pytorch-xpu-setup

name

pytorch-xpu-setup

description

Guide for installing, configuring, and using PyTorch with Intel XPU (GPU) support. Use this skill whenever the user mentions: PyTorch with Intel GPU, XPU, torch.xpu, Intel Arc GPU for deep learning, Intel discrete GPU with PyTorch, installing PyTorch for Intel graphics, XPU device in PyTorch, migrating CUDA code to XPU, Intel oneAPI with PyTorch, or any task involving running PyTorch workloads on Intel GPUs (Arc A-series, B-series, Meteor Lake, Arrow Lake, Lunar Lake, Panther Lake, Data Center GPU Max). Also trigger when the user asks about mixed precision on XPU, torch.compile on XPU, or training/inference on Intel GPUs. Even if they just say "Intel GPU" and "PyTorch" in the same breath, use this skill.

PyTorch XPU Setup & Usage

This skill covers end-to-end setup and usage of PyTorch on Intel GPUs (XPU) on Windows and Linux.

Important Context (as of 2026):

• Intel Extension for PyTorch (IPEX) reached end of life in March 2026. All XPU support is now native in upstream PyTorch.
• There are no conda packages for XPU. Only pip wheels exist from the XPU index URL.
• The recommended workflow: create a conda env → install via pip inside it.

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Quick Reference — Installation

Prerequisites

1. Intel GPU Driver — required for all users.

   • Windows: Download from Intel Arc & Iris Xe Graphics - Windows. Include LevelZeroSDK in the install for torch.compile support.
   • Linux: Follow Client GPU installation instructions.
   • Full prerequisites page: https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-11.html

2. Intel Deep Learning Essentials — only needed if building from source. If installing from pip wheels, the runtime packages are bundled automatically.

Supported Hardware

Platform	Supported GPUs
Windows 11 / Ubuntu 24.04+	Intel Arc A-Series (Alchemist), Arc B-Series (Battlemage), Core Ultra with Arc Graphics (Meteor Lake-H, Arrow Lake-H, Lunar Lake)
Windows 11 / Ubuntu 25.10	Panther Lake
Linux (RHEL 9.2, SUSE 15 SP5, Ubuntu 22.04+)	Intel Data Center GPU Max Series (Ponte Vecchio)

Installation Steps

# 1. Create conda environment
conda create -n pytorch-xpu python=3.11 -y
conda activate pytorch-xpu

# 2. Install PyTorch with XPU support (stable release)
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu

# --- OR for nightly builds ---
pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

# --- OR for a specific version (e.g. 2.10.0) ---
pip install torch==2.10.0 torchvision==0.25.0 torchaudio==2.10.0 --index-url https://download.pytorch.org/whl/xpu

# 3. Verify
python -c "import torch; print(torch.xpu.is_available()); print(torch.xpu.get_device_name(0))"

Python version note: Python 3.10–3.12 are well-tested. Python 3.13 has reported issues with some XPU nightly builds.

Troubleshooting Installation

Symptom	Fix
torch.xpu.is_available() returns False	Update Intel GPU driver to latest version. Verify with sycl-ls (Linux) or Device Manager (Windows).
AssertionError: Torch not compiled with XPU enabled	You installed a non-XPU wheel. Reinstall with --index-url https://download.pytorch.org/whl/xpu.
Import errors about missing DLLs on Windows	Ensure LevelZeroSDK was included during driver install.
torch.compile fails on Windows	Requires PyTorch ≥ 2.7 + MSVC (Desktop Development with C++).

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Quick Reference — Code Usage

The torch.xpu API mirrors torch.cuda almost 1:1. Migrating from CUDA is a find-and-replace of "cuda" → "xpu".

For detailed code patterns (inference, training, AMP, torch.compile, memory management, multi-device, profiling), read the reference file:

→ references/code-patterns.md — Comprehensive code examples for all XPU workflows.

Minimal Example

import torch

device = torch.device("xpu" if torch.xpu.is_available() else "cpu")

model = MyModel().to(device)
data = torch.randn(32, 3, 224, 224).to(device)

with torch.no_grad():
    output = model(data)

Key API Surface

Function	Purpose
torch.xpu.is_available()	Check if XPU device is available
torch.xpu.device_count()	Number of Intel GPU devices
torch.xpu.current_device()	Index of selected device
torch.xpu.get_device_name(i)	Device name string
torch.xpu.synchronize()	Wait for all kernels to finish
torch.xpu.empty_cache()	Free unused cached memory
torch.xpu.memory_allocated()	Current memory usage (bytes)
torch.xpu.max_memory_allocated()	Peak memory usage
torch.xpu.memory_stats()	Dict of allocator statistics
torch.xpu.Stream()	Create a compute stream
torch.xpu.Event()	Create a timing event
torch.autocast(device_type="xpu")	Enable AMP
torch.amp.GradScaler(device="xpu")	Gradient scaling for AMP training

Important Caveats

1. GradScaler & FP64: GradScaler requires FP64 hardware support. Intel Arc A-Series does NOT support FP64 natively. On Arc A-series, use autocast without GradScaler.
2. torch.compile on Windows: Supported from PyTorch 2.7+. Requires MSVC (Visual Studio 2022, Desktop Development with C++ workload).
3. No IPEX needed: Do NOT install intel-extension-for-pytorch for new projects. It is EOL. Use native torch.xpu directly.
4. No conda XPU packages: Always use pip with --index-url https://download.pytorch.org/whl/xpu inside a conda env.

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Official Documentation Links

Resource	URL
torch.xpu API Reference	https://docs.pytorch.org/docs/stable/xpu.html
Getting Started on Intel GPU	https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html
torch.compile on Windows XPU	https://docs.pytorch.org/tutorials/unstable/inductor_windows.html
Prerequisites for Intel GPUs	https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-11.html
Intel GPU Driver (Windows)	https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html
PyTorch Previous Versions	https://pytorch.org/get-started/previous-versions/