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TFX is an end-to-end platform for deploying production ML pipelines
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README.md

TFX

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

Documentation

Please see the TFX User Guide.

Examples

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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