Rflow-tfx

Major Features and Improvements

* N/A

Bug fixes and other changes
* Fixed InfraValidator signal handling bug on BeamDagRunner.
* Dropped "Type" suffix from primitive type artifact names (Integer, Float,
String, Bytes).

Deprecations

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements
* Added run/pipeline link when creating runs/pipelines on KFP through TFX CLI.
* Added support for `ValueArtifact`, whose attribute `value` allows users to
access the content of the underlying file directly in the executor. Support
Bytes/Integer/String/Float type. Note: interactive resolution does not
support this for now.
* Added InfraValidator component that is used as an early warning layer
before pushing a model into production.

Bug fixes and other changes
* Starting this version, TFX will only release python3 packages.
* Replaced relative import with absolute import in generated templates.
* Added a native keras model in the taxi template and the template now uses
generic Trainer.
* Added support of TF 2.1 runtime configuration for AI Platform Prediction
Pusher.
* Added support for using ML Metadata ArtifactType messages as Artifact
classes.
* Changed CLI behavior to create new versions of pipelines instead of
delete and create new ones when pipelines are updated for KFP. (Requires
kfp >= 0.3.0)
* Added ability to enable quantization in tflite rewriter.
* Added k8s pod labels when the pipeline is executed via KubeflowDagRunner for
better usage telemetry.
* Parameterized the GCP taxi pipeline sample for easily ramping up to full
taxi dataset.
* Added support for hyphens(dash) in addition to underscores in CLI flags.
Underscores will be supported as well.
* Fixed ill-formed underscore in the markdown visualization when running on
KFP.
* Enabled per-component control for caching with enable_cache argument in
each component.

Deprecations

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements
* Updated `StatisticsGen` to optionally consume a schema `Artifact`.
* Added support for configuring the `StatisticsGen` component via serializable
parts of `StatsOptions`.
* Added Keras guide doc.
* Changed Iris model_to_estimator e2e example to use generic Trainer.
* Demonstrated how TFLite is supported in TFX by extending MNIST example
pipeline to also train a TFLite model.

Bug fixes and other changes
* Fix the behavior of Trainer Tensorboard visualization when caching is used.
* Added component documentation and guide on using TFLite in TFX.
* Relaxed the PyYaml dependency.

Deprecations
* Model Validator (its functionality is now provided by the Evaluator).

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements
* Pipelines compiled using KubeflowDagRunner now defaults to using the
gRPC-based MLMD server deployed in Kubeflow Pipelines clusters when
performing operations on pipeline metadata.
* Added tfx model rewriting and tflite rewriter.
* Added LatestBlessedModelResolver as an experimental feature which gets the
latest model that was blessed by model validator.
* The specific `Artifact` subclass that was serialized (if defined in the
deserializing environment) will be used when deserializing `Artifact`s and
when reading `Artifact`s from ML Metadata (previously, objects of the
generic `tfx.types.artifact.Artifact` class were created in some cases).
* Updated Evaluator's executor to support model validation.
* Introduced awareness of chief worker to Trainer's executor, in case running
in distributed training cluster.
* Added a Chicago Taxi example with native Keras.
* Updated TFLite converter to work with TF2.
* Enabled filtering by artifact producer and output key in ResolverNode.

Bug fixes and other changes
* Added --skaffold_cmd flag when updating a pipeline for kubeflow in CLI.
* Changed python_version to 3.7 when using TF 1.15 and later for Cloud AI Platform Prediction.
* Added 'tfx_runner' label for CAIP, BQML and Dataflow jobs submitted from
TFX components.
* Fixed the Taxi Colab notebook.
* Adopted the generic trainer executor when using CAIP Training.
* Depends on 'tensorflow-data-validation>=0.21.4,<0.22'.
* Depends on 'tensorflow-model-analysis>=0.21.4,<0.22'.
* Depends on 'tensorflow-transform>=0.21.2,<0.22'.
* Fixed misleading logs in Taxi pipeline portable Beam example.

Deprecations

* N/A

Breaking changes
* Remove "NOT_BLESSED" artifact.
* Change constants ARTIFACT_PROPERTY_BLESSED_MODEL_* to ARTIFACT_PROPERTY_BASELINE_MODEL_*.

For pipeline authors

* N/A

For component authors

* N/A

Documentation updates

* N/A

Major Features and Improvements

* TFX version 0.21.0 will be the last version of TFX supporting Python 2.
* Added experimental cli option `template`, which can be used to scaffold a
new pipeline from TFX templates. Currently the `taxi` template is provided
and more templates would be added in future versions.
* Added support for `RuntimeParameter`s to allow users can specify templated
values at runtime. This is currently only supported in Kubeflow Pipelines.
Currently, only attributes in `ComponentSpec.PARAMETERS` and the URI of
external artifacts can be parameterized (component inputs / outputs can
not yet be parameterized). See
`tfx/examples/chicago_taxi_pipeline/taxi_pipeline_runtime_parameter.py`
for example usage.
* Users can access the parameterized pipeline root when defining the
pipeline by using the `pipeline.ROOT_PARAMETER` placeholder in
KubeflowDagRunner.
* Users can pass appropriately encoded Python `dict` objects to specify
protobuf parameters in `ComponentSpec.PARAMETERS`; these will be decoded
into the proper protobuf type. Users can avoid manually constructing complex
nested protobuf messages in the component interface.
* Added support in Trainer for using other model artifacts. This enables
scenarios such as warm-starting.
* Updated trainer executor to pass through custom config to the user module.
* Artifact type-specific properties can be defined through overriding the
`PROPERTIES` dictionary of a `types.artifact.Artifact` subclass.
* Added new example of chicago_taxi_pipeline on Google Cloud Bigquery ML.
* Added support for multi-core processing in the Flink and Spark Chicago Taxi
PortableRunner example.
* Added a metadata adapter in Kubeflow to support logging the Argo pod ID as
an execution property.
* Added a prototype Tuner component and an end-to-end iris example.
* Created new generic trainer executor for non estimator based model, e.g.,
native Keras.
* Updated to support passing `tfma.EvalConfig` in evaluator when calling TFMA.
* Added an iris example with native Keras.
* Added an MNIST example with native Keras.

Bug fixes and other changes
* Switched the default behavior of KubeflowDagRunner to not mounting GCP
secret.
* Fixed "invalid spec: spec.arguments.parameters[6].name 'pipeline-root' is
not unique" error when the user include `pipeline.ROOT_PARAMETER` and run
pipeline on KFP.
* Added support for an hparams artifact as an input to Trainer in
preparation for tuner support.
* Refactored common dependencies in the TFX dockerfile to a base image to
improve the reliability of image building process.
* Fixes missing Tensorboard link in KubeflowDagRunner.
* Depends on `apache-beam[gcp]>=2.17,<2.18`
* Depends on `ml-metadata>=0.21,<0.22`.
* Depends on `tensorflow-data-validation>=0.21,<0.22`.
* Depends on `tensorflow-model-analysis>=0.21,<0.22`.
* Depends on `tensorflow-transform>=0.21,<0.22`.
* Depends on `tfx-bsl>=0.21,<0.22`.
* Depends on `pyarrow>=0.14,<0.15`.
* Removed `tf.compat.v1` usage for iris and cifar10 examples.
* CSVExampleGen: started using the CSV decoding utilities in `tfx-bsl`
(`tfx-bsl>=0.15.2`)
* Fixed problems with Airflow tutorial notebooks.
* Added performance improvements for the Transform Component (for statistics
generation).
* Raised exceptions when container building fails.
* Enhanced custom slack component by adding a kubeflow example.
* Allowed windows style paths in Transform component cache.
* Fixed bug in CLI (--engine=kubeflow) which uses hard coded obsolete image
(TFX 0.14.0) as the base image.
* Fixed bug in CLI (--engine=kubeflow) which could not handle skaffold
response when an already built image is reused.
* Allowed users to specify the region to use when serving with AI Platform.
* Allowed users to give deterministic job id to AI Platform Training job.
* System-managed artifact properties ("name", "state", "pipeline_name" and
"producer_component") are now stored as ML Metadata artifact custom
properties.
* Fixed loading trainer and transformation functions from python module files
without the .py extension.
* Fixed some ill-formed visualization when running on KFP.
* Removed system info from artifact properties and use channels to hold info
for generating MLMD queries.
* Rely on MLMD context for inter-component artifact resolution and execution
publishing.
* Added pipeline level context and component run level context.
* Included test data for examples/chicago_taxi_pipeline in package.
* Changed `BaseComponentLauncher` to require the user to pass in an ML
Metadata connection object instead of a ML Metadata connection config.
* Capped version of Tensorflow runtime used in Google Cloud integration to
1.15.
* Updated Chicago Taxi example dependencies to Beam 2.17.0, Flink 1.9.1, Spark
2.4.4.
* Fixed an issue where `build_ephemeral_package()` used an incorrect path to
locate the `tfx` directory.
* The ImporterNode now allows specification of general artifact properties.
* Added 'tfx_executor', 'tfx_version' and 'tfx_py_version' labels for CAIP,
BQML and Dataflow jobs submitted from TFX components.
* Use '_' instead of '/' in feature names of several examples to avoid
potential clash with namescope separator.


