Releasing PyTorch

General Overview

Releasing a new version of PyTorch generally entails 3 major steps:

  1. Cutting a release branch and making release branch specific changes
  2. Drafting RCs (Release Candidates), and merging cherry picks
  3. Promoting RCs to stable

Cutting release branches

Release branches are typically cut from the branch viable/strict as to ensure that tests are passing on the release branch.

Release branches should be prefixed like so:

release/{MAJOR}.{MINOR}

An example of this would look like:

release/1.8

Please make sure to create branch that pins divergent point of release branch from the main branch, i.e. orig/release/{MAJOR}.{MINOR}

Making release branch specific changes

These are examples of changes that should be made to release branches so that CI / tooling can function normally on them:

These are examples of changes that should be made to the default branch after a release branch is cut

Getting CI signal on release branches:

Create a PR from release/{MAJOR}.{MINOR} to orig/release/{MAJOR}.{MINOR} in order to start CI testing for cherry-picks into release branch.

Example:

Drafting RCs (Release Candidates)

To draft RCs, a user with the necessary permissions can push a git tag to the main pytorch/pytorch git repository.

The git tag for a release candidate must follow the following format:

v{MAJOR}.{MINOR}.{PATCH}-rc{RC_NUMBER}

An example of this would look like:

v1.8.1-rc1

Pushing a release candidate should trigger the binary_builds workflow within CircleCI using pytorch/pytorch-probot's trigger-circleci-workflows functionality.

This trigger functionality is configured here: pytorch-circleci-labels.yml

Release Candidate Storage

Release candidates are currently stored in the following places:

Backups are stored in a non-public S3 bucket at s3://pytorch-backup

Cherry Picking Fixes

Typically within a release cycle fixes are necessary for regressions, test fixes, etc.

For fixes that are to go into a release after the release branch has been cut we typically employ the use of a cherry pick tracker.

An example of this would look like:

NOTE: The cherry pick process is not an invitation to add new features, it is mainly there to fix regressions

Promoting RCs to Stable

Promotion of RCs to stable is done with this script: pytorch/builder:release/promote.sh

Users of that script should take care to update the versions necessary for the specific packages you are attempting to promote.

Promotion should occur in two steps:

  • Promote S3 artifacts (wheels, libtorch) and Conda packages
  • Promote S3 wheels to PyPI

NOTE: The promotion of wheels to PyPI can only be done once so take caution when attempting to promote wheels to PyPI, (see https://github.com/pypa/warehouse/issues/726 for a discussion on potential draft releases within PyPI)

Special Topics

Updating submodules for a release

In the event a submodule cannot be fast forwarded and a patch must be applied we can take two different approaches:

  • (preferred) Fork the said repository under the pytorch Github organization, apply the patches we need there, and then switch our submodule to accept our fork.
  • Get the dependencies maintainers to support a release branch for us

Editing submodule remotes can be easily done with: (running from the root of the git repository)

git config --file=.gitmodules -e

An example of this process can be found here: