Fix fresh-clone desktop development by creating the required dist placeholder before Tauri starts, and make source setup install the selected CUDA or ROCm PyTorch stack consistently. Adds behavior-level cross-platform regression coverage.\n\nFixes #1664.\nFixes #1665.\n\nThanks @uberclokr for the contribution.
`scripts/setup.py` prints ✓/⚙ status glyphs. When its stdout is a pipe rather
than an interactive console — which is exactly the case under `bun run
setup:api` and in CI — Windows Python encodes with the cp1252 codepage, which
can't represent those characters, so the script dies with UnicodeEncodeError
mid-setup and takes `bun desktop` down at the setup:api step. It only "works"
interactively by luck of the console encoding.
Reconfigure sys.stdout/sys.stderr to UTF-8 (errors="replace") at startup so the
output is identical whether run interactively or piped. No-op where the streams
already speak UTF-8 (macOS/Linux, modern Windows Terminal) or can't be
reconfigured. Verified: `bun desktop` from a stale terminal now completes
setup:api with piped stdout and launches the app.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(bootstrap): port cuDNN 8 (NVIDIA CUDA GPU) + VC++ redist install into ensure_venv_ready()
* fix(bootstrap): address #869 review — drop dead VC++ half, cache negative CUDA probe, gate on ROCm, sync docs
Per maintainer review on #869:
1. Drop the VC++ Redistributable half: LoadLibraryA("vcruntime140.dll")
from the running Tauri exe is a tautology (the exe itself links the
MSVC CRT, so the process wouldn't be running without it), and torch's
real failure mode is msvcp140.dll inside the venv python process.
Dead code removed; a comment records why for future readers.
2. Stop taxing every non-CUDA launch: a negative torch probe (CPU /
Intel / AMD — most installs) is now cached in a
.venv/.cudnn8_probe_negative marker, so the synchronous `import
torch` runs at most once per venv lifetime. Invalidated on every
path that can change the torch build (drift sync #307, repair sync,
first-run sync, ROCm reinstall) and implicitly by a venv rebuild.
A probe that fails to run cleanly is skipped WITHOUT caching so a
transient error can't wedge a real CUDA machine.
3. Rewrite docs/install/troubleshooting.md §10 to the actual root
cause: packaged installs never had the cudnn8_compat libs (so
reinstalling never restored them); the bootstrap now installs them
automatically on CUDA machines, with the manual uv pip command as
the offline fallback and PyTorch Whisper as the sidestep.
4. Gate the ~700 MB nvidia-cudnn-cu12 download on the venv torch being
a real CUDA build: the probe now reports 'hip' before checking
cuda.is_available() (which HIP spoofs), so opt-in ROCm installs
(#124) never fetch the CUDA wheel.
Also reflow the CHANGELOG entry to house style (bold one-line lead,
1-3 lines of why, (#827, #869) refs) and extend the bootstrap unit
tests: classify_cuda_probe verdict mapping and the marker
write/invalidate round-trip (6 cuDNN tests total, 43 lib tests green).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Co-authored-by: mergetest <test@local>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Parker Hill