Current version (not yet released; still in development)

Major Features and Improvements

Bug fixes and other changes

Deprecations

Breaking changes

For pipeline authors

For component authors

Version 0.14.0

Major Features and Improvements

Added support for Google Cloud ML Engine Training and Serving as extension.
Supported pre-split input for ExampleGen components
Added ImportExampleGen component for importing tfrecord files with TF Example data format
Added a generic ExampleGen component to reduce the work of custom ExampleGen
Released Python 3 type hints and added support for Python 3.6 and 3.7.
Added an Airflow integration test for chicago_taxi_simple example.
Updated tfx docker image to use Python 3.6 on Ubuntu 16.04.
Added example for how to define and add a custom component.
Added PrestoExampleGen component.
Added Parquet executor for ExampleGen component.
Added Avro executor for ExampleGen component.
Enables Kubeflow Pipelines users to specify arbitrary ContainerOp decorators that can be applied to each pipeline step.
Added scripts and instructions for running the TFX Chicago Taxi example on Spark (via Apache Beam).
Introduced a new mechanism of artifact info passing between components that relies solely on ML Metadata.
Unified driver and execution logging to go through tf.logging.
Added support for Beam as an orchestrator.
Introduced the experimental InteractiveContext environment for iterative notebook development, as well as an example Chicago Taxi notebook in this environment with TFDV / TFMA examples.
Enabled Transform and Trainer components to specify user defined function (UDF) module by Python module path in addition to path to a module file.
Enable ImportExampleGen component for Kubeflow.
Enabled SchemaGen to infer feature shape.
Enabled metadata logging and pipeline caching capability for KubeflowRunner.
Used custom container for AI Platform Trainer extension.
Introduced ExecutorSpec, which generalizes the representation of executors to include both Python classes and containers.
Supported run context for metadata tracking of tfx pipeline.

Deprecations

Deprecated 'metadata_db_root' in favor of passing in metadata_connection_config directly.
airflow_runner.AirflowDAGRunner is renamed to airflow_dag_runner.AirflowDagRunner.
runner.KubeflowRunner is renamed to kubeflow_dag_runner.KubeflowDagRunner.
The "input" and "output" exec_properties fields for ExampleGen executors have been renamed to "input_config" and "output_config", respectively.
Declared 'cmle_training_args' on trainer and 'cmle_serving_args' on pusher deprecated. User should use the trainer/pusher executors in tfx.extensions.google_cloud_ai_platform module instead.
Moved tfx.orchestration.gcp.cmle_runner to tfx.extensions.google_cloud_ai_platform.runner.

Bug fixes and other changes

Updated components and code samples to use tft.TFTransformOutput ( introduced in tensorflow_transform 0.8). This avoids directly accessing the DatasetSchema object which may be removed in tensorflow_transform 0.14 or 0.15.
Fixed issue #113 to have consistent type of train_files and eval_files passed to trainer user module.
Fixed issue #185 preventing the Airflow UI from visualizing the component's subdag operators and logs.
Fixed issue #201 to make GCP credentials optional.
Bumped dependency to kfp (Kubeflow Pipelines SDK) to be at version at least 0.1.18.
Updated code example to
- use 'tf.data.TFRecordDataset' instead of the deprecated function 'tf.TFRecordReader'
- add test to train, evaluate and export.
Component definition streamlined with explicit ComponentSpec and new style for defining component classes.
TFX now depends on pyarrow>=0.14.0,<0.15.0 (through its dependency on tensorflow-data-validation).
Introduced 'examples' to the Trainer component API. It's recommended to use this field instead of 'transformed_examples' going forward.
Trainer can now run without the 'transform_output' input.
Added check for duplicated component ids within a pipeline.
String representations for Channel and Artifact (TfxType) classes were improved.
Updated workshop/setup/setup_demo.sh to fix version incompatibilities
Updated workshop by adding note and instructions to fix issue with GCC version when starting airflow webserver.
Prepared support for analyzer cache optimization in transform executor.
Fixed issue #463 correcting syntax in SCHEMA_EMPTY message.
Added an explicit check that pipeline name cannot exceed 63 characters.
SchemaGen takes a new argument, infer_feature_shape to indicate whether to infer shape of features in schema. Current default value is False, but we plan to remove default value for it in future.
Depended on 'click>=7.0,<8'
Depended on apache-beam[gcp]>=2.14,<3
Depended on ml-metadata>=-1.14.0,<0.15
Depended on tensorflow-data-validation>=0.14.1,<0.15
Depended on tensorflow-model-analysis>=0.14.0,<0.15
Depended on tensorflow-transform>=0.14.0,<0.15

