Current CI status: 
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 VM with Kaggle!
Take a look at one of our Kaggle notebooks to get started:
PyTorch/XLA is now on PyPI!
To install PyTorch/XLA a new TPU VM:
pip install torch~=2.3.0 torch_xla[tpu]~=2.3.0 -f https://storage.googleapis.com/libtpu-releases/index.html
To update your existing training loop, make the following changes:
-import torch.multiprocessing as mp
+import torch_xla.core.xla_model as xm
+import torch_xla.distributed.parallel_loader as pl
+import torch_xla.distributed.xla_multiprocessing as xmp
def _mp_fn(index):
...
+ # Move the model paramters to your XLA device
+ model.to(xm.xla_device())
+
+ # MpDeviceLoader preloads data to the XLA device
+ xla_train_loader = pl.MpDeviceLoader(train_loader, xm.xla_device())
- for inputs, labels in train_loader:
+ for inputs, labels in xla_train_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_fn(outputs, labels)
loss.backward()
- optimizer.step()
+
+ # `xm.optimizer_step` combines gradients across replicas
+ xm.optimizer_step()
if __name__ == '__main__':
- mp.spawn(_mp_fn, args=(), nprocs=world_size)
+ # xmp.spawn automatically selects the correct world size
+ xmp.spawn(_mp_fn, args=())If you're using DistributedDataParallel, make the following changes:
import torch.distributed as dist
-import torch.multiprocessing as mp
+import torch_xla.core.xla_model as xm
+import torch_xla.distributed.parallel_loader as pl
+import torch_xla.distributed.xla_multiprocessing as xmp
+import torch_xla.distributed.xla_backend
def _mp_fn(rank):
...
- os.environ['MASTER_ADDR'] = 'localhost'
- os.environ['MASTER_PORT'] = '12355'
- dist.init_process_group("gloo", rank=rank, world_size=world_size)
+ # Rank and world size are inferred from the XLA device runtime
+ dist.init_process_group("xla", init_method='xla://')
+
+ model.to(xm.xla_device())
+ # `gradient_as_bucket_view=True` required for XLA
+ ddp_model = DDP(model, gradient_as_bucket_view=True)
- model = model.to(rank)
- ddp_model = DDP(model, device_ids=[rank])
+ xla_train_loader = pl.MpDeviceLoader(train_loader, xm.xla_device())
- for inputs, labels in train_loader:
+ for inputs, labels in xla_train_loader:
optimizer.zero_grad()
outputs = ddp_model(inputs)
loss = loss_fn(outputs, labels)
loss.backward()
optimizer.step()
if __name__ == '__main__':
- mp.spawn(_mp_fn, args=(), nprocs=world_size)
+ xmp.spawn(_mp_fn, args=())Additional information on PyTorch/XLA, including a description of its semantics and functions, is available at PyTorch.org. See the API Guide for best practices when writing networks that run on XLA devices (TPU, CUDA, CPU and...).
Our comprehensive user guides are available at:
Documentation for the latest release
Documentation for master branch
PyTorch/XLA releases starting with version r2.1 will be available on PyPI. You
can now install the main build with pip install torch_xla. To also install the
Cloud TPU plugin, install the optional tpu dependencies after installing the main build with
pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
GPU and nightly builds are available in our public GCS bucket.
| Version | Cloud TPU/GPU VMs Wheel |
|---|---|
| 2.3 (Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.3.0-cp38-cp38-manylinux_2_28_x86_64.whl |
| 2.3 (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.3.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.3 (CUDA 12.1 + Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp38-cp38-manylinux_2_28_x86_64.whl |
| 2.3 (CUDA 12.1 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| nightly (Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp38-cp38-linux_x86_64.whl |
| nightly (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp310-cp310-linux_x86_64.whl |
| nightly (CUDA 12.1 + Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-nightly-cp38-cp38-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.
older versions
| Version | Cloud TPU VMs Wheel |
|---|---|
| 2.2 (Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl |
| 2.2 (Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.2 (CUDA 12.1 + Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl |
| 2.2 (CUDA 12.1 + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.1 (XRT + Python 3.10) | https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl |
| 2.1 (Python 3.8) | https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.1.0-cp38-cp38-linux_x86_64.whl |
| 2.0 (Python 3.8) | https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-2.0-cp38-cp38-linux_x86_64.whl |
| 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 |
Note: For TPU Pod customers using XRT (our legacy runtime), we have custom
wheels for torch and torch_xla at
https://storage.googleapis.com/tpu-pytorch/wheels/xrt.
| Package | Cloud TPU VMs Wheel (XRT on Pod, Legacy Only) |
|---|---|
| torch_xla | https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl |
| torch | https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch-2.1.0%2Bxrt-cp310-cp310-linux_x86_64.whl |
| Version | GPU Wheel + Python 3.8 |
|---|---|
| 2.1+ CUDA 11.8 | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-2.1.0-cp38-cp38-manylinux_2_28_x86_64.whl |
| 2.0 + CUDA 11.8 | https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-2.0-cp38-cp38-linux_x86_64.whl |
| 2.0 + CUDA 11.7 | https://storage.googleapis.com/tpu-pytorch/wheels/cuda/117/torch_xla-2.0-cp38-cp38-linux_x86_64.whl |
| 1.13 | https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp38-cp38-linux_x86_64.whl |
| nightly + CUDA 12.0 >= 2023/06/27 | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.0/torch_xla-nightly-cp38-cp38-linux_x86_64.whl |
| nightly + CUDA 11.8 <= 2023/04/25 | https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-nightly-cp38-cp38-linux_x86_64.whl |
| nightly + CUDA 11.8 >= 2023/04/25 | https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/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 |
| Version | Colab TPU Wheel |
|---|---|
| 2.0 | https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-2.0-cp310-cp310-linux_x86_64.whl |
| Version | Cloud TPU VMs Docker |
|---|---|
| 2.3 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.3.0_3.10_tpuvm |
| 2.2 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_tpuvm |
| 2.1 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_tpuvm |
| 2.0 | gcr.io/tpu-pytorch/xla:r2.0_3.8_tpuvm |
| 1.13 | gcr.io/tpu-pytorch/xla:r1.13_3.8_tpuvm |
| nightly python | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm |
To use the above dockers, please pass --privileged --net host --shm-size=16G along. Here is an example:
docker run --privileged --net host --shm-size=16G -it us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm /bin/bash| Version | GPU CUDA 12.1 Docker |
|---|---|
| 2.3 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.3.0_3.10_cuda_12.1 |
| 2.2 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_cuda_12.1 |
| 2.1 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_12.1 |
| nightly | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1 |
| nightly at date | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1_YYYYMMDD |
| Version | GPU CUDA 11.8 + Docker |
|---|---|
| 2.1 | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_11.8 |
| 2.0 | gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.8 |
| nightly | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8 |
| nightly at date | us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8_YYYYMMDD |
older versions
| Version | GPU CUDA 11.7 + Docker |
|---|---|
| 2.0 | gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.7 |
| Version | GPU CUDA 11.2 + Docker |
|---|---|
| 1.13 | gcr.io/tpu-pytorch/xla:r1.13_3.8_cuda_11.2 |
| Version | GPU CUDA 11.2 + 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 |
To run on compute instances with GPUs.
If PyTorch/XLA isn't performing as expected, see the troubleshooting guide, which has suggestions for debugging and optimizing your network(s).
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!
See the contribution guide.
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.
You can find additional useful reading materials in