Three fixes based on review:
1. Make uv truly optional: _bootstrap_uv() now only checks if uv is
already on PATH. It no longer tries to pip install uv. If uv is
not present, pip is used with zero changes to behavior.
2. Add --system flag for Colab: on Colab there is no venv (packages
install into system Python). uv requires --system in this case,
otherwise it errors with "No virtual environment found". Added
_in_virtualenv() check that detects VIRTUAL_ENV, sys.real_prefix,
or sys.base_prefix != sys.prefix.
3. Fix label printed twice on uv fallback: when uv fails and falls
back to pip, the label now says "(pip)" to distinguish from the
initial uv attempt, instead of printing the same label twice.
Tested:
- venv path: no --system flag, uv installs correctly
- no-venv path (Colab sim): --system flag added automatically
- full unsloth studio setup + training run (Llama-3.2-1B, 10 steps)
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
Replace pip with uv in install_python_stack.py to speed up the Python
dependency installation phase of `unsloth studio setup`.
- Add _bootstrap_uv() that checks for uv on PATH, and if not found,
installs it via pip. Falls back to pip if uv is unavailable.
- Translate pip flags to uv equivalents (--no-cache-dir dropped since
uv caching is fast, --force-reinstall becomes --reinstall).
- Add --torch-backend=auto so uv auto-detects CUDA version for
PyTorch ecosystem packages.
- Per-install fallback: if any uv install step fails, it retries that
step with pip before exiting.
Measured on clean venv setup:
Python packages (pip): 2m 28s
Python packages (uv): 18s
Speedup: ~8x
Total setup time goes from ~4m 35s to ~2m 30s (llama.cpp build is
now the bottleneck at 1m 40s).
Editable installs (-e) work via a .pth file that is only processed at
Python startup. In Colab the kernel is already running when setup.sh
installs the plugin, so the .pth file never gets picked up and
data_designer_unstructured_seed is not importable.
Remove -e so pip copies the package files directly into site-packages,
which the live kernel can find immediately. Local venv installs are
unaffected since the venv is always created fresh before install.
