[Discussion] TFX Addons Release

[hanneshapke]

Thread to bundle the discussion around TFX Addons releases.

Based on the feedback so far, here are some ideas for TFX Addons:

• Create PyPI project. tfx-addons?
• Clear release instructions in the repo similar to https://github.com/tensorflow/community/blob/master/sigs/addons/RELEASE.md
• Supported TFX versions: 3 most recent TFX releases. We could probably support more version automatically if there aren't any breaking changes
• Let's provide a compatibility matrix similar to https://github.com/tensorflow/addons#python-op-compatibility-matrix
• Supported TF versions: Driven by the TFX versions
• Github actions to build PyPI versions
• Nightly builds (with new commits to main) to tfx-addons-nightly driven by Github Actions?
• Long-term maintenance of components, examples, etc.?

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Feedback from @seanpmorgan (core member for https://github.com/tensorflow/addons)

Best Practices:

• Create a RELEASE.md in your SIG folder. This improves your bus factor and makes it transparent to all what exactly goes on during a release. Here's ours.
• Have more than one person authorized and experienced in doing the release process
• TF-Addons actually does a "nightly" release for each PR that merges. This makes it so we have a lot of trial release runs and we're aware if there's anything broken in the release process. Here you can see us gaiting the uploading of wheels in our CI: https://github.com/tensorflow/addons/blob/master/.github/workflows/release.yml#L109
• Utilize a stored secret in the repo for releases so there's no need to share credentials amongst team members

How do you manage the long-term maintenance?

• Wish I had a better story, but one tool we use is backport. It lets us utilize a bot to quickly create PRs to several release branches but simply tagging the PR with labels

Criteria for cutting new releases

• Since we support custom ops, we need to release with every TF version. The C++ API is not stable so it's only guaranteed to work with the version its compiled against. We try to keep our python compatibility for 3 TF versions.
• You can see our compatibility matrices here:
  https://github.com/tensorflow/addons#python-op-compatibility-matrix
  https://github.com/tensorflow/addons#c-custom-op-compatibility
• How do you maintain version compatibility with the different TF versions or does TF Addons support only a subset of TF versions?
  See above

[seanpmorgan]

Wanted to add another recommendation. We start testing against TF release candidates as soon as they're published so we can iron out any bugs with ~month of runway. TF2.8rc0 just release today:
tensorflow/addons#2635