* Removing .precommit config
* edited colab comments
* studio: update Unsloth_Studio_Colab.ipynb
* studio: update Unsloth_Studio_Colab.ipynb
* studio: add Colab T4 GPU metadata to force T4 instance
* style: update colab popup to black/white theme with gem icon and play button
* feat: center landscape image in colab notebook
* style: shrink popup to fit content, truncate URL display
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: center landscape image in colab notebook
* feat: use GitHub raw URL for studio landscape image in notebook
* chore: update colab notebook
* feat: add studio landscape colab display image and update notebook
* feat: update notebook with studio landscape image
* style: remove colors, add progress bar, add VERBOSE flag to install output
* docs: add comments explaining VERBOSE flag and progress bar
* chore: update colab notebook
* fix: define VERBOSE, _STEP, _TOTAL at module level to fix NameError
---------
Co-authored-by: LeoBorcherding <LeoBorcherding@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The `set -u` (nounset) flag in setup.sh causes `${_HIDDEN_GITIGNORES[@]}`
to fail with "unbound variable" when no parent .gitignore with `*` is
found (common on Mac where the install is not inside a Python venv).
Use the `${arr[@]+"${arr[@]}"}` idiom to safely expand empty arrays
under nounset mode.
Two issues caused the studio frontend to render without any styling
when installed via `pip install` (non-editable):
1. `pyproject.toml` package-data only included `frontend/dist/**/*`.
The `include-package-data = true` setting relies on `git ls-files`,
which fails in isolated builds (pip/uv copy source to a temp dir
without `.git`). This meant `frontend/src/`, `package.json`,
`vite.config.ts`, and other build files were missing from the
installed package. Tailwind had no source files to scan.
2. Python venvs auto-create a `.gitignore` with a bare `*` pattern.
Tailwind v4's oxide scanner walks parent directories and respects
`.gitignore` -- so even when source files are present, the venv's
`*` pattern causes the scanner to skip all `.tsx` files. The result
is a 34KB CSS skeleton with zero utility classes instead of the
expected 265KB.
Additionally, Vite adds `crossorigin` to script/link tags by default.
This forces CORS mode on font subresource loads, which Firefox
HTTPS-Only Mode does not exempt -- causing all @font-face downloads
to fail silently when Studio is served over HTTP.
Changes:
- pyproject.toml: Expand package-data to include frontend source,
config files, setup scripts, and backend requirements using glob
patterns (no node_modules)
- studio/setup.sh: Temporarily hide parent .gitignore files containing
a bare `*` during `npm run build`, with trap-based restoration
- studio/backend/main.py: Strip `crossorigin` attributes from HTML
at serve time so fonts load correctly on any protocol
install_python_stack.py:
- Print uv error output on failure for debuggability
- Refactor pip_install() to use early return after uv success,
removing duplicated pip command path
setup.sh:
- Guard nvidia-smi command substitution with || true so it does
not abort the script under set -euo pipefail when nvidia-smi
fails (e.g., containerized environments, driver quirks)
- Read all GPU compute capabilities and deduplicate, so
mixed-GPU hosts get kernels built for all present architectures
instead of only the first GPU
Restore separate cmake --build calls for llama-server and
llama-quantize on both setup.sh and setup.ps1. The combined
approach made llama-quantize failure fatal, but it was originally
best-effort (|| true on Linux, [WARN] on Windows). The timing
savings from combining was only ~2.7s, not worth the semantic
change.
The Ninja + arch detection speedups are preserved (55s vs 1m 37s).
Three improvements to the llama.cpp build step in setup.sh:
1. Detect GPU compute capability via nvidia-smi and limit
CMAKE_CUDA_ARCHITECTURES to the current GPU. Without this, cmake
builds for all default CUDA architectures which is very slow.
2. Use Ninja build generator when available. Ninja has better
parallelism than Make for CUDA compilation.
3. Build both llama-server and llama-quantize targets in a single
cmake --build invocation for better parallelism.
4. Add --threads=0 to CMAKE_CUDA_FLAGS for multi-threaded nvcc
compilation.
Measured on 192-core machine with B200 (sm_100):
Make (all archs): very slow (minutes for each arch)
Make (single arch): 1m 37s
Ninja (single arch): 55s
Speedup: ~1.7x
Combined with the uv change, total setup goes from ~4m 35s to ~1m 40s.
