Community-developed components, examples, and tools for TFX

TFX-Addons is available on PyPI for all OS. To install the latest version, run:

pip install tfx-addons

You can then use TFX-Addons like this:

from tfx import v1 as tfx
import tfx_addons as tfxa

# Then you can easily load projects tfxa.{project_name}. For example:

Developers helping developers. TFX-Addons is a collection of community projects to build new components, examples, libraries, and tools for TFX. The projects are organized under the auspices of the special interest group, SIG TFX-Addons.

Join the community and share your work with the world!

Perform feature selection using various algorithms with this TFX component.

A TFX component to publish/update ML models to Firebase ML.

Pushes a blessed model to the Hugging Face Model Hub. Optionally pushes the application to the Hugging Face Spaces Hub.

Handle the completion or failure of a pipeline by notifying users, including any error messages.

Client library to inspect content in ML Metadata populated by TFX pipelines.

The ModelCardGenerator takes dataset statistics, model evaluation, and a pushed model to automatically populate parts of a model card.

Use Pandas dataframes instead of the standard Transform component for your feature engineering. Processing is distributed using Apache Beam for scalability.

A TFX component to sample data from examples, using probabilistic estimation.

Apply user code to a schema produced by the SchemaGen component, and curate it based on domain knowledge.