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JavaScript version of PyTorch with CUDA acceleration","maintainers":[{"name":"alex000291","email":"mhy88612345@163.com"}],"readme":"# JsTorch\n\nA JavaScript version of PyTorch with CUDA acceleration for Node.js.\n\n## Features\n\n- 🚀 CUDA-accelerated tensor operations\n- 📦 Native Node.js addon (N-API)\n- 🎯 PyTorch-like API\n- 🔧 Universal binary supporting multiple GPU architectures\n\n## Requirements\n\n- **OS**: Windows x64 (Linux/Mac support coming soon)\n- **Node.js**: >= 18.0.0\n- **CUDA**: 13.0 or higher\n- **GPU**: NVIDIA GPU with compute capability >= 7.5\n  - RTX 20xx/30xx/40xx series\n  - GTX 16xx series\n  - Tesla T4/A100/H100\n  - Quadro RTX series\n\n**Note**: GTX 10xx and older GPUs are not supported (require compute capability < 7.5).\n\n## Installation\n\n```bash\nnpm install @alex000291/jstorch\n```\n\n**Important**: Make sure CUDA runtime DLLs are in your PATH:\n- Add `C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v13.x\\bin` to your system PATH\n- Or ensure `cudart64_XX.dll` is accessible\n\n## Quick Start\n\n```javascript\nimport { torch } from '@alex000291/jstorch';\n\n// Create matrices (row-major order)\nconst a = new Float32Array([\n    1, 2, 3,\n    4, 5, 6\n]); // 2x3 matrix\n\nconst b = new Float32Array([\n    1, 2, 3, 4,\n    5, 6, 7, 8,\n    9, 10, 11, 12\n]); // 3x4 matrix\n\n// Matrix multiplication on GPU: C = A × B\nconst result = torch.matmul(a, b, 2, 3, 4); // (M, K, N)\nconsole.log(result);\n// Float32Array(8) [ 38, 44, 50, 56, 83, 98, 113, 128 ]\n```\n\n## API Reference\n\n### `torch.matmul(a, b, M, K, N)`\n\nPerforms matrix multiplication on GPU: **C = A × B**\n\n**Parameters:**\n- `a` (Float32Array): Matrix A with shape (M, K), stored in row-major order\n- `b` (Float32Array): Matrix B with shape (K, N), stored in row-major order\n- `M` (number): Number of rows in A\n- `K` (number): Number of columns in A / rows in B\n- `N` (number): Number of columns in B\n\n**Returns:**\n- `Float32Array`: Result matrix C with shape (M, N)\n\n**Example:**\n```javascript\n// 2×3 matrix\nconst A = new Float32Array([1, 2, 3, 4, 5, 6]);\n\n// 3×2 matrix\nconst B = new Float32Array([7, 8, 9, 10, 11, 12]);\n\n// Result: 2×2 matrix\nconst C = torch.matmul(A, B, 2, 3, 2);\n```\n\n## Supported GPU Architectures\n\nThe package includes a universal binary that supports:\n- **sm_75**: Turing (RTX 20xx, GTX 16xx, Tesla T4)\n- **sm_80**: Ampere (A100, A30, A40)\n- **sm_86**: Ampere (RTX 30xx, A10, A16)\n- **sm_89**: Ada Lovelace (RTX 40xx, L4, L40)\n- **sm_90**: Hopper (H100, H800)\n- **PTX**: Future architectures (JIT compiled at runtime)\n\nThe CUDA runtime automatically selects the optimal code for your GPU.\n\n## Troubleshooting\n\n### `Error: Cannot find module 'jstorch.node'`\nMake sure the package was installed correctly and `build/win/jstorch.node` exists.\n\n### `Error: the provided PTX was compiled with an unsupported toolchain`\nYour GPU may be too old. JsTorch requires compute capability >= 7.5 (RTX 20xx or newer).\n\n### `Error loading CUDA runtime`\nEnsure CUDA is installed and `cudart64_XX.dll` is in your PATH.\n\n## Roadmap\n\n- [x] Matrix multiplication (matmul)\n- [ ] Tensor class with automatic shape tracking\n- [ ] Element-wise operations (add, mul, div, etc.)\n- [ ] Reduction operations (sum, mean, max, etc.)\n- [ ] Broadcasting support\n- [ ] Autograd / backpropagation\n- [ ] Linux / macOS support\n- [ ] CPU fallback\n\n## Performance\n\nJsTorch uses CUDA for GPU acceleration. Performance depends on:\n- Matrix size (larger matrices benefit more from GPU)\n- GPU model (newer GPUs are faster)\n- Memory transfer overhead (minimize CPU↔GPU transfers)\n\n## Contributing\n\nContributions welcome! Please open an issue or PR on [GitHub](https://github.com/Alex000291/JsTorch).\n\n## License\n\nMIT\n\n## Author\n\nAlex000291\n\n## Links\n\n- [GitHub Repository](https://github.com/Alex000291/JsTorch)\n- [Issue Tracker](https://github.com/Alex000291/JsTorch/issues)\n- [npm Package](https://www.npmjs.com/package/@alex000291/jstorch)\n","readmeFilename":"README.md"}