Installing PyTorch/XLA on a GPU involves several steps, which are outlined below. This process allows you to utilize the XLA compiler with PyTorch on GPU instances.
Step 1: Create a GPU Instance
First, ensure you have access to a GPU instance. This can be a local machine with a GPU or a cloud-based GPU VM. For cloud services like Google Cloud, you can create a GPU VM following their guidelines.Step 2: Install NVIDIA Container Toolkit
If you're using Docker, you'll need to install the NVIDIA Container Toolkit. This involves setting up the NVIDIA repository and installing the toolkit. You can follow NVIDIA's installation guide for this step.Step 3: Pull Docker Image
Pull a Docker image that supports PyTorch/XLA and GPU acceleration. For example, you can use a nightly build image like this:bash
sudo docker pull us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1
Step 4: Build PyTorch and PyTorch/XLA from Source
1. Clone PyTorch Repository:**bash
git clone https://github.com/pytorch/pytorch.git
cd pytorch
2. Install Required Packages:**
Ensure you have `cmake` and `ninja` installed. You can install them using `conda`:
bash
conda install cmake ninja
pip install -r requirements.txt
3. Build PyTorch with CUDA Support:**
bash
USE_CUDA=1 python setup.py install
USE_CUDA=1 python setup.py bdist_wheel
4. Clone PyTorch/XLA Repository:**
bash
git clone https://github.com/pytorch/xla.git
cd xla
5. Install PyTorch/XLA with CUDA Support:**
bash
XLA_CUDA=1 python setup.py install
Step 5: Verify Installation
After installation, verify that PyTorch/XLA is working correctly by running a simple model on the GPU.Additional Notes
- Ensure your `PATH` and `LD_LIBRARY_PATH` environment variables are correctly set up for CUDA.- If you encounter issues, consider using a nightly build or checking the PyTorch/XLA documentation for troubleshooting tips[3][5].
Citations:
[1] https://www.restack.io/p/pytorch-install-answer-xla
[2] https://github.com/googlecolab/colabtools/issues/3481
[3] https://pytorch.org/xla/master/gpu.html
[4] https://stackoverflow.com/questions/63486381/missing-xla-configuration-when-running-pytorch-xla
[5] https://github.com/pytorch/xla
[6] https://www.kaggle.com/code/tanulsingh077/pytorch-xla-understanding-tpu-s-and-xla
[7] https://pytorch.org/xla/release/1.7/index.html
[8] https://stackoverflow.com/questions/76199099/how-to-build-pytorch-xla-from-source-on-windows-11-wsl