doc: update range grammar description in GPU virtualization - #792
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📝 WalkthroughWalkthroughThe site updates Docusaurus URL handling in page and theme components. Footer hook initialization is reordered. GPU utilization wording is revised in current and versioned documentation. The ChangesSite URL and theme updates
Supporting maintenance
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to The PR improves the current GPU virtualization documentation but also changes a historical v2.9.0 snapshot that should remain immutable. It is mergeable after the historical-file edit is reverted. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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saiyam1814
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Thanks for cleaning this sentence up. Two small changes are still needed: should be (or simply ), and the changes are unrelated to this one-line docs fix, so they should be reverted.
saiyam1814
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Correction to my previous review text: between 20% to 40% should be between 20% and 40% (or simply 20% to 40%). The package-lock.json changes are also unrelated to this one-line docs fix and should be reverted.
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@saiyam1814 Thank you for the correction. I have made the changes |
mesutoezdil
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good catch, the wording is better. the docs health ci did not run on this pr, please rebase to trigger it.
| In AI inference scenarios, a common dilemma is that GPUs are expensive, but mostly idle. | ||
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| A typical inference service often only uses 20%~40% of the GPU's compute and a small amount of VRAM, leaving the rest idle. Kubernetes' default GPU scheduling model is exclusive: `nvidia.com/gpu: 1` means the entire card is yours, and all other Pods must wait. Want to share a single GPU across multiple inference services? The standard Device Plugin cannot do it, because it can only report device counts (integers) to the scheduler - there is no concept of "VRAM quota." | ||
| A typical inference service often only uses between 20% and 40% of the GPU's compute and a small amount of VRAM, leaving the rest idle. Kubernetes' default GPU scheduling model is exclusive: `nvidia.com/gpu: 1` means the entire card is yours, and all other Pods must wait. Want to share a single GPU across multiple inference services? The standard Device Plugin cannot do it, because it can only report device counts (integers) to the scheduler - there is no concept of "VRAM quota." |
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the exact same sentence exists in versioned_docs/version-v2.9.0/core-concepts/gpu-virtualization.md line 8, which is the default version readers see. apply the fix there too. the zh copy needs no change since the tilde range is natural in chinese, worth noting that in the checklist.
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Thanks for the review @mesutoezdil , the contributing docs advises to edit just docs/, and coderabbit has also just flagged changing the versioned docs, so I reverted the changes
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Actionable comments posted: 1
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Inline comments:
In `@versioned_docs/version-v2.9.0/core-concepts/gpu-virtualization.md`:
- Line 8: Revert the wording change in the v2.9.0 snapshot while retaining the
corrected wording in the unversioned GPU virtualization documentation. Use the
repository’s approved historical-correction process rather than manually editing
or copying files under versioned_docs.
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package.jsonsrc/pages/case-studies.jssrc/pages/index.jssrc/theme/BlogPostItem/Header/index.jssrc/theme/Footer/index.jsversioned_docs/version-v2.9.0/core-concepts/gpu-virtualization.md
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What type of PR is this?
/kind documentation
What this PR does / why we need it:
The previous statement misrepresents how range is written in english,
this changes makes it read better and accurate
Which issue(s) this PR fixes:
N/A
Checklist:
npm run lintandnpm run format:checkpassnpm run buildsucceeds for bothenandzhgit commit -s)Summary by CodeRabbit
Bug Fixes
Documentation