XLA bridge for PyTorch

These details have been verified by PyPI

Maintainers

aws-neuron-pypi-admin bhavya01 lsy323 qihan shauheen

These details have not been verified by PyPI

Project links

Homepage

Project description

PyTorch/XLA

Current CI status: GitHub Actions 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:

Installation

TPU

To install PyTorch/XLA stable build in a new TPU VM:

pip install torch~=2.7.0 'torch_xla[tpu]~=2.7.0'

# Optional: if you're using custom kernels, install pallas dependencies
pip install 'torch_xla[pallas]' \
  -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
  -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html

To install PyTorch/XLA nightly build in a new TPU VM:

pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
pip install 'torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev-cp310-cp310-linux_x86_64.whl' \
  -f https://storage.googleapis.com/libtpu-releases/index.html \
  -f https://storage.googleapis.com/libtpu-wheels/index.html

# Optional: if you're using custom kernels, install pallas dependencies
pip install 'torch_xla[pallas]' \
  -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
  -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html

C++11 ABI builds

As of 03/18/2025 and starting from Pytorch/XLA 2.7 release, C++11 ABI builds are the default and we no longer provide wheels built with pre-C++11 ABI.

In Pytorch/XLA 2.6, we'll provide wheels and docker images built with two C++ ABI flavors: C++11 and pre-C++11. Pre-C++11 is the default to align with PyTorch upstream, but C++11 ABI wheels and docker images have better lazy tensor tracing performance.

To install C++11 ABI flavored 2.6 wheels (Python 3.10 example):

pip install torch==2.6.0+cpu.cxx11.abi \
  https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0%2Bcxx11-cp310-cp310-manylinux_2_28_x86_64.whl \
  'torch_xla[tpu]' \
  -f https://storage.googleapis.com/libtpu-releases/index.html \
  -f https://storage.googleapis.com/libtpu-wheels/index.html \
  -f https://download.pytorch.org/whl/torch

The above command works for Python 3.10. We additionally have Python 3.9 and 3.11 wheels:

To access C++11 ABI flavored docker image:

us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.6.0_3.10_tpuvm_cxx11

If your model is tracing bound (e.g. you see that the host CPU is busy tracing the model while TPUs are idle), switching to the C++11 ABI wheels/docker images can improve performance. Mixtral 8x7B benchmarking results on v5p-256, global batch size 1024:

Pre-C++11 ABI MFU: 33%
C++ ABI MFU: 39%

GPU Plugin

PyTorch/XLA now provides GPU support through a plugin package similar to libtpu:

pip install torch~=2.5.0 torch_xla~=2.5.0 https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla_cuda_plugin-2.5.0-py3-none-any.whl

The newest stable version where PyTorch/XLA:GPU wheel is available is torch_xla 2.5. We do not offer a PyTorch/XLA:GPU wheel in the PyTorch/XLA 2.6 release. We understand this is important and plan to reinstate GPU support by the 2.7 release. PyTorch/XLA remains an open-source project and we welcome contributions from the community to help maintain and improve the project. To contribute, please start with the contributors guide.

Github Doc Map

Our github contains many useful docs on working with different aspects of PyTorch XLA, here is a list of useful docs spread around our repository:

docs/source/learn: docs for learning concepts associated with XLA, troubleshooting, pjrt, eager mode, and dynamic shape.
docs/source/accelerators: references to GPU and TPU accelerator documents.
docs/source/perf: documentation about performance specific aspects of PyTorch/XLA such as: AMP, DDP, Dynamo, Fori loop, FSDP, quantization, recompilation, and SPMD
docs/source/features: documentation on distributed torch, pallas, scan, stable hlo, and triton.
docs/source/contribute: documents on setting up PyTorch for development, and guides for lowering operations.
PJRT plugins:
- CPU
- CUDA
torchax/docs: torchax documents
- torchax/examples: torchax examples

Getting Started

To update your existing training loop, make the following changes:

-import torch.multiprocessing as mp
+import torch_xla as xla
+import torch_xla.core.xla_model as xm

 def _mp_fn(index):
   ...

