mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-15 04:40:40 -07:00
fix: update package_cuda.py for PyInstaller 6.18 layout and remove split_binary.py
- Fix is_nvidia_file() to match NVIDIA DLLs in _internal/torch/lib/ (PyInstaller 6.18 + torch 2.10 no longer uses nvidia/ subdirectories) - Remove deprecated split_binary.py (both archives are under 2GB) - Update torch_compat range to >=2.6.0,<2.11.0 - Update build docs for new dual-archive packaging flow
This commit is contained in:
Jamie Pine
@@ -212,7 +212,7 @@ jobs:
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backend/dist/voicebox-server-cuda/ \
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--output release-assets/ \
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--cuda-libs-version cu126-v1 \
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--torch-compat ">=2.6.0,<2.8.0"
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--torch-compat ">=2.6.0,<2.11.0"
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- name: Upload archives to GitHub Release
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if: startsWith(github.ref, 'refs/tags/')
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@@ -159,12 +159,14 @@ Tauri looks for `voicebox-server-${PLATFORM}` in `src-tauri/binaries/` and bundl
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The `build-cuda-windows` job runs separately:
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1. Install PyTorch with CUDA 12.1
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2. Build with `build_binary.py --cuda`
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3. Split binary with `scripts/split_binary.py`
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4. Upload parts as release artifacts
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1. Install PyTorch with CUDA 12.6
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2. Build with `build_binary.py --cuda` (produces `--onedir` output)
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3. Package with `scripts/package_cuda.py` into two archives:
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- `voicebox-server-cuda.tar.gz` — server core (~945 MB)
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- `cuda-libs-cu126-v1.tar.gz` — NVIDIA runtime libraries (~1.7 GB, cached independently)
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4. Upload archives as release artifacts
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This binary is downloaded on-demand by users who enable CUDA in settings.
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This binary is downloaded on-demand by users who enable CUDA in settings. The CUDA libs archive is only re-downloaded when the CUDA toolkit version changes, not on every app update.
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## Troubleshooting
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@@ -170,4 +170,4 @@ PyInstaller onedir creates a parent bootloader + child Python process on Windows
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5. Update `main.rs`: change launch path to `backends/cuda/` dir + add `.current_dir()`
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6. Add `ensure_cuda_structure()` helper in Rust to verify exe + nvidia/ subdirs exist before spawning
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7. Update CI pipeline: `build-cuda-windows` produces two archives instead of split parts
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8. Update `split_binary.py` or replace with archive-based distribution
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8. ~~Update `split_binary.py` or replace with archive-based distribution~~ Done: replaced with `package_cuda.py`
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+45
-19
@@ -19,41 +19,67 @@ import sys
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import tarfile
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from pathlib import Path
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# Directories / prefixes that belong in the CUDA libs archive.
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# PyInstaller --onedir puts NVIDIA packages in nvidia/ subdirectories
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# (e.g. nvidia/cublas/lib/, nvidia/cudnn/lib/, etc.)
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NVIDIA_PREFIXES = (
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"nvidia/",
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"nvidia\\",
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)
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# Individual DLL patterns that may end up at the top level on Windows
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# DLL name prefixes that identify NVIDIA CUDA runtime libraries.
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# These DLLs may appear in different locations depending on the torch
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# and PyInstaller version:
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# - nvidia/ subdirectories (older torch with separate nvidia-* packages)
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# - _internal/torch/lib/ (torch 2.10+ bundles NVIDIA DLLs directly)
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# - Top-level directory (some PyInstaller versions)
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NVIDIA_DLL_PREFIXES = (
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"cublas",
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"cublaslt",
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"cudart",
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"cudnn",
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"cufft",
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"cufftw",
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"curand",
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"cusolver",
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"cusolvermg",
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"cusparse",
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"nvjitlink",
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"nvrtc",
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"nccl",
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"caffe2_nvrtc",
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)
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# Files to keep in the server core even if they match NVIDIA prefixes.
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# These are small Python modules or stubs, not the large runtime DLLs.
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NVIDIA_KEEP_IN_CORE = {
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"torch/cuda/nccl.py",
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"torch/_inductor/codegen/cuda/cutlass_lib_extensions/cutlass_mock_imports/cuda/cudart.py",
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}
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def is_nvidia_file(rel_path: str) -> bool:
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"""Check if a relative path belongs to the NVIDIA CUDA libs."""
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"""Check if a relative path belongs to the NVIDIA CUDA libs.
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Identifies large NVIDIA runtime DLLs (.dll/.so) regardless of where
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PyInstaller placed them. Excludes small Python stubs that happen to
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share NVIDIA-related names.
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"""
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rel_lower = rel_path.lower().replace("\\", "/")
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# Files under nvidia/ subdirectory tree
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if rel_lower.startswith("nvidia/"):
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return True
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# Never split out Python source files or small stubs
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if rel_lower in NVIDIA_KEEP_IN_CORE:
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return False
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# Top-level NVIDIA DLLs (Windows) — e.g. cublas64_12.dll
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name = rel_lower.rsplit("/", 1)[-1]
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for prefix in NVIDIA_DLL_PREFIXES:
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if name.startswith(prefix) and (name.endswith(".dll") or name.endswith(".so")):
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# Files under nvidia/ subdirectory tree (older torch layout)
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if rel_lower.startswith("nvidia/") or "nvidia/" in rel_lower.split("/", 1)[-1:]:
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# Only DLLs/shared objects — not .py, .dist-info, etc.
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if rel_lower.endswith((".dll", ".so")):
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return True
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# Include entire nvidia/ namespace package tree
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for part in rel_lower.split("/"):
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if part == "nvidia":
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return True
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# NVIDIA DLLs anywhere in the tree (e.g. _internal/torch/lib/cublas64_12.dll)
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name = rel_lower.rsplit("/", 1)[-1]
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if name.endswith(".dll") or name.endswith(".so"):
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name_no_ext = name.rsplit(".", 1)[0]
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for prefix in NVIDIA_DLL_PREFIXES:
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if name_no_ext.startswith(prefix):
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return True
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return False
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@@ -186,8 +212,8 @@ def main():
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parser.add_argument(
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"--torch-compat",
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type=str,
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default=">=2.6.0,<2.8.0",
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help="Torch version compatibility range (default: >=2.6.0,<2.8.0)",
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default=">=2.6.0,<2.11.0",
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help="Torch version compatibility range (default: >=2.6.0,<2.11.0)",
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)
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args = parser.parse_args()
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@@ -1,88 +0,0 @@
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"""
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Split a large binary into chunks for GitHub Releases (<2 GB each).
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DEPRECATED: For CUDA builds, use scripts/package_cuda.py instead.
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This script was used when the CUDA binary was built with --onefile and
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needed to be split into parts for the 2GB GitHub Release asset limit.
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With the switch to --onedir + dual archives (server core + CUDA libs),
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package_cuda.py handles the packaging.
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Usage:
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python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe
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python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe --chunk-size 1900000000
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python scripts/split_binary.py backend/dist/voicebox-server-cuda.exe --output release-assets/
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The script produces:
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- voicebox-server-cuda.part00.exe, .part01.exe, ... (binary chunks)
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- voicebox-server-cuda.sha256 (SHA-256 checksum of the complete file)
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- voicebox-server-cuda.manifest (ordered list of part filenames)
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"""
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import argparse
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import hashlib
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import sys
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from pathlib import Path
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def split(input_path: Path, chunk_size: int, output_dir: Path):
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output_dir.mkdir(parents=True, exist_ok=True)
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data = input_path.read_bytes()
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total_size = len(data)
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# Write SHA-256 of the complete file
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sha256 = hashlib.sha256(data).hexdigest()
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checksum_file = output_dir / f"{input_path.stem}.sha256"
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checksum_file.write_text(f"{sha256} {input_path.name}\n")
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# Split into chunks
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parts = []
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for i in range(0, total_size, chunk_size):
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part_index = len(parts)
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part_name = f"{input_path.stem}.part{part_index:02d}{input_path.suffix}"
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part_path = output_dir / part_name
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part_path.write_bytes(data[i : i + chunk_size])
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parts.append(part_name)
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# Write manifest (ordered list of part filenames)
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manifest_file = output_dir / f"{input_path.stem}.manifest"
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manifest_file.write_text("\n".join(parts) + "\n")
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print(f"Input: {input_path} ({total_size / (1024**3):.2f} GB)")
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print(f"Output: {output_dir}/")
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print(f"Parts: {len(parts)} (chunk size: {chunk_size / (1024**3):.2f} GB)")
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print(f"SHA-256: {sha256}")
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print(f"Manifest: {manifest_file.name}")
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for p in parts:
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size = (output_dir / p).stat().st_size
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print(f" {p} ({size / (1024**3):.2f} GB)")
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def main():
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parser = argparse.ArgumentParser(
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description="Split a large binary into chunks for GitHub Releases"
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)
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parser.add_argument("input", type=Path, help="Path to the binary file to split")
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parser.add_argument(
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"--chunk-size",
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type=int,
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default=1_900_000_000, # 1.9 GB — safely under 2 GB GitHub limit
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help="Maximum chunk size in bytes (default: 1.9 GB)",
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)
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parser.add_argument(
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"--output",
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type=Path,
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default=None,
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help="Output directory (default: same directory as input)",
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)
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args = parser.parse_args()
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if not args.input.exists():
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print(f"Error: {args.input} does not exist", file=sys.stderr)
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sys.exit(1)
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output_dir = args.output or args.input.parent
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split(args.input, args.chunk_size, output_dir)
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if __name__ == "__main__":
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main()
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