TFX
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.
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 |