Skip to content
/ tfx Public
forked from tensorflow/tfx

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

License

Notifications You must be signed in to change notification settings

jechague/tfx

This branch is 1 commit ahead of, 4383 commits behind tensorflow/tfx:master.

Folders and files

NameName
Last commit message
Last commit date
May 12, 2020
Jun 30, 2020
Jul 13, 2020
May 12, 2020
Oct 18, 2019
Apr 22, 2020
Jul 1, 2020
Feb 4, 2019
Jan 30, 2020
Jun 15, 2020
Jul 1, 2020
Jun 23, 2020
Jun 29, 2020
Apr 9, 2020
Nov 12, 2019
Apr 28, 2020
Mar 1, 2019
Sep 9, 2019
Jun 15, 2020

Repository files navigation

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

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

TFX is an open-source project and we strongly encourage active participation by the ML community in helping to shape TFX to meet or exceed their needs. An important component of that effort is the RFC process. Please see the listing of current and past TFX RFCs. Please see the TensorFlow Request for Comments (TF-RFC) process page for information on how community members can contribute.

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 apache-beam[gcp] ml-metadata pyarrow tensorflow tensorflow-data-validation tensorflow-metadata tensorflow-model-analysis tensorflow-transform tfx-bsl
GitHub master 2.21.0 0.22.0 0.16.0 nightly (1.x/2.x) 0.22.0 0.22.0 0.22.1 0.22.0 0.22.0
0.22.0 2.21.0 0.22.0 0.16.0 1.15.0 / 2.2.0 0.22.0 0.22.0 0.22.1 0.22.0 0.22.0
0.21.4 2.17.0 0.21.2 0.15.0 1.15.0 / 2.1.0 0.21.5 0.21.1 0.21.5 0.21.2 0.21.4
0.21.3 2.17.0 0.21.2 0.15.0 1.15.0 / 2.1.0 0.21.5 0.21.1 0.21.5 0.21.2 0.21.4
0.21.2 2.17.0 0.21.2 0.15.0 1.15.0 / 2.1.0 0.21.5 0.21.1 0.21.5 0.21.2 0.21.4
0.21.1 2.17.0 0.21.2 0.15.0 1.15.0 / 2.1.0 0.21.4 0.21.1 0.21.4 0.21.2 0.21.3
0.21.0 2.17.0 0.21.0 0.15.0 1.15.0 / 2.1.0 0.21.0 0.21.0 0.21.1 0.21.0 0.21.0
0.15.0 2.16.0 0.15.0 0.15.0 1.15.0 0.15.0 0.15.0 0.15.2 0.15.0 0.15.1
0.14.0 2.14.0 0.14.0 0.14.0 1.14.0 0.14.1 0.14.0 0.14.0 0.14.0 n/a
0.13.0 2.12.0 0.13.2 n/a 1.13.1 0.13.1 0.13.0 0.13.2 0.13.0 n/a
0.12.0 2.10.0 0.13.2 n/a 1.12.0 0.12.0 0.12.1 0.12.1 0.12.0 n/a

About

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

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 94.7%
  • Jupyter Notebook 3.7%
  • Other 1.6%