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Current version (not yet released; still in development)

Major Features and Improvements

  • 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.
  • Added option to use fixed Schema artifact for ExampleValidator, Transform and Trainer.
  • 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.

Bug fixes and other changes

  • Bumped test dependency to kfp (Kubeflow Pipelines SDK) to be at version at least 0.1.30.
  • 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.

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

Breaking changes

For pipeline authors

For component authors

Documentation updates

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

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.
  • Deprecated csv_input and tfrecord_input, use external_input instead.

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

Breaking changes