entrypoint.sh: a container started without a GPU request has no
nvidia-smi at all (the toolkit injects it), so the old check 1 reported
'CUDA runtime in this image is broken, re-pull' for the most common user
error. Fold the missing-binary case into the actionable 'No GPU visible'
message and document the CPU-only option (UNSLOTH_ALLOW_CPU=1).
run.sh / test_locally.sh: guard empty-array expansions with the
${arr[@]+...} form; bash 3.2 (macOS /bin/bash) treats "${empty[@]}"
as unbound under set -u, which broke the documented macOS CPU path.
studio_launch.sh: exclude *_TOKEN, *_API_KEY, *_PASSWORD, *_SECRET,
*_LICENSE from the env snapshot written for SSH sessions; secrets stay
in process env only, never on disk.
supervisord.conf / Dockerfile.studio: pin HOME=/root for the studio and
jupyter programs (jupyter would silently fall back to token auth if HOME
were unset), default JUPYTER_PORT and UNSLOTH_ENABLE_SSHD at the image
level so a direct supervisord invocation cannot hit a bad %(ENV_*)s
expansion, and document the root-services decision (non-root parity with
the previous production image is a tracked follow-up).
docker_confirm.ps1: mirror the bash script's GPU selector translation so
GPUS=0 / 0,1 select devices instead of silently using all GPUs.
docker-publish.yml: studio cache scope moves to mode=min; a mode=max
cache of a ~24GB image would evict everything else in the 10GB GHA
quota for no hit-rate gain.
Base image (docker/Dockerfile):
- Install JupyterLab + notebook + ipywidgets in a separate pure-Python uv
pass so the cu128 pin set cannot move; EXPOSE 8888.
- Bake the prebuilt llama.cpp bundle into /opt/unsloth/llama.cpp at the
runtime stage using studio/install_llama_prebuilt.py from the same
UNSLOTH_REF (sha256-verified, portable CUDA bundle since the build host
has no GPU; arm64 resolves the linux-arm64-cuda13 bundle). Export
UNSLOTH_LLAMA_CPP_PATH so unsloth_zoo's save_pretrained_gguf finds it
and never reaches the interactive install prompt or a source build.
- Optional github_token BuildKit secret for the resolver's API calls on
shared CI runner IPs.
Entrypoint: UNSLOTH_ALLOW_CPU=1 degrades a missing GPU to a warning so
Docker Desktop on macOS / Windows-without-WSL2-GPU and plain CPU hosts can
run Jupyter, GGUF tooling and Studio chat; with a GPU visible the normal
pre-flight still runs.
Full image (docker/Dockerfile.studio): now mirrors the production service
set under supervisord - Studio on 8000, JupyterLab on 8888, key-only sshd
on 22 (enabled only when PUBLIC_KEY/SSH_KEY is set). Points Studio's
llama.cpp dir at the baked bundle to skip a duplicate download, accepts
any git ref via fetch+checkout (CI passes commit SHAs), and FROMs a
digest-pinned BASE_IMAGE.
Publish workflow: base image moves to the base-* tag namespace; new
build-studio/merge-studio jobs publish the full image as :latest (hub
parity with the previous production image, which shipped Studio + Jupyter
+ SSH). Studio builds FROM the exact base manifest digest published by the
same run. GPU smoke job now also boots the full image and probes Studio
/api/health and Jupyter /api.
run.sh: UNSLOTH_GPUS=none, UNSLOTH_ALLOW_CPU forwarding, UNSLOTH_PORTS
publish flags, CPU-mode and Jupyter usage examples.
