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Simplify build process: CUDA support now included by default

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Since pyproject.toml is configured to use PyTorch CUDA index by default,
all builds automatically include CUDA support. Removed redundant separate
CUDA build scripts and updated documentation.

Changes:
- Removed build-cuda.sh and build-cuda.bat (no longer needed)
- Updated build.sh and build.bat to include CUDA by default
  - Added "uv sync" step to ensure CUDA PyTorch is installed
  - Updated messages to clarify CUDA support is included
- Updated BUILD.md to reflect simplified build process
  - Removed separate CUDA build sections
  - Clarified all builds include CUDA support
  - Updated GPU support section
- Updated CLAUDE.md with simplified build commands

Benefits:
- Simpler build process (one script per platform instead of two)
- Less confusion about which script to use
- All builds work on any system (GPU or CPU)
- Automatic fallback to CPU if no GPU available
- pyproject.toml is single source of truth for dependencies

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jknapp
2025-12-28 19:09:36 -08:00
parent be53f2e962
commit d34d272cf0
6 changed files with 42 additions and 186 deletions
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14
CLAUDE.md
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@@ -64,23 +64,19 @@ uv pip install torch --index-url https://download.pytorch.org/whl/cu121
### Building Executables
```bash
# Linux (CPU-only)
# Linux (includes CUDA support - works on both GPU and CPU systems)
./build.sh
# Linux (with CUDA support - works on both GPU and CPU systems)
./build-cuda.sh
# Windows (CPU-only)
# Windows (includes CUDA support - works on both GPU and CPU systems)
build.bat
# Windows (with CUDA support)
build-cuda.bat
# Manual build with PyInstaller
uv sync # Install dependencies (includes CUDA PyTorch)
uv pip uninstall -q enum34 # Remove incompatible enum34 package
uv run pyinstaller local-transcription.spec
```
**Important:** CUDA builds can be created on systems without NVIDIA GPUs. The PyTorch CUDA runtime is bundled, and the app automatically falls back to CPU if no GPU is available.
**Important:** All builds include CUDA support via `pyproject.toml` configuration. CUDA builds can be created on systems without NVIDIA GPUs. The PyTorch CUDA runtime is bundled, and the app automatically falls back to CPU if no GPU is available.
### Testing
```bash