3. Promoting RCs to stable and performing release day tasks
## Cutting a release branch preparations
Following Requirements needs to be met prior to final RC Cut:
* Resolve all outstanding issues in the milestones(for example [1.11.0](https://github.com/pytorch/pytorch/milestone/28))before first RC cut is completed. After RC cut is completed following script should be executed from builder repo in order to validate the presence of the fixes in the release branch :
* Validate that all new workflows have been created in the PyTorch and domain libraries included in the release. Validate it against all dimensions of release matrix, including operating systems(Linux, MacOS, Windows), Python versions as well as CPU architectures(x86 and arm) and accelerator versions(CUDA, ROCm).
* All the nighly jobs for pytorch and domain libraries should be green. Validate this using following HUD links:
Release branches are typically cut from the branch [`viable/strict`](https://github.com/pytorch/pytorch/tree/viable/strict) as to ensure that tests are passing on the release branch.
*Note*: Release branches for individual domain libraries should be created after first release candidate build of PyTorch is available in staging channels (which happens about a week after PyTorch release branch has been created). This is absolutely required to allow sufficient testing time for each of the domain library. Domain libraries branch cut is performed by Domain Library POC.
Builder branch cut should be performed at the same time as Pytorch core branch cut. Convenience script can also be used domains as well as `pytorch/builder`
* A release branches should also be created in [`pytorch/xla`](https://github.com/pytorch/xla) and [`pytorch/builder`](https://github.com/pytorch/builder) repos and pinned in `pytorch/pytorch`
Domain library branch cut is done a week after branch cut for the `pytorch/pytorch`. The branch cut is performed by the Domain Library POC.
After the branch cut is performed, the Pytorch Dev Infra memeber should be informed of the branch cut and Domain Library specific change is required before Drafting RC for this domain library.
To draft RCs, a user with the necessary permissions can push a git tag to the main `pytorch/pytorch` git repository. Please note: exactly same process is used for each of the domain library
Pushing a release candidate should trigger the `binary_builds` workflow within CircleCI using [`pytorch/pytorch-probot`](https://github.com/pytorch/pytorch-probot)'s [`trigger-circleci-workflows`](trigger-circleci-workflows) functionality.
This trigger functionality is configured here: [`pytorch-circleci-labels.yml`](https://github.com/pytorch/pytorch/blob/master/.github/pytorch-circleci-labels.yml)
To view the state of the release build, please navigate to [HUD](https://hud.pytorch.org/hud/pytorch/pytorch/release%2F1.12). and make sure all binary builds are successful.
Backups are stored in a non-public S3 bucket at [`s3://pytorch-backup`](https://s3.console.aws.amazon.com/s3/buckets/pytorch-backup?region=us-east-1&tab=objects)
Validate that the documentation build has completed and generated entry corresponding to the release in [docs folder](https://github.com/pytorch/pytorch.github.io/tree/site/docs/) of pytorch.github.io repository
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)
Please note: This PR needs to be merged on the release day and hence it should be absolutely free of any failures. To test this PR, open another test PR but pointing to to the Release candidate location as above [Release Candidate Storage](RELEASE.md#release-candidate-storage)
### Open Google Colab issue
This is normally done right after the release is completed. We would need to create Google Colab Issue see following [PR](https://github.com/googlecolab/colabtools/issues/2372)
A patch release is a maintenance release of PyTorch that includes fixes for regressions found in a previous minor release. Patch releases typically will bump the `patch` version from semver (i.e. `[major].[minor].[patch]`
## Patch Release Criteria
Patch releases should be considered if a regression meets the following criteria:
1. Does the regression break core functionality (stable / beta features) including functionality in first party domain libraries?
2. Triage reviewers will then check if the regression / fix identified fits within above mentioned [Patch Release Criteria](#patch-release-criteria)
3. Triage reviewers will then add the issue / pull request to the related milestone (i.e. `1.9.1`) if the regressions if found to be within the [Patch Release Criteria](#patch-release-criteria)
* 
### Building a release schedule / cherry picking
> Main POC: Patch Release Managers
1. After regressions / fixes have been triaged Patch Release Managers will work together and build /announce a schedule for the patch release
* *NOTE*: Ideally this should be ~2-3 weeks after a regression has been identified to allow other regressions to be identified
2. Patch Release Managers will work with the authors of the regressions / fixes to cherry pick their change into the related release branch (i.e. `release/1.9` for `1.9.1`)
### Building Binaries / Promotion to Stable
> Main POC: Patch Release managers
1. Patch Release Managers will follow the process of [Drafting RCs (Release Candidates)](#drafting-rcs-release-candidates)
2. Patch Release Managers will follow the process of [Promoting RCs to Stable](#promoting-rcs-to-stable)
# Hardware / Software Support in Binary Build Matrix
PyTorch has a support matrix across a couple of different axis. This section should be used as a decision making framework to drive hardware / software support decisions
## Python
For versions of Python that we support we follow the [NEP 29 policy](https://numpy.org/neps/nep-0029-deprecation_policy.html), which was originally drafted by numpy.
### TL;DR
* All minor versions of Python released 42 months prior to the project, and at minimum the two latest minor versions.
* All minor versions of numpy released in the 24 months prior to the project, and at minimum the last three minor versions.
In some instances support for a particular version of software will continue if a need is found. For example, our CUDA 11 binaries do not currently meet
the size restrictions for publishing on PyPI so the default version that is published to PyPI is CUDA 10.2.
These special support cases will be handled on a case by case basis and support may be continued if current PyTorch maintainers feel as though there may still be a
need to support these particular versions of software.
* (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.