Deprecations

* N/A

Breaking changes

For pipeline authors
* Standard artifact TYPE_NAME strings were reconciled to match their class
names in `types.standard_artifacts`.
* The "split" property on multiple artifacts has been replaced with the
JSON-encoded "split_names" property on a single grouped artifact.
* The execution caching mechanism was changed to rely on ML Metadata
pipeline context. Existing cached executions will not be reused when running
on this version of TFX for the first time.
* The "split" property on multiple artifacts has been replaced with the
JSON-encoded "split_names" property on a single grouped artifact.

For component authors
* Artifact type name strings to the `types.artifact.Artifact` and
`types.channel.Channel` classes are no longer supported; usage here should
be replaced with references to the artifact subclasses defined in
`types.standard_artfacts.*` or to custom subclasses of
`types.artifact.Artifact`.

Documentation updates

* N/A

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.
* Introduced `PipelineConfig` and `BaseComponentConfig` to control the
platform specific settings for pipelines and components.
* Added a custom Executor of Pusher to push model to BigQuery ML for serving.
* Added KubernetesComponentLauncher to support launch ExecutorContainerSpec in
a Kubernetes cluster.
* 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.
* 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.
* Added Kubeflow artifact visualization of inputs, outputs and execution
properties for components using a Markdown file. Added Tensorboard to
Trainer components as well.

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 local example pipeline that executes components in-cluster.
* Fixed a bug that prevents updating execution type.
* Fixed a bug in model validator driver that reads across pipeline boundaries
when resolving latest blessed model.
* 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'
* Made launcher return execution information, containing populated inputs,
outputs, and execution id.
* Updated the default configuration for accessing MLMD from pipelines running
in Kubeflow.
* Updated Airflow developer tutorial
* CSVExampleGen: started using the CSV decoding utilities in `tfx-bsl`
(`tfx-bsl>=0.15.2`)
* Added documentation for Fairness Indicators.

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.
* Starting from 1.15, package `tensorflow` comes with GPU support. Users won't
need to choose between `tensorflow` and `tensorflow-gpu`. If any GPU devices
are available, processes spawned by all TFX components will try to utilize
them; note that in rare cases, this may exhaust the memory of the device(s).
* Caveat: `tensorflow` 2.0.0 is an exception and does not have GPU support. If
`tensorflow-gpu` 2.0.0 is installed before installing `tfx`, it will be
replaced with `tensorflow` 2.0.0. Re-install `tensorflow-gpu` 2.0.0 if
needed.
* Caveat: MLMD schema auto-upgrade is now disabled by default. For users who
upgrades from 0.13 and do not want to lose the data in MLMD, please refer to
[MLMD documentation](https://github.com/google/ml-metadata/blob/master/g3doc/get_started.md#upgrade-mlmd-library)
for guide to upgrade or downgrade MLMD database. Users who upgraded from TFX
0.14 should not be affected since there is not schema change between these
two versions.

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.
* Any SavedModel produced by tf.Transform <=0.14 using any tf.contrib ops (or
tf.Transform ops that used tf.contrib ops such as tft.quantiles,
tft.bucketize, etc.) cannot be loaded with TF 2.0 since the contrib library
has been removed in 2.0. Please refer to this
[issue](https://github.com/tensorflow/tfx/issues/838).

For component authors

* N/A

Documentation updates

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