{"data":{"slug":"huggingface-accelerate","name":"accelerate","tagline":"A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.","github_url":"https://github.com/huggingface/accelerate","owner":"huggingface","repo":"accelerate","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":9885,"forks":1513,"topics":[],"archived":false,"github_pushed_at":"2026-09-25T16:32:10+00:00","maintenance_label":"Very active","stars_delta_30d":82,"url":"https://www.graphcanon.com/tools/huggingface-accelerate","markdown_url":"https://www.graphcanon.com/tools/huggingface-accelerate.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-accelerate","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-accelerate","description":"🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support","homepage_url":"https://huggingface.co/docs/accelerate","license":"Apache-2.0","open_issues":88,"watchers":97,"ai_summary":"🤗 Accelerate provides an easy way to train PyTorch models using automatic mixed-precision techniques and supports FSDP and DeepSpeed configuration. It simplifies model deployment across a variety of hardware setups.","readme_excerpt":"## Installation\n\nThis repository is tested on Python 3.8+ and PyTorch 1.10.0+\n\nYou should install 🤗 Accelerate in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\n\nFirst, create a virtual environment with the version of Python you're going to use and activate it.\n\nThen, you will need to install PyTorch: refer to the [official installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Then 🤗 Accelerate can be installed using pip as follows:\n\n```bash\npip install accelerate\n```","github_created_at":"2020-10-30T13:27:12+00:00","created_at":"2026-07-11T23:22:26.598457+00:00","updated_at":"2026-09-26T06:00:20.164409+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"deepspeed","name":"deepspeed"},{"slug":"fsdp","name":"fsdp"},{"slug":"mixed-precision","name":"mixed precision"},{"slug":"pytorch","name":"pytorch"}],"trust":{"provenance":{"is_fork":false,"github_id":308638334,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-26T06:00:18.789Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":1,"days_since_push":0,"last_release_at":"2026-09-09T13:30:33Z","stars_delta_30d":82,"open_issues_delta_30d":-17},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:22:30.767Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-26T06:00:19.449Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-26T06:00:19.449Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-26T06:00:19.449Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Easy mixed-precision support for PyTorch models","Simplified training across diverse hardware setups","Seamless integration with FSDP, DeepSpeed configurations"],"when_not_to_use":["Non-PyTorch projects do not benefit from this tool","Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow","Limited to Python environments compatible with PyTorch 1.10.0+"],"source":"enrich:decision_facts","observed_at":"2026-07-15T09:49:25.994Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Tool: accelerate"}]}}