[doc-only] cuda.core: document how to install nightly (top-of-tree) builds#2175
[doc-only] cuda.core: document how to install nightly (top-of-tree) builds#2175matysanchez wants to merge 3 commits into
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…uilds Adds a new section to the cuda.core installation guide explaining how to download and install the latest top-of-tree wheel artifacts published by CI on every push to main, mirroring the workflow used by numba-cuda-mlir. Closes NVIDIA#2166
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@leofang — when you have a moment, would you take a look? No rush. I picked you based on merged 8 docs PRs in last 6 months; happy to address feedback or re-route to someone better if you're not the right person. |
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@mdboom — when you have a moment, would you take a look? No rush. I picked you based on merged 8 docs PRs in last 6 months; happy to address feedback or re-route to someone better if you're not the right person. |
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Please do not ping individual team members. We'll triage PRs during team sync. |
Per @leofang's review on NVIDIA#2175: the nightly/top-of-tree install recipe is a developer concern, not end-user installation. Demote the section from a top-level Installation subsection to the first sub-subsection under Development environment (above 'Development with uv').
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Thanks for the review, @leofang — pushed Also — sincere apology for the cold pings yesterday. I shouldn't have @-mentioned individual team members; I missed the team-triage workflow note. Won't happen again on cuda-python or other NVIDIA repos. Happy to close and resubmit without those comments if you'd prefer, but otherwise I'll just leave it for triage at your team's normal cadence. |
leofang
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Thanks, @matysanchez! If this PR is done, let's un-draft it!
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@leofang thanks Leo |
Closes #2166
What
Adds an Installing the latest nightly (top-of-tree builds) section to
cuda_core/docs/source/install.rst. The section explains how to usegh run downloadto grab the wheel artifact from the most recent successful CI run onmain, with notes for picking your Python version, target platform, and CUDA major version.Why
Per the issue, the equivalent section already exists in numba-cuda-mlir. The two repos share CI infrastructure, so the recipe transfers directly — only repo name, workflow file (
ci.yml), and artifact pattern names needed adapting.Tested
gh run list -R NVIDIA/cuda-python -w ci.yml -b main -s success -L1resolves cleanly against the live repo (returns the latest successful CI run).Verification