This document covers the cuda.core release process. For other packages: cuda-bindings and cuda-python involve a private repository and are not documented here; cuda-pathfinder is largely automated by the release-cuda-pathfinder.yml workflow.

Each section below provides detailed guidance for a step in the Release Checklist. To start a release, create a new issue from that template and work through it item by item, referring back here as needed.

Create an nvbug to request that SWQA begin post-release validation. Issues identified by that process are typically addressed in a patch release. To find the template, search for a previous release's nvbug (e.g. by title "Release of cuda.core") and create a new bug from the same template.

Example:

Title: Release of cuda.core v0.6.0

Description:

Requesting SWQA validation for the cuda.core v0.6.0 release. Please test the following SW combinations on all listed platforms and report any issues found.

SW Combinations

• cuda.core 0.6.0 / cuda.bindings 12.9 / CTK 12.9 / CUDA 12.9 driver
• cuda.core 0.6.0 / cuda.bindings 13.0 / CTK 13.0 / CUDA 13.0 driver
• cuda.core 0.6.0 / cuda.bindings 13.1 / CTK 13.1 / CUDA 13.1 driver

Platforms

• Linux x86-64
• Linux arm64
• Windows x86-64 (TCC and WDDM)
• WSL

Test Plan

Functional tests as described in the cuda.core test plan.

Release Milestones

• Pre-release QA (this request)
• GitHub release tag and posting
• PyPI wheel upload
• Post-release validation

Update the version, SW combinations (check with the release owner), and platforms as appropriate for each release.

Review cuda_core/pyproject.toml and verify that all dependency requirements are current.

Review every PR included in the release. For each one, check whether new functions, classes, or features were added and whether they have complete docstrings. Add or edit docstrings as needed — touching docstrings and type annotations in code is OK during code freeze.

Write the release notes in cuda_core/docs/source/release/. Look at historical release notes for guidance on format and structure. Balance all entries for length, specificity, tone, and consistency. Highlight a few notable items in the highlights section, keeping their full entries in the appropriate sections below.

Add the new version to cuda_core/docs/nv-versions.json. This file drives the version switcher on the documentation site. Add an entry for the new version after "latest", following the existing pattern. The docs themselves are built and deployed automatically by the release workflow.

Warning: Pushing a tag is a potentially irrevocable action. Be absolutely certain the tag points to the correct commit before pushing.

Tags should be GPG-signed. The tag name format is cuda-core-v<VERSION> (e.g. cuda-core-v0.6.0). The tag must point to a commit on main.

git checkout main
git pull origin main
git tag -s cuda-core-v0.6.0 -m "cuda-core v0.6.0"
git push origin cuda-core-v0.6.0

Pushing the tag triggers a CI run automatically. Monitor it in the Actions tab on GitHub.

• All CI tests should succeed. If any fail, investigate and rerun as needed.
• Note the run ID of the successful tag-triggered run. The release workflow can auto-detect it from the tag, but you can also provide it explicitly.

This is a two-stage process: first publish to TestPyPI, verify, then publish to PyPI.

1. Go to Actions > CI: Release and run the workflow with:

   • Component: cuda-core
   • The release git tag: cuda-core-v0.6.0
   • The GHA run ID that generated validated artifacts: This is the run ID of the successful tag-triggered CI run from the previous step. You can find it in the URL when viewing the run in the Actions tab (e.g. https://github.com/NVIDIA/cuda-python/actions/runs/123456789 — the run ID is 123456789).
   • build-ctk-ver: the cuda.build.version from ci/versions.yml (e.g. 13.1.1)
   • Which wheel index to publish to: testpypi

2. Wait for the workflow to complete.

3. Verify the TestPyPI upload by installing and running tests from a checked-out copy of the repository:

   pip install -i https://test.pypi.org/simple/ \
       --extra-index-url https://pypi.org/simple/ \
       cuda-core==0.6.0
   cd cuda_core/tests && pytest

Once TestPyPI verification passes, rerun the same workflow with:

• Which wheel index to publish to: pypi

After completion, verify:

pip install cuda-core==0.6.0

The conda-forge feedstock builds from the GitHub Release source archive (not from PyPI). There are three approaches to updating the feedstock, from least effort to most control.

The regro-cf-autotick-bot periodically scans for new releases and opens a PR automatically. If nothing has changed in the build requirements, the bot's PR may be sufficient — review it and ask a feedstock maintainer to merge. However, the bot only updates the version and sha256. If build dependencies, import paths, or other recipe fields have changed, the bot's PR will be incomplete and CI will fail.

If the bot hasn't opened a PR, you can request one explicitly. Go to the feedstock's Issues tab and create a new "Bot commands" issue:

• Title: @conda-forge-admin, please update version
• Body: (leave empty)

This triggers the bot to create a version-bump PR. As with approach A, review the PR and push additional fixes if needed.

For full control — or when the bot's PR needs extensive fixes — open a PR manually from a fork.

Fork and clone (one-time setup):

gh repo fork conda-forge/cuda-core-feedstock --clone
cd cuda-core-feedstock

Create a branch and edit recipe/meta.yaml:

git checkout -b update-v0.6.0 origin/main

Update the following fields:

1. version: Set to the new version (e.g. 0.6.0).

2. number (build number): Reset to 0 for a new version.

3. sha256: The SHA-256 of the source archive from the GitHub Release. Download it and compute the hash:

   curl -sL https://github.com/NVIDIA/cuda-python/releases/download/cuda-core-v0.6.0/cuda-python-cuda-core-v0.6.0.tar.gz \
       | sha256sum

4. Host dependencies: Ensure all build-time dependencies are listed. For example, v0.6.0 added a Cython C++ dependency on nvrtc.h, requiring cuda-nvrtc-dev in both host requirements and ignore_run_exports_from.

5. Test commands and descriptions: Update any import paths or descriptions that changed (e.g. cuda.core.experimental -> cuda.core).

Open a PR:

git add recipe/meta.yaml
git commit -m "Update cuda-core to 0.6.0"
git push <your-github-username> update-v0.6.0

gh pr create \
    --repo conda-forge/cuda-core-feedstock \
    --head <your-github-username>:update-v0.6.0 \
    --title "Update cuda-core to 0.6.0" \
    --body "Update cuda-core to version 0.6.0."

The feedstock CI (Azure Pipelines) triggers automatically on the PR. Monitor it for build failures — common issues include missing build-time header dependencies. Feedstock maintainers (listed in recipe/meta.yaml under extra.recipe-maintainers) can merge the PR.

TBD

The release workflow creates a draft GitHub Release. To publish it:

1. Go to the repository on GitHub, click Tags, then switch to the Releases tab.
2. Find the draft release for the new tag and click Edit.
3. Copy the body from a previous release as a starting point. It typically links to the release notes in the documentation (e.g. https://nvidia.github.io/cuda-python/cuda-core/latest/release/0.6.0-notes.html).
4. Update the link and any version references, then click Publish release.

The release owner will prepare and send the announcement.

TBD