Community-developed components, examples, and tools for TFXΒΆ
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!
TFX-Addons is available on PyPI for all OS. To install the latest version, run:
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:
tfxa.feast_examplegen.FeastExampleGen(...)
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An ExampleGen component for ingesting datasets from a Feast Feature Store.
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Perform feature selection using various algorithms with this TFX component.
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A TFX component to publish/update ML models to Firebase ML.
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Hugging Face Model Hub. Optionally pushes the application to the Hugging Face Spaces Hub.
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Message Exit Handler Component
Handle the completion or failure of a pipeline by notifying users, including any error messages.
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Client library to inspect content in ML Metadata populated by TFX pipelines.
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The ModelCardGenerator takes dataset statistics, model evaluation, and a pushed model to automatically populate parts of a model card.
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Use Pandas dataframes instead of the standard Transform component for your feature engineering. Processing is distributed using Apache Beam for scalability.
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A TFX component to sample data from examples, using probabilistic estimation.
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Apply user code to a schema produced by the SchemaGen component, and curate it based on domain knowledge.
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Evaluate XGBoost models by extending the standard Evaluator component.