Breaking changes

For pipeline authors

The "outputs" argument, which is used to override the automatically- generated output Channels for each component class has been removed; the equivalent overriding functionality is now available by specifying optional keyword arguments (see each component class definition for details).
The optional arguments "executor" and "unique_name" of component classes have been uniformly renamed to "executor_spec" and "instance_name", respectively.
The "driver" optional argument of component classes is no longer available: users who need to override the driver for a component should subclass the component and override the DRIVER_CLASS field.
The example_gen.component.ExampleGen class has been refactored into the example_gen.component._QueryBasedExampleGen and example_gen.component.FileBasedExampleGen classes.
pipeline_root passed to pipeline.Pipeline is now the root to the running pipeline instead of root of all pipelines.

For component authors

Component class definitions have been simplified; existing custom components need to:
- specify a ComponentSpec contract and conform to new class definition style (see base_component.BaseComponent)
- specify EXECUTOR_SPEC=ExecutorClassSpec(MyExecutor) in the component definition to replace executor=MyExecutor from component constructor.
Artifact definitions for standard TFX components have moved from using string type names into being concrete Artifact classes (see each official TFX component's ComponentSpec definition in types.standard_component_specs and the definition of built-in Artifact types in types.standard_artifacts).
The base_component.ComponentOutputs class has been renamed to base_component._PropertyDictWrapper.
The tfx.utils.types.TfxType class has been renamed to tfx.types.Artifact.
The tfx.utils.channel.Channel class has been moved to tfx.types.Channel.
The "static_artifact_collection" argument to types.Channel has been renamed to "artifacts".
ArtifactType for artifacts will have two new properties: pipeline_name and producer_component.
The ARTIFACT_STATE_* constants were consolidated into the types.artifacts.ArtifactState enum class.

Version 0.13.0

Major Features and Improvements

Adds support for Python 3.5
Initial version of following orchestration platform supported:
- Kubeflow
Added TensorFlow Model Analysis Colab example
Supported split ratio for ExampleGen components
Supported running a single executor independently

Bug fixes and other changes

Fixes issue #43 that prevent new execution in some scenarios
Fixes issue #47 that causes ImportError on chicago_taxi execution on dataflow
Depends on apache-beam[gcp]>=2.12,<3
Depends on tensorflow-data-validation>=0.13.1,<0.14
Depends on tensorflow-model-analysis>=0.13.2,<0.14
Depends on tensorflow-transform>=0.13,<0.14
Deprecations:
- PipelineDecorator is deprecated. Please construct a pipeline directly from a list of components instead.
Increased verbosity of logging to container stdout when running under Kubeflow Pipelines.
Updated developer tutorial to support Python 3.5+

Breaking changes

Examples code are moved from 'examples' to 'tfx/examples': this ensures that PyPi package contains only one top level python module 'tfx'.

Things to notice for upgrading

Multiprocessing on Mac OS >= 10.13 might crash for Airflow. See AIRFLOW-3326 for details and solution.

Version 0.12.0

Major Features and Improvements

Adding TFMA Architecture doc
TFX User Guide
Initial version of the following TFX components:
- CSVExampleGen - CSV data ingestion
- BigQueryExampleGen - BigQuery data ingestion
- StatisticsGen - calculates statistics for the dataset
- SchemaGen - examines the dataset and creates a data schema
- ExampleValidator - looks for anomalies and missing values in the dataset
- Transform - performs feature engineering on the dataset
- Trainer - trains the model
- Evaluator - performs analysis of the model performance
- ModelValidator - helps validate exported models ensuring that they are "good enough" to be pushed to production
- Pusher - deploys the model to a serving infrastructure, for example the TensorFlow Serving Model Server
Initial version of following orchestration platform supported:
- Apache Airflow
Polished examples based on the Chicago Taxi dataset.

Bug fixes and other changes

Cleanup Colabs to remove TF warnings
Performance improvement during shuffling of post-transform data.
Changing example to move everything to one file in plugins
Adding instructions to refer to README when running Chicago Taxi notebooks

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