58 lines
2.1 KiB
Markdown
58 lines
2.1 KiB
Markdown
# Repo context for agents
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## Remotes
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| Name | URL | Purpose |
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|---|---|---|
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| `gitea` | `https://gitea.cap.nlogn.ir/fazel/spacy-fa-pipeline.git` | Primary self-hosted Gitea |
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## Pushing to Gitea
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Credentials come from `rbw` (Bitwarden CLI) — no token in git config or any tracked file.
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**rbw entry:** `api/gitea_token_AllExceptAdmin`
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> Note: the older `site_farsi` convention used `pass gitea/token_AllExceptAdmin`. That entry
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> exists but its GPG agent is not unlocked on this machine — `pass` blocks for ~60s and
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> returns empty. Use `rbw`.
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The credential helper is set locally in `.git/config` (never committed):
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```
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credential.https://gitea.cap.nlogn.ir.helper=
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!f() { echo username=fazel; echo password=$(rbw get "api/gitea_token_AllExceptAdmin" | head -1); }; f
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```
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**Push command** (bypass proxy — Gitea is on the local network):
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```bash
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NO_PROXY="*" no_proxy="*" http_proxy="" https_proxy="" HTTP_PROXY="" HTTPS_PROXY="" \
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git push gitea main
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```
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If `rbw` is locked, `rbw unlock` first. To rotate: update in the Gitea UI, then
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`rbw edit "api/gitea_token_AllExceptAdmin"`. No git config change needed.
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## What is NOT committed
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`.gitignore` excludes `assets/ corpus/ training/ metrics/ packages/ .venv/`. The repo holds
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only source: configs, scripts, `project.yml`, docs. Everything else is regenerated:
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```bash
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python -m venv .venv && .venv/bin/python -m pip install -r requirements.txt
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.venv/bin/python -m spacy project assets # checksummed downloads
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.venv/bin/python -m spacy project run all # ~2h on 4 CPU cores
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```
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The trained wheel (13 MB) is a build artifact, not source. Publish it to the HF Hub instead —
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see `docs/CONTRIBUTING-GUIDE.md` §2.
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## Environment
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The venv is Python 3.12.2, created from the conda env at `/home/fazel/anaconda3/envs/p12`
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(the anaconda base python is 3.7 and cannot run spaCy 3.8). The IPython kernel available to
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agents is bound to that 3.7 base and will fail on this project — shell out to `.venv/bin/python`.
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GPU (GTX 940MX, 2 GB) is unused: too small for a transformer, not worth the transfer overhead
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for an `sm` pipeline. All training is CPU, `--gpu-id -1`.
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