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Harmonise CI runners with lecture-python.myst (RunsOn T4) once QuantEcon/meta#386 recommends a configuration

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  • ¿Algo no está bien?
#306 1 comentario 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Refactorización
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
aws, github-actions, python
Área
build-system, ci-cd

Línea de trabajo

Start by reading QuantEcon/meta#386 for its runtime recommendation, then compare ci.yml, cache.yml, publish.yml and environment.yml with lecture-python.myst. If GPU is recommended, mirror its runner and JAX setup and validate with a cold cache.yml run, comparing lecture times and outputs against the 2026-10-22 CPU build; if CPU is recommended, record the evidence and close the issue.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

This edition builds on GitHub-hosted ubuntu-latest (CPU), while its English source, QuantEcon/lecture-python.myst, builds on RunsOn g4dn.2xlarge (Tesla T4) with jax[cuda13]. Harmonise the runners with the source, but only after the runtime investigation in QuantEcon/meta#386 recommends a configuration. On 2026-10-19 this edition's CPU cache build ran faster (73 minutes of notebook execution against 107), so moving to the GPU runner is not obviously a gain.

Workflow This repository lecture-python.myst
ci.yml, cache.yml, publish.yml runs-on: ubuntu-latest runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/volume=80gb/spot=false"
JAX CPU jax from environment.yml pip install -U "jax[cuda13]", then scripts/test-jax-install.py and nvidia-smi

Until then the edition stays on CPU. Its outputs can differ from the English site in low-order digits for JAX lectures; that is expected, not a defect.

Tasks

  • QuantEcon/meta#386 concludes with a recommendation for this repository.
  • If it recommends the GPU runner: mirror the source's runner and JAX steps in ci.yml, cache.yml and publish.yml, including XLA_PYTHON_CLIENT_PREALLOCATE=false and whatever CPU pinning the source applies per lecture (see QuantEcon/meta#350); reconcile environment.yml's CPU jax. The push needs the workflow token scope.
  • Validate with a cold cache.yml run: every notebook executes, and the per-lecture times and outputs are compared with the CPU build of 2026-10-22 (39724402846).
  • If it recommends staying on CPU: record the decision and the evidence here, and shut the issue.

Related: #271 (the -W gate and warning-only cache failures, the cause of this edition's recent failed cache runs) is independent of the runner and is better fixed first.

Lenguaje dominante
TeX
Estrellas
5
Forks
4
Merge medio
1 d 21 h
PR fusionados (30 d)
15

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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