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TFX is an end-to-end platform for deploying production ML pipelines
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rcrowe-google and tensorflow-extended-team Adding prerequisites and component anatomy
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docs Adding prerequisites and component anatomy Mar 20, 2019
examples update LOCAL_MODEL_DIR location for chicago_taxi_pipeline example. Mar 12, 2019
LICENSE Enable Python3 in TFX This marks the release of version 0.1… Mar 8, 2019
requirements.txt Enable Python3 in TFX This marks the release of version 0.1… Mar 8, 2019


Python PyPI

TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves as well as the integrations with orchestration systems can be extended.

TFX components interact with a ML Metadata backend that keeps a record of component runs, input and output artifacts, and runtime configuration. This metadata backend enables advanced functionality like experiment tracking or warmstarting/resuming ML models from previous runs.

TFX Components


Please see the TFX User Guide.


Compatible versions

The following table describes how the tfx package versions are compatible with its major dependency PyPI packages. This is determined by our testing framework, but other untested combinations may also work.

tfx tensorflow tensorflow-data-validation tensorflow-model-analysis tensorflow-metadata tensorflow-transform ml-metadata apache-beam[gcp]
GitHub master nightly (1.x) 0.13.1 0.13.0 0.13.0 0.13.0 0.13.2 2.11.0
0.12.0 1.12 0.12.0 0.12.1 0.12.1 0.12.0 0.13.2 2.10.0
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