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
User Documentation
Please see the
TFX User Guide.
Development References
Roadmap
The TFX Roadmap,
which is updated quarterly.
Release Details
For detailed previous and upcoming changes, please
check here
Requests For Comment
For designs, we started to publish
RFCs under the
Tensorflow community.
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