+  # Move the model paramters to your XLA device
+  model.to(xla.device())

   for inputs, labels in train_loader:
+    with xla.step():
+      # Transfer data to the XLA device. This happens asynchronously.
+      inputs, labels = inputs.to(xla.device()), labels.to(xla.device())
       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(optimizer)

 if __name__ == '__main__':
-  mp.spawn(_mp_fn, args=(), nprocs=world_size)
+  # xla.launch automatically selects the correct world size
+  xla.launch(_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 as xla
+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())
+  ddp_model = DDP(model, gradient_as_bucket_view=True)

-  model = model.to(rank)
-  ddp_model = DDP(model, device_ids=[rank])

   for inputs, labels in train_loader:
+    with xla.step():
+      inputs, labels = inputs.to(xla.device()), labels.to(xla.device())
       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)
+  xla.launch(_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 tutorials

Reference implementations

The AI-Hypercomputer/tpu-recipes repo. contains examples for training and serving many LLM and diffusion models.

Available docker images and wheels

Python packages

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 corresponding to your installed torch_xla, install the optional tpu dependencies after installing the main build with

pip install torch_xla[tpu] \
  -f https://storage.googleapis.com/libtpu-wheels/index.html \
  -f https://storage.googleapis.com/libtpu-releases/index.html

GPU and nightly builds are available in our public GCS bucket.

Version	Cloud GPU VM Wheels
2.5 (CUDA 12.1 + Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.1 + Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.1 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl`
nightly (Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.7.0.dev-cp39-cp39-linux_x86_64.whl`
nightly (Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.7.0.dev-cp310-cp310-linux_x86_64.whl`
nightly (Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.7.0.dev-cp311-cp311-linux_x86_64.whl`
nightly (CUDA 12.1 + Python 3.8)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.6.0.dev-cp38-cp38-linux_x86_64.whl`

Use nightly build

You can also add yyyymmdd like torch_xla-2.8.0.devyyyymmdd (or the latest dev version) to get the nightly wheel of a specified date. Here is an example:

pip3 install torch==2.8.0.dev20250318+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250318-cp310-cp310-linux_x86_64.whl

The torch wheel version 2.8.0.dev20250318+cpu can be found at https://download.pytorch.org/whl/nightly/torch/.

older versions

Version	Cloud TPU VMs Wheel
2.6 (Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.5 (Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.4 (Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.4.0-cp310-cp310-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`
2.2 (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`
2.1 (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`

Version	GPU Wheel
2.5 (CUDA 12.1 + Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.1 + Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.1 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp39-cp39-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.5 (CUDA 12.4 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.4/torch_xla-2.5.0-cp311-cp311-manylinux_2_28_x86_64.whl`
2.4 (CUDA 12.1 + Python 3.9)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp39-cp39-manylinux_2_28_x86_64.whl`
2.4 (CUDA 12.1 + Python 3.10)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp310-cp310-manylinux_2_28_x86_64.whl`
2.4 (CUDA 12.1 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp311-cp311-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`
2.3 (CUDA 12.1 + Python 3.11)	`https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.3.0-cp311-cp311-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 + 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`
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`

Docker

Version	Cloud TPU VMs Docker
2.6	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.6.0_3.10_tpuvm`
2.6 (C++11 ABI)	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.6.0_3.10_tpuvm_cxx11`
2.5	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_tpuvm`
2.4	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_tpuvm`
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`
nightly python	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm`
nightly python (C++11 ABI)	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_cxx11`

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 12.4 Docker
2.5	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_cuda_12.4`
2.4	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_cuda_12.4`

Version	GPU CUDA 12.1 Docker
2.5	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.5.0_3.10_cuda_12.1`
2.4	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.4.0_3.10_cuda_12.1`
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	`us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.0_3.8_cuda_11.8`

To run on compute instances with GPUs.

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, Meta and a number of individual contributors listed in the CONTRIBUTORS file. For questions directed at Meta, please send an email to opensource@fb.com. For questions directed at Google, please send an email to pytorch-xla@googlegroups.com. For all other questions, please open up an issue in this repository here.

