{"data":{"slug":"pytorch-pytorch","name":"pytorch","tagline":"Tensors and Dynamic neural networks in Python with strong GPU acceleration","github_url":"https://github.com/pytorch/pytorch","owner":"pytorch","repo":"pytorch","owner_avatar_url":"https://avatars.githubusercontent.com/u/21003710?v=4","primary_language":"Python","stars":102144,"forks":28650,"topics":["autograd","deep-learning","gpu","machine-learning","neural-network","numpy","python","tensor"],"archived":false,"github_pushed_at":"2026-08-03T06:00:50+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/pytorch-pytorch","markdown_url":"https://www.graphcanon.com/tools/pytorch-pytorch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pytorch-pytorch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pytorch-pytorch","description":"Tensors and Dynamic neural networks in Python with strong GPU acceleration","homepage_url":"https://pytorch.org","license":"Other","open_issues":18389,"watchers":1806,"ai_summary":"A machine learning library for Python that includes support for dynamic computation graphs and automatic differentiation. It is designed to be easy to use and has extensive support for using GPUs.","readme_excerpt":"# magma installation: run with active conda environment. specify CUDA version to install\n.ci/docker/common/install_magma_conda.sh 12.4\n\n---\n\n# (optional) If using torch.compile with inductor/triton, install the matching version of triton\n\n---\n\n### Docker Image\n\n#### Using pre-built images\n\nYou can also pull a pre-built docker image from Docker Hub and run with docker v23.0+\n\n```bash\ndocker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest\n```\n\nPlease note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.\nfor multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you\nshould increase shared memory size either with `--ipc=host` or `--shm-size` command line options to `nvidia-docker run`.\n\n#### Building the image yourself\n\n**NOTE:** Must be built with a Docker version >= 23.0\n\nThe Dockerfile is supplied to build images with CUDA 12.1 support and cuDNN v9.\nYou can pass `PYTHON_VERSION=x.y` make variable to specify which Python version is to be used by Miniconda, or leave it\nunset to use the default, as the Dockerfile uses system Python.\n\n```bash\nmake -f docker.Makefile\n\n---\n\n# images are tagged as docker.io/${your_docker_username}/pytorch\n```\n\nYou can also pass the `CMAKE_VARS=\"...\"` environment variable to specify additional CMake variables to be passed to CMake during the build.\nSee [`cmake/EnvVarForwarding.cmake`](./cmake/EnvVarForwarding.cmake) for the list of available variables.\n\n```bash\nmake -f docker.Makefile\n```\n\n---\n\n## Getting Started\n\nPointers to get you started:\n- [Tutorials: get you started with understanding and using PyTorch](https://pytorch.org/tutorials/)\n- [Examples: easy to understand PyTorch code across all domains](https://github.com/pytorch/examples)\n- [The API Reference](https://pytorch.org/docs/)\n- [Glossary](https://github.com/pytorch/pytorch/blob/main/GLOSSARY.md)\n\n---\n\n## License\n\nPyTorch has a BSD-style license, as found in the [LICENSE](LICENSE) file.","github_created_at":"2016-08-13T05:26:41+00:00","created_at":"2026-07-11T23:24:34.3944+00:00","updated_at":"2026-08-03T06:01:44.903251+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":"autograd","name":"autograd"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"gpu","name":"gpu"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"neural-network","name":"neural-network"},{"slug":"numpy","name":"numpy"},{"slug":"python","name":"python"},{"slug":"tensor","name":"tensor"}],"trust":{"provenance":{"is_fork":false,"github_id":65600975,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T06:01:44.012Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":3,"days_since_push":0,"last_release_at":"2026-07-08T17:39:58Z"},"security_summary":{"status":"ok","scanner":"osv@v1","low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:24:37.863Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T06:01:44.451Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-03T06:01:44.451Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-03T06:01:44.451Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-03T06:01:44.451Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-03T06:01:44.451Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-08-03T06:01:44.451Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Required dynamic computation graph functionality for flexible model architectures","GPU-accelerated tensor operations critical due to high-dimensional data"],"when_not_to_use":["Static graph frameworks like TensorFlow are preferred for simpler, less variable models","Environments with limited GPU support or requiring multi-language compatibility"],"source":"enrich:decision_facts","observed_at":"2026-07-12T15:30:32.640Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Dynamic computation graphs with GPU acceleration."}]}}