Can I install PyTorch/XLA on a GPU using a Docker container

1. Ensure NVIDIA Container Toolkit is Installed: First, make sure the NVIDIA Container Toolkit is installed on your host system. This is necessary for Docker to access NVIDIA GPUs[6].

2. Pull the Docker Image: You can use a Docker image that supports PyTorch/XLA. For example, you can pull a nightly build image from Google's repository:

bash
   sudo docker pull us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1
   

This image includes support for CUDA 12.1, which is compatible with many NVIDIA GPUs[1].

3. Run the Docker Container: To run the container interactively with GPU support, use the following command:

bash
   sudo docker run --shm-size=16g --net=host --gpus all -it -d us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1 bin/bash
   

This command allocates shared memory and makes all GPUs available to the container[1].

4. Verify GPU Setup: After logging into the container, you can verify that the GPU is properly set up using the `nvidia-smi` command[1].

5. Build PyTorch/XLA from Source (Optional): If you need to build PyTorch/XLA from source for custom configurations, you can do so inside the Docker container. This step is optional but may be necessary for specific requirements[1].

6. Install PyTorch/XLA Packages: If you prefer to use pre-built packages, you can install them using pip. For example, you can install the PyTorch/XLA package with CUDA support using wheels available in the PyTorch/XLA repository[5].

By following these steps, you can effectively use PyTorch/XLA on a GPU within a Docker container.