depends on `tfx-bsl>=0.23,<0.24`.
* Added execution_result_pb2.ExecutorOutput as an Optional return value of
BaseExecutor. This change is backward compatible to all existing executors.
* Added executor_output_uri and stateful_working_dir to Executor's context.

Breaking changes
* Changed the URIs of the value artifacts to point to files.
* De-duplicated the
tfx.dsl.component.experimental.executor_specs.CommandLineArgumentType
union type in favor of
tfx.dsl.component.experimental.placeholders.CommandLineArgumentType


For pipeline authors
* Moved BigQueryExampleGen to `tfx.extensions.google_cloud_big_query`. The
previous module path from `tfx.components` is not available anymore. This is
a breaking change.
* Moved BigQuery ML Pusher to `tfx.extensions.google_cloud_big_query.pusher`.
The previous module path from `tfx.extensions.google_cloud_big_query_ml`
is not available anymore.
* Updated beam pipeline args, users now need to set both `direct_running_mode`
and `direct_num_workers` explicitly for multi-processing.
* Added required 'output_data_format' execution property to
FileBaseExampleGen.
* Changed ExampleGen to take a string as input source directly instead of a
Channel of external artifact:
* Previously deprecated `input_base` Channel is changed to string type
instead of Channel. This is a breaking change, users should pass string
directly to `input_base`.
* Fully removed csv_input and tfrecord_input in dsl_utils. This is a breaking
change, users should pass string directly to `input_base`.

For component authors
* Changed GetInputSourceToExamplePTransform interface by removing input_dict.
This is a breaking change, custom ExampleGens need to follow the interface
change.
* Changed ExampleGen to take a string as input source directly instead of a
Channel of external artifact:
* `input` Channel is deprecated. The use of `input` is valid but
should change to string type `input_base` ASAP.

Documentation updates
* N/A

Deprecations
* ExternalArtifact and `external_input` function are deprecated. The use
of `external_input` with ExampleGen `input` is still valid but should change
to use `input_base` ASAP.
* Note: We plan to remove Python 3.5 support after this release.

Major Features and Improvements

* N/A

Bug fixes and other changes

* Reuse Examples artifact type introduced in TFX 0.23 to allow older release jobs running together with TFX 0.23+ release.

Deprecations

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements

* N/A

Bug fixes and other changes
* Depends on 'tensorflowjs>=2.0.1.post1,<3' for `[all]` dependency.
* Fixed the name of the usage telemetry when tfx templates are used.
* Depends on `tensorflow-data-validation>=0.22.2,<0.23.0`.
* Depends on `tensorflow-model-analysis>=0.22.2,<0.23.0`.
* Depends on `tfx-bsl>=0.22.1,<0.23.0`.
* Depends on `ml-metadata>=0.22.1,<0.23.0`.

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Deprecations

* N/A

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`.
* Depends on 'tensorflowjs>=2.0.1.post1,<3' for `[all]` dependency.
* 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

Major Features and Improvements

* N/A

Bug fixes and other changes

* Reuse Examples artifact type introduced in TFX 0.23 to allow older release jobs running together with TFX 0.23+ release.
* Removed python-snappy from [all] extra dependency list.

Deprecations

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A