Additional Reads

You can find additional useful reading materials in

Related Projects

Project details

These details have been verified by PyPI

Maintainers

aws-neuron-pypi-admin bhavya01 lsy323 qihan shauheen

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

2.9.0

Nov 17, 2025

2.8.1

Sep 11, 2025

2.8.0

Aug 8, 2025

This version

2.7.0

Apr 24, 2025

2.6.1

Apr 4, 2025

2.6.0

Jan 30, 2025

2.5.1

Oct 31, 2024

2.5.0

Oct 18, 2024

2.4.0

Jul 25, 2024

2.3.0

Apr 24, 2024

2.2.0

Jan 30, 2024

2.1.0

Oct 4, 2023

2.1.0rc5 pre-release

Sep 19, 2023

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

torch_xla-2.7.0-cp311-cp311-manylinux_2_28_x86_64.whl (96.6 MB view details)

Uploaded Apr 24, 2025 CPython 3.11manylinux: glibc 2.28+ x86-64

torch_xla-2.7.0-cp310-cp310-manylinux_2_28_x86_64.whl (96.5 MB view details)

Uploaded Apr 24, 2025 CPython 3.10manylinux: glibc 2.28+ x86-64

torch_xla-2.7.0-cp39-cp39-manylinux_2_28_x86_64.whl (96.5 MB view details)

Uploaded Apr 24, 2025 CPython 3.9manylinux: glibc 2.28+ x86-64

File details

Details for the file torch_xla-2.7.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

Download URL: torch_xla-2.7.0-cp311-cp311-manylinux_2_28_x86_64.whl
Upload date: Apr 24, 2025
Size: 96.6 MB
Tags: CPython 3.11, manylinux: glibc 2.28+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.10.12

File hashes

Hashes for torch_xla-2.7.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm	Hash digest
SHA256	`1a2b323e107bc58b9605df6194046be21be1f5ea8ef0bb3fa666b21bea49292f`
MD5	`48526a3ff6ee9f2bff2cd9ab1cddccd7`
BLAKE2b-256	`cf3ee0dc52ddf2058359cf088eb58faf928512996085f3fdd9f51e670d97202f`

See more details on using hashes here.

File details

Details for the file torch_xla-2.7.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

Download URL: torch_xla-2.7.0-cp310-cp310-manylinux_2_28_x86_64.whl
Upload date: Apr 24, 2025
Size: 96.5 MB
Tags: CPython 3.10, manylinux: glibc 2.28+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.10.12

File hashes

Hashes for torch_xla-2.7.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm	Hash digest
SHA256	`e318dc781a4a3e06920023783f648ff5b4e09502aef94fe85dd2df7b351b6ef4`
MD5	`9edaabbc19406c1164d38a95eb446f2a`
BLAKE2b-256	`ee6077f14af09a637a4ff459f086ae4fdfba1b05cb8d2f485a0733f96cecc03b`

See more details on using hashes here.

File details

Details for the file torch_xla-2.7.0-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

Download URL: torch_xla-2.7.0-cp39-cp39-manylinux_2_28_x86_64.whl
Upload date: Apr 24, 2025
Size: 96.5 MB
Tags: CPython 3.9, manylinux: glibc 2.28+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.10.12

File hashes

Hashes for torch_xla-2.7.0-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm	Hash digest
SHA256	`c46b7a91cef11ddf7f0554de0abcddf51c2d754714318ca6dbd7f7b70194b611`
MD5	`7910729b205a3a2cb3721f36fdce629b`
BLAKE2b-256	`3255e293117a2642fe8dbf1169f8adcdb90f6fde536f4b0b813116ae7ab312a3`

See more details on using hashes here.

torch-xla 2.7.0

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

PyTorch/XLA

Installation

TPU

C++11 ABI builds

GPU Plugin

Github Doc Map

Getting Started

PyTorch/XLA tutorials

Reference implementations

Available docker images and wheels

Python packages

Use nightly build

Docker

Troubleshooting

Providing Feedback

Contributing

Disclaimer

Additional Reads

Related Projects

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distributions

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes