lecture-python.myst: bake env + deploy CLI into the RunsOn GPU AMI
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- Dominant language
- Shell
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Description
#92 Phase 2, the lecture-python.myst (GPU/RunsOn) half of pre-baking: bake miniconda + env + pinned jax[cuda13]/numpyro + the preview-deploy CLI into the RunsOn AMI. The repo stays on RunsOn — CPU migration was evaluated and rejected in PROJECT-OPTIMIZE-PREVIEWS.md. Conda 102s + JAX 55s measured per PR are the target.
- Bake the AMI with an
env_hashdrift guard - Per QuantEcon/infrastructure#4, the baked CLI is now
wrangler, not netlify-cli - Flip
ci.ymltosetup-environment's container-mode path — verify the AMI marker file (/etc/quantecon-container) exists first (Decision 3 of #92) - Staged rollout via
workflow_dispatch
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with ci.yml and the setup-environment container-mode path, then read PROJECT-OPTIMIZE-PREVIEWS.md and the decisions from #92. Verify how the RunsOn AMI is built, including the env_hash drift guard and /etc/quantecon-container marker, then use workflow_dispatch for the staged rollout. Done means the AMI contains the pinned environment and wrangler, and the workflow uses it successfully.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- github-actions, python, shell
- Domain
- ci-cd, cloud, devops, infrastructure
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100