Heavy training stack (torch, transformers, peft, trl, datasets,
bitsandbytes, accelerate) moves out of the core install into a new
[train] optional-dependency extra. `pip install soup-cli` is now a
light CLI + data-tools install with no PyTorch; `pip install
'soup-cli[train]'` adds the training stack.
- pyproject: new [train] + [all] extras; [dev] self-references [train]
so CI (`pip install -e ".[dev]"`) still gets torch. Pins unchanged.
- errors.py: missing torch/transformers/peft/trl/datasets/bitsandbytes/
accelerate now surface a single 'install soup-cli[train]' fix.
- Dockerfile: install soup-cli[train,serve,data,eval] so the GPU image
can still fine-tune.
- README + docs/models.md: split install into light core vs [train].
- CHANGELOG: cut [0.71.0]; bump version 0.70.0 -> 0.71.0.
GHA runners had repeated connection timeouts pulling
ppa.launchpadcontent.net during v0.38.0 GHCR builds (2 reruns failed).
Ubuntu 22.04 ships python3 (3.10) natively, which is in Soup's supported
range (3.9+) — avoids the PPA dependency entirely.
CI-only fix; no version bump (per CLAUDE.md CI-only hotfix policy).
PyPI v0.38.0 unaffected.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Fix image reference in README and docker-compose.yml
(soupcli/soup -> ghcr.io/makazhanalpamys/soup) to match
where the workflow actually publishes
- Remove deprecated 'version: 3.8' from docker-compose.yml
- Use 'docker compose' (v2) instead of legacy 'docker-compose'
- Document in Dockerfile that pip install pulls from PyPI,
not local source
* feat: add Dockerfile with CUDA 12.1 and Python 3.11 for Soup
* feat: add docker-compose.yml with GPU passthrough and volume mounts
* chore: add .dockerignore to exclude caching and local data from image
* ci: add GitHub action to build and publish Docker image to GHCR
* docs: add Docker installation and usage instructions to README
Alpamys
Salil M