install.ps1 (WoA WSL fallback):
- report failure (non-zero) + restore the rolled-aside venv when the WSL GPU
install fails (torch.cuda absent) or when WSL needs enabling+reboot, instead of
returning success — so -File/Tauri callers don't see a broken install as complete
- on WSL success, Complete-StudioVenvRollback so the previous-venv backup isn't orphaned
- refuse under --tauri with a clear "use the CLI installer" message (the desktop
launcher resolves a Windows-venv backend, which a WSL-only install can't provide)
- reset $LASTEXITCODE before each wsl.exe / python probe (a stale 0 could mark WSL
ready / torch OK if the native command fails to launch)
- torch-availability probe: --reinstall so an already-installed CPU torch in a
migrated venv isn't accepted as "satisfied" (would wrongly skip the WSL path)
- treat a null HKCU PATH as empty (fresh profile) so shim PATH update can't throw
- keep apt stderr visible inside WSL (only stdout -> /dev/null) for diagnosability
scripts/uninstall.ps1:
- scope WSL cleanup to /root (the fallback's install location); stop deleting
/home/*/.unsloth, which could erase an unrelated WSL user's own Unsloth/cache
studio/setup.sh:
- direct (non-install.ps1) WSL installs now provision CUDA llama.cpp themselves
instead of being left with no GGUF server: install.ps1 exports
UNSLOTH_WSL_LLAMA_DEFERRED=1, and the aarch64+NVIDIA provision block runs under
WSL only when that marker is absent
- mark a provisioner-built llama.cpp as Studio-owned in custom-STUDIO_HOME mode so
the next setup's _assert_studio_owned_or_absent doesn't abort
- glibc>=2.41 check: also match a future major>2 (e.g. 3.0)
studio/scripts/provision_llama_cuda.sh:
- install base tools (cmake/git/curl) in their own apt transaction before the
best-effort gcc-14/g++-14 (unavailable on Ubuntu 22.04 / Debian 12, where bundling
them aborted the whole transaction and left no build tools)
- back up an existing (e.g. CPU-only) llama.cpp before the destructive clone and
restore it on clone failure, so a failed clone doesn't leave the user with no server
- honor a pinned llama.cpp ref via UNSLOTH_LLAMA_TAG instead of always tracking main
unsloth/models/_utils.py:
- set PYTORCH_CUDA_ALLOC_CONF (expandable_segments) via a CUDA-free Spark detector
(nvidia-smi, not torch.cuda.get_device_name) so it takes effect before CUDA/the
caching allocator initialize — previously it was a silent no-op on auto-detected Spark
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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