Practical overview
When it fits
Use PyTorch/XLA when a PyTorch workload needs to run on an XLA device such as a Google TPU and you need the integration package and workflow guidance to adapt it.
Who it is for
- PyTorch developers adapting training or inference workloads to XLA-backed accelerators.
- Teams working with TPU systems that need documented single-process or multi-process training patterns.
Documented uses
- Adapt an existing PyTorch training loop to place models and data on an XLA device.
- Configure a multi-process training example that launches workers and combines gradients across replicas.
- Explore example workloads through the linked Kaggle notebooks for Stable Diffusion and distributed PyTorch/XLA.
Requirements & limits
The project is presented as a public GitHub repository. Its README describes stable TPU VM installation and offers a Kaggle route to try a single Cloud TPU VM.
- The documented TPU installation pairs specific torch and torch_xla versions, so check the repository's current instructions before setup.
- The README shows distinct single-process, multi-process, and SPMD modes; multi-processing is stated to be incompatible with SPMD.
- Custom-kernel users are given separate optional Pallas dependency instructions.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Adapt a PyTorch training loop for a single XLA device
Make the documented changes to an existing training loop so its model and data use an XLA device.
Before you start
- A TPU VM with PyTorch/XLA installed.
- The README's stable-build instructions specify Python 3.8 to 3.11 and pair torch 2.8.0 with torch_xla[tpu] 2.8.0.
Steps
Import torch_xla.
Move the model parameters to the XLA device with model.to('xla').
Inside the training loop, wrap the training work in torch_xla.step() and move inputs and labels to 'xla'.
Call torch_xla.sync() after the loop.
Expected outcome
The documented example adapts the training loop to train the model on a TPU through the XLA device.
Guide limitations
- This recipe covers the single-process example; the README describes that mode as one Python interpreter controlling a single TPU at a time.
- The installation versions are specific to the documented stable-build instructions; check the repository for current instructions.
Adapt an existing loop for multi-process TPU training
Follow the README's multi-process changes for launching workers and combining replica gradients.
Before you start
- A TPU system with multiple devices available to the workers.
- A PyTorch/XLA environment installed according to the repository's TPU instructions.
Steps
Import torch_xla and torch_xla.core.xla_model as xm.
Move the model parameters to the XLA device in the worker function.
Wrap each training iteration in torch_xla.step() and move inputs and labels to 'xla'.
Use xm.optimizer_step(optimizer) in place of optimizer.step(), then launch the worker function with torch_xla.launch.
Expected outcome
The example configures a multi-process PyTorch/XLA training workflow and uses the documented optimizer step to combine gradients across replicas.
Guide limitations
- The README describes this mode as multiple Python interpreters corresponding to the TPUs found on the system.
- Multi-processing is not compatible with SPMD; the repository links to a separate SPMD guide.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- pytorch/xla · Official homepage title · retained in its source language