This repository is meant as a basic tutorial for Tensorflow Serving deployment on Kubernetes. If you would like to contribute to other solutions around the deployment of Tensorflow Serving, we encourage you to contribute to the Kubeflow project.
For the purpose of improving this tutorial, we'd love to accept contributions to this project if it improves clarity or demonstrates an updated approach to deploying TF serving. There are a few small guidelines you need to follow.
Contributions to this project must be accompanied by a Contributor License Agreement. You (or your employer) retain the copyright to your contribution, this simply gives us permission to use and redistribute your contributions as part of the project. Head over to https://cla.developers.google.com/ to see your current agreements on file or to sign a new one.
You generally only need to submit a CLA once, so if you've already submitted one (even if it was for a different project), you probably don't need to do it again.
All submissions, including submissions by project members, require review. We use GitHub pull requests for this purpose. Consult GitHub Help for more information on using pull requests.
For notebook contributions or edits, after making proper changes, please shut down your notebook editor (e.g. Jupyter, Colab, etc.), and run the python script in the project's base directory as the FINAL STEP to ensure that outputs and other editor-specific metadata are removed. This ensures that diffs between notebooks are clearly marked during code review.
python cleanup_notebooks.py