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RELEASE.md

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Current version (not yet released; still in development)

Major Features and Improvements

Bug fixes and other changes

Breaking changes

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