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Getting Started on Intel GPU#

Created On: Jun 14, 2024 | Last Updated On: Sep 01, 2025

Hardware Prerequisite#

For Intel Data Center GPU

Device

Red Hat* Enterprise Linux* 9.2

SUSE Linux Enterprise Server* 15 SP5

Ubuntu* Server 22.04 (>= 5.15 LTS kernel)

Intel® Data Center GPU Max Series (CodeName: Ponte Vecchio)

yes

yes

yes

For Intel Client GPU

Supported OS

Validated Hardware

Windows 11 & Ubuntu 24.04/25.04




Intel® Arc A-Series Graphics (CodeName: Alchemist)
Intel® Arc B-Series Graphics (CodeName: Battlemage)
Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (CodeName: Meteor Lake-H)
Intel® Core™ Ultra Desktop Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Lunar Lake)
Intel® Core™ Ultra Mobile Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Arrow Lake-H)

Intel GPUs support (Prototype) is ready from PyTorch* