spb/tools-spboucher-ai

1{2  "slug": "pytorch",3  "name": "PyTorch",4  "aliases": ["torch", "LibTorch"],5  "vendor": { "name": "PyTorch Foundation (Linux Foundation)", "slug": "pytorch-foundation", "website": "https://pytorch.org" },6  "homepage_url": "https://pytorch.org/",7  "docs_url": "https://docs.pytorch.org/docs/stable/index.html",8  "repo_url": "https://github.com/pytorch/pytorch",9  "license_url": "https://github.com/pytorch/pytorch/blob/main/LICENSE",10  "product_type": "framework",11  "product_status": "active",12  "summary": "Open-source deep-learning framework with eager tensors, autograd, torch.compile and distributed training, governed by the PyTorch Foundation and the base of most LLM tooling",13  "description": "PyTorch is the BSD-3-Clause deep-learning framework originally developed at Facebook AI Research and governed since 2022 by the PyTorch Foundation under the Linux Foundation. It provides GPU-accelerated tensors with automatic differentiation, an eager execution model that behaves like ordinary Python, `torch.compile` for graph capture and kernel fusion, `torch.distributed` for data, tensor and pipeline parallelism, and a LibTorch C++ distribution. Backends include CUDA, ROCm, MPS on Apple Silicon and CPU, and the stable release requires Python 3.10 or later.\n\nMost of the modern LLM stack is built on it: Hugging Face Transformers defines models in PyTorch, vLLM, Unsloth and Axolotl run on it, and Whisper and Sentence Transformers depend on it. The ecosystem adds torchvision, torchaudio, PyTorch Lightning, TorchServe, ExecuTorch for on-device deployment and a landscape of foundation-hosted projects. For researchers it is the default choice because papers and reference implementations target it. Caveats: wheels are large and tied to specific CUDA versions, MPS on Macs lags CUDA in operator coverage and performance, and major releases occasionally deprecate APIs that downstream libraries depend on.",14  "capabilities": ["GPU tensors with automatic differentiation", "Eager execution plus torch.compile", "Distributed training (DDP, FSDP, tensor and pipeline parallel)", "CUDA, ROCm, MPS (Apple Silicon) and CPU backends", "LibTorch C++ API and ExecuTorch for edge", "Large ecosystem: torchvision, torchaudio, Lightning, TorchServe"],15  "workflows": ["Prototype a model in a notebook and scale it with FSDP", "Run Transformers models on an Apple Silicon Mac via MPS", "Export a trained model for serving or on-device inference"],16  "advantages": ["Default framework for research code and LLM libraries", "Vendor-neutral foundation governance", "Permissive BSD license"],17  "limitations": ["Large, CUDA-version-specific wheels", "MPS backend less complete than CUDA", "API deprecations across major versions"],18  "prerequisites": ["Python 3.10+; CUDA or ROCm drivers for GPU use"],19  "audiences": ["ML researchers and students", "Engineers building training and inference systems"],20  "alternatives": ["jax", "mlx", "transformers"],21  "categories": [{ "slug": "model-training", "primary": true }, { "slug": "math-stats-tools", "label": "Array computing and autograd" }],22  "tags": ["open-source", "python", "deep-learning", "gpu", "apple-silicon"],23  "use_cases": ["fine-tune-models", "run-local-models"],24  "operating_systems": ["linux", "macos", "windows"],25  "execution_modes": ["local", "self-hosted", "cloud"],26  "interfaces": ["library"],27  "deployment_models": ["embedded"],28  "integrations": ["Hugging Face Transformers", "vLLM", "PyTorch Lightning", "TorchServe", "ONNX", "CUDA", "ROCm", "Metal (MPS)"],29  "data_formats": ["PyTorch checkpoints (.pt)", "Safetensors", "ONNX (export)"],30  "languages": ["Python", "C++"],31  "source_model": "open-source",32  "license_spdx": "BSD-3-Clause",33  "pricing_model": ["open-source", "free"],34  "self_hostable": true,35  "sources": [36    { "url": "https://pytorch.org/", "type": "official_site", "title": "PyTorch Foundation", "supports": ["name", "summary", "vendor", "prerequisites"], "checked_at": "2026-11-02" },37    { "url": "https://docs.pytorch.org/docs/stable/index.html", "type": "official_docs", "title": "PyTorch documentation", "supports": ["capabilities"], "checked_at": "2026-11-02" },38    { "url": "https://github.com/pytorch/pytorch/blob/main/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02", "note": "Three-clause BSD text with multiple copyright holders" }39  ],40  "confidence": "high",41  "discovery_method": "editorial-research",42  "depth": "standard",43  "editorial_status": "reviewed",44  "visibility": "public",45  "featured": false,46  "author": "batch-A"47}48