Mirror the public-link convenience Studio already has for its own UI, for
JupyterLab. Off by default; opt in two ways:
docker run -e UNSLOTH_JUPYTER_CLOUDFLARE=1 ... unsloth/unsloth
docker exec <container> unsloth-jupyter-tunnel --force
unsloth-jupyter-tunnel waits for JupyterLab, reuses a cached cloudflared (or
fetches the static binary for the arch, no account needed), and starts a
quick tunnel to the Jupyter port; the https://<name>.trycloudflare.com URL is
printed to docker logs. supervisord runs it as the jupyter-cloudflare program,
autostarted only when UNSLOTH_JUPYTER_CLOUDFLARE=1 (studio_launch.sh exports a
0 default so the autostart gate expands, matching the sshd pattern). JupyterLab
still enforces its password, so the tunnel is not an open door.
Verified: the helper fetches cloudflared and mints a working trycloudflare URL
that reaches JupyterLab (HTTP 200) inside a running container.
- Dockerfile: lift numba past vllm's 0.61.2 pin after the numpy>=2.4
re-upgrade; 0.61.2 refuses numpy 2.3+ at import time and the stack
cannot move numpy down. Verified numba 0.65 + numpy 2.4.6 + vllm
import cleanly together.
- docker-publish.yml: resolve UNSLOTH_ZOO_REF in a step that mirrors
the pushed tag only when the tag exists in unsloth-zoo (the zoo
currently cuts no tags, so blind mirroring broke every tag publish);
falls back to main.
- Dockerfile.studio: Studio venv stays on cu128 for arm64 too, matching
the base venv (cu130 wheels would lift the driver floor to 580+), and
gets the same NVRTC cu13 swap for DGX Spark / GB10 sm_121 support.
- docker_confirm.sh: do not drop to CPU mode when docker info lacks a
nvidia runtime entry; CDI installs and Docker Desktop WSL2 expose
GPUs without one. The phase 3 --gpus probe is now the authority.
- docker_confirm.ps1: GPU selector built as an args array; comma device
lists get version-aware CSV quoting (native arg passing changed in
PowerShell 7.3).
- studio_launch.sh: no fixed Jupyter default password; generate a
random one and print it when JUPYTER_PASSWORD is unset. Env snapshot
for SSH sessions now written via shlex.quote instead of sed so
values with quotes or command substitution cannot break or inject
into /etc/profile.d.
- install.ps1: honour UNSLOTH_TORCH_INDEX_FAMILY like install.sh does.
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.
