Opt-in overlays under docker/ that pass the host GPU into the odysseus
container. Pick one in .env:
COMPOSE_FILE=docker-compose.yml:docker/gpu.nvidia.yml
COMPOSE_FILE=docker-compose.yml:docker/gpu.amd.yml
Non-GPU users are unaffected (no default merge). README now points at
the overlays instead of the old ad-hoc `gpus: all` suggestion.
Each overlay header notes that it only exposes the GPU devices — the
slim image still needs vLLM / llama-cpp-python / etc. installed via
Cookbook -> Dependencies before models can serve on GPU.
Tested on Arch + Docker 29.5.1 + RTX 4090:
docker compose exec odysseus nvidia-smi -L
GPU 0: NVIDIA GeForce RTX 4090 (UUID: GPU-...)
Cookbook hardware scan reports the 24 GB GPU and recommends GPU-fit
models. `docker compose config` validates cleanly for all three
COMPOSE_FILE variants (base, +nvidia, +amd).
Builds on the structure proposed in #91 by @krllus with the path /
docs fixes from the review on that PR.
Closes#163.
Co-authored-by: krllus <krllus@users.noreply.github.com>
When Cookbook installs vllm via `pip install --user vllm`, pip pulls in
nvidia-cuda-* wheels under /app/.local but doesn't set CUDA_HOME or
create /usr/local/cuda. vllm 0.22+ then crashes during engine init:
RuntimeError: Could not find nvcc and default cuda_home='/usr/local/cuda' doesn't exist
After that, the mixed cuda-nvcc 13.3 / cuda-runtime 13.0 wheel combo
fails FlashInfer's JIT sampler with:
error: "CUDA compiler and CUDA toolkit headers are incompatible"
Detect the pip-installed nvcc on startup, point CUDA_HOME at it, and
default VLLM_USE_FLASHINFER_SAMPLER=0 (sampler only, no attention
impact) so the engine boots. No-op when vllm isn't installed.
Fixes#214.
Co-authored-by: sirs <sirs@local>
