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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.
82 lines
3.7 KiB
Text
82 lines
3.7 KiB
Text
# Full Unsloth image: base training stack + Studio + JupyterLab + sshd.
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#
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# This is the image published as docker.io/unsloth/unsloth:latest. It layers
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# Unsloth Studio on top of the lean base image (Dockerfile, published under
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# the `base` tags) and runs the same service trio as the previous production
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# image: Studio on 8000, JupyterLab on 8888, key-only sshd on 22.
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#
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# Build (local):
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# docker buildx build \
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# --build-arg BASE_IMAGE=unsloth-blackwell:test \
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# -f docker/Dockerfile.studio \
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# -t unsloth-blackwell:studio docker/
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#
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# Run:
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# docker run --rm --gpus all -p 8000:8000 -p 8888:8888 \
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# -v $HOME/.cache/huggingface:/workspace/.cache/huggingface \
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# unsloth-blackwell:studio
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#
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# Open http://localhost:8000 for Studio (first-boot admin password is printed
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# in the container logs and persisted under /opt/unsloth-studio/auth/) and
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# http://localhost:8888 for JupyterLab (password: JUPYTER_PASSWORD env,
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# default `unsloth`). On hosts without GPU passthrough (Docker Desktop on
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# macOS, Windows without WSL2 GPU) add -e UNSLOTH_ALLOW_CPU=1: training is
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# unavailable but Studio chat / Data Recipes / GGUF tooling / Jupyter work.
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#
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# CI pins BASE_IMAGE to the just-published multi-arch base digest so the two
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# images always ship the same stack.
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ARG BASE_IMAGE=unsloth-blackwell:test
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FROM ${BASE_IMAGE}
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# Studio source ref to clone. Defaults to `main`, but a CI publish pipeline
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# that pins BASE_IMAGE to a digest should pin this too (same UNSLOTH_REF as
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# the base) so the published image is reproducible against a known ref.
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ARG UNSLOTH_STUDIO_REF=main
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USER root
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ENV UNSLOTH_STUDIO_HOME=/opt/unsloth-studio \
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DEBIAN_FRONTEND=noninteractive
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# install.sh needs curl + git; supervisor + openssh-server run the service
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# trio. The base image already has python + uv + pip.
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends \
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curl git ca-certificates supervisor openssh-server \
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&& rm -rf /var/lib/apt/lists/*
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# Clone + install Studio into a dedicated venv under $UNSLOTH_STUDIO_HOME.
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# --local makes install.sh use the just-cloned source tree (editable
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# install), so the source dir MUST persist for the venv's `unsloth_cli`
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# entrypoint to keep resolving. Move it under $UNSLOTH_STUDIO_HOME/src
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# (already inside the persistent layer) instead of deleting it. Strip
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# .git to save ~120MB.
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#
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# The llama.cpp symlink BEFORE install.sh points Studio's prebuilt dir at
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# the bundle already baked into the base image (validated, sha256-checked,
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# UNSLOTH_PREBUILT_INFO.json present), so the installer's prebuilt step
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# recognises it and skips a second ~400MB download.
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# fetch+checkout FETCH_HEAD instead of `clone --branch` because the CI
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# pipeline passes a commit SHA as the ref (clone --branch only accepts
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# branch/tag names).
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RUN mkdir -p "${UNSLOTH_STUDIO_HOME}" \
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&& ln -s /opt/unsloth/llama.cpp "${UNSLOTH_STUDIO_HOME}/llama.cpp" \
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&& git init -q "${UNSLOTH_STUDIO_HOME}/src" \
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&& cd "${UNSLOTH_STUDIO_HOME}/src" \
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&& git remote add origin https://github.com/unslothai/unsloth \
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&& git fetch -q --depth 1 origin "${UNSLOTH_STUDIO_REF}" \
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&& git checkout -q FETCH_HEAD \
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&& UNSLOTH_STUDIO_HOME="${UNSLOTH_STUDIO_HOME}" bash install.sh --local \
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&& rm -rf "${UNSLOTH_STUDIO_HOME}/src/.git" /root/.cache
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COPY supervisord.conf /etc/supervisor/supervisord.conf
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COPY studio_launch.sh /usr/local/bin/unsloth-studio-launch
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RUN chmod +x /usr/local/bin/unsloth-studio-launch
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# Studio web UI, JupyterLab, sshd. All bind 0.0.0.0 inside the container's
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# network namespace; the operator publishes them explicitly with -p.
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EXPOSE 8000 8888 22
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# The base ENTRYPOINT (unsloth-entrypoint) still runs its GPU pre-flight
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# first, then hands off to the service launcher.
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CMD ["/usr/local/bin/unsloth-studio-launch"]
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