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PyTorch/XLA

Current CI status: CircleCI

PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU with Google Colab, and use it in production and on Cloud TPU Pods with Google Cloud.

Take a look at one of our Colab notebooks to quickly try different PyTorch networks running on Cloud TPUs and learn how to use Cloud TPUs as PyTorch devices:

The rest of this README covers:

Additional information on PyTorch/XLA, including a description of its semantics and functions, is available at PyTorch.org.

Running PyTorch/XLA on Cloud TPU and GPU

Running on a Single Cloud TPU VM

Google Cloud offers TPU VMs for more transparent and easier access to the TPU hardware. This is our recommedned way of running PyTorch/XLA on Cloud TPU. Please check out our Cloud TPU VM User Guide. To learn more about the Cloud TPU System Architecture, please check out this doc.

How to Run on TPU VM Pods (distributed training)

If a single TPU VM does not suit your requirment, you can consider using TPU Pod. TPU Pod is a collection of TPU devices connected by dedicated high-speed network interfaces. Please checkout our Cloud TPU VM Pod User Guide.

Available docker images and wheels

Docker

The following pre-built docker images are available. For running dockers, check this doc for TPUVM and this doc for GPU.

Version Cloud TPU VMs Docker
1.13 gcr.io/tpu-pytorch/xla:r1.13_3.8_tpuvm
1.12 gcr.io/tpu-pytorch/xla:r1.12_3.8_tpuvm
nightly gcr.io/tpu-pytorch/xla:nightly_3.8_tpuvm
nightly at date gcr.io/tpu-pytorch/xla:nightly_3.8_YYYYMMDD

Version GPU CUDA 11.2 + Python 3.7 Docker
1.13 gcr.io/tpu-pytorch/xla:r1.13_3.7_cuda_11.2
1.12 gcr.io/tpu-pytorch/xla:r1.12_3.7_cuda_11.2

Version GPU CUDA 11.2 + Python 3.8 Docker
1.13 gcr.io/tpu-pytorch/xla:r1.13_3.8_cuda_11.2

Version GPU CUDA 11.7 + Python 3.8 Docker
nightly gcr.io/tpu-pytorch/xla:nightly_3.8_cuda_11.7
nightly at date(>=20230210) gcr.io/tpu-pytorch/xla:nightly_3.8_cuda_11.7_YYYYMMDD

Version GPU CUDA 11.8 + Python 3.8 Docker
nightly gcr.io/tpu-pytorch/xla:nightly_3.8_cuda_11.8
nightly at date(>=20230210) gcr.io/tpu-pytorch/xla:nightly_3.8_cuda_11.8_YYYYMMDD

To run on compute instances with GPUs.

Wheel

Version Cloud TPU VMs Wheel
1.13 https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.13-cp38-cp38-linux_x86_64.whl
1.12 https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.12-cp38-cp38-linux_x86_64.whl
1.11 https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.11-cp38-cp38-linux_x86_64.whl
1.10 https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.10-cp38-cp38-linux_x86_64.whl
1.9 https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.9-cp38-cp38-linux_x86_64.whl
nightly https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-nightly-cp38-cp38-linux_x86_64.whl

Version GPU Wheel + Python 3.7
1.13 https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl
1.12 https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.12-cp37-cp37m-linux_x86_64.whl
1.11 https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.11-cp37-cp37m-linux_x86_64.whl
nightly https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-nightly-cp37-cp37-linux_x86_64.whl

Version GPU Wheel + Python 3.8
1.13 https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp38-cp38-linux_x86_64.whl
nightly https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-nightly-cp38-cp38-linux_x86_64.whl

Version Colab TPU Wheel
1.13 https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl
1.12 https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-1.12-cp37-cp37m-linux_x86_64.whl
1.11 https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-1.11-cp37-cp37m-linux_x86_64.whl

You can also add +yyyymmdd after torch_xla-nightly to get the nightly wheel of a specified date. To get the companion pytorch nightly wheel, replace the torch_xla with torch on above wheel links.

Installing libtpu

For PyTorch/XLA release r1.13 and older and when developing PyTorch/XLA, install the libtpu pip package with the following command:

pip3 install torch_xla[tpuvm]

This is only required on Cloud TPU VMs.

API & Best Practices

In general PyTorch/XLA follows PyTorch APIs, some additional torch_xla specific APIs are available at:

Documentation for the latest release

Documentation for master branch

See the API Guide for best practices when writing networks that run on Cloud TPUs and Cloud TPU Pods.

Performance Profiling and Auto-Metrics Analysis

With PyTorch/XLA we provide a set of performance profiling tooling and auto-metrics analysis which you can check the following resources:

Troubleshooting

If PyTorch/XLA isn't performing as expected, see the troubleshooting guide, which has suggestions for debugging and optimizing your network(s).

Providing Feedback

The PyTorch/XLA team is always happy to hear from users and OSS contributors! The best way to reach out is by filing an issue on this Github. Questions, bug reports, feature requests, build issues, etc. are all welcome!

Contributing

See the contribution guide.

Disclaimer

This repository is jointly operated and maintained by Google, Facebook and a number of individual contributors listed in the CONTRIBUTORS file. For questions directed at Facebook, please send an email to [email protected]. For questions directed at Google, please send an email to [email protected]. For all other questions, please open up an issue in this repository here.