Notebook dependency coverage (base Dockerfile):
- Bake omegaconf, einx, librosa, decord, ftfy so the TTS/STT and vision
notebooks stop dying on a silent No module named X. Installed in the
notebook-deps layer (after the torch/vLLM resolve) with an assertion that
the resolve did not move torch 2.10.0 / numpy>=2.3 / numba>=0.65.
Image size (no functional change):
- Base: prune npp to the two libs torchcodec actually dlopens
(libnppicc + libnppc), drop link-time-only .a archives and the nvshmem
device bitcode. Headers (torch/include etc) are kept so causal-conv1d /
mamba-ssm still build at notebook time with --no-build-isolation.
- Studio: pin the Studio venv to Python 3.12 (matches base) so its
nvidia-*-cu12 wheels are byte-identical to the base venv's, then symlink
the heavy arch-independent CUDA libs (cudnn/cublas/nccl/...) into the base
venv copy. cuda_nvrtc and cuda_runtime are excluded (the arm64 nvrtc swap
mutates nvrtc in place). Also remove the build-only frontend node_modules
(runtime serves the committed dist). Studio image drops ~4.8GB.
Per-notebook transformers version, run notebooks unchanged:
- Bake coherent transformers sidecars (4.57.6 default + 5.3.0/5.5.0/5.10.2),
each transformers==X with its matched huggingface_hub/tokenizers/
safetensors installed --no-deps into its own dir. Companion versions are
resolved at build time so they satisfy each transformers' requirements.
- unsloth_nb_compat.py: pick the sidecar from the notebook's pin or the
model name and activate it (prepend to sys.path) before any ML import,
without touching the base cu128 torch/vLLM/unsloth stack.
- pip/uv shim on PATH: a notebook install cell becomes safe and idempotent
inside a kernel (keeps the baked stack, records the requested transformers
for its sidecar); passthrough to the real tool everywhere else.
- IPython startup hook for manual JupyterLab, and unsloth-run for the
headless driven path.
