magma installation: run with active conda environment. specify CUDA version to install

.ci/docker/common/install_magma_conda.sh 12.4

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(optional) If using torch.compile with inductor/triton, install the matching version of triton

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Docker Image

Using pre-built images

You can also pull a pre-built docker image from Docker Hub and run with docker v23.0+

docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

Building the image yourself

NOTE: Must be built with a Docker version >= 23.0

The Dockerfile is supplied to build images with CUDA 12.1 support and cuDNN v9. You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it unset to use the default, as the Dockerfile uses system Python.

make -f docker.Makefile

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# images are tagged as docker.io/${your_docker_username}/pytorch

You can also pass the CMAKE_VARS="..." environment variable to specify additional CMake variables to be passed to CMake during the build. See setup.py for the list of available variables.

make -f docker.Makefile

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Getting Started

Pointers to get you started:

• Tutorials: get you started with understanding and using PyTorch
• Examples: easy to understand PyTorch code across all domains
• The API Reference
• Glossary

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License

PyTorch has a BSD-style license, as found in the LICENSE file.