⚙️ Disable spot instances on GPU runners (use on-demand)#570
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programming was the only g4dn.2xlarge GPU repo still on RunsOn's default (spot); force on-demand with spot=false in cache.yml and ci.yml, matching QuantEcon/lecture-jax#327 and QuantEcon/lecture-python.myst#936. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Pull request overview
This PR updates the GitHub Actions GPU runner configuration to explicitly request on-demand capacity (instead of the RunsOn default spot behavior), aligning this repo with the other QuantEcon GPU lecture repos.
Changes:
- Add
/spot=falseto the RunsOnruns-onlabel in.github/workflows/ci.yml. - Add
/spot=falseto the RunsOnruns-onlabel in.github/workflows/cache.yml.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
| .github/workflows/ci.yml | Forces on-demand GPU runner for PR preview builds by appending /spot=false to the RunsOn label. |
| .github/workflows/cache.yml | Forces on-demand GPU runner for scheduled/manual cache builds by appending /spot=false to the RunsOn label. |
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Aligns
lecture-python-programmingwith the other GPU repos —lecture-jax(QuantEcon/lecture-jax#327) andlecture-python.myst(QuantEcon/lecture-python.myst#936) — by forcing on-demand GPU runners: addsspot=falseto the RunsOn label incache.ymlandci.yml.This repo was the only one of the three
g4dn.2xlargeGPU repos still relying on RunsOn's default (spot) instead of explicitly forcing on-demand, so spot-interrupted builds were still possible here. This change makes the runner configuration consistent across the GPU repos.Runner sizing / image / volume are unchanged — this only sets
spot=false.Surfaced during the Anaconda 2026.06 audit (see QuantEcon/workspace-lectures#7).