_admonition/gpu.md is present but untranslated (still English)
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Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 78/100
- Issue type
- Documentation
- Clarity
- Clearly specified
- Activity status
- Quiet
- Tech stack
- python
- Domain
- documentation, localization
Research direction
Open lectures/_admonition/gpu.md and compare it with the English source and the translated .zh-cn copy. Translate the admonition body into Persian while preserving the directive header, warning class, Google Colab URL, and literal UI label; verify the result renders correctly in autodiff, jax_intro, and numpy_vs_numba_vs_jax.
Written by the indexing model from the issue text.
Description
lectures/_admonition/gpu.md in this repo is byte-identical to the English source — it was seeded but never translated. It is included by three lectures (autodiff, jax_intro, numpy_vs_numba_vs_jax), so the English GPU admonition renders inside three otherwise-Persian pages.
For comparison, .zh-cn has a properly translated copy, and .fr was missing the file entirely (fixed in .fr#14).
Why this needs its own issue
A strict build will not catch it. The include resolves, so there is no warning and no error — -W (added to this repo's ci.yml in #137) only catches the missing-file case, not the untranslated-content case. Nothing in the current pipeline flags an asset that exists but was never translated.
That is worth noting beyond this one file: shared assets under lectures/_admonition/ sit outside the set that translation sync moves, so they are neither synced nor reviewed. See action-translation#117 for the same class on quant-econ.bib.
Fix
Translate the admonition body into Persian, keeping the directive header and :class: warning option line unchanged, and keeping the Google Colab URL and the literal UI label intact.
Current content:
:class: warning
This lecture was built using a machine with access to a GPU --- although it will also run without one.
[Google Colab](https://colab.research.google.com/) has a free tier with GPUs
that you can access as follows:
1. Click on the "play" icon top right
2. Select Colab
3. Set the runtime environment to include a GPU
Found while enabling strict builds across the programming trio (QuantEcon/project-translation#9).
- Dominant language
- Python
- Stars
- 1
- Forks
- 1
- Avg merge
- 10h 5m
- Merged PRs (30d)
- 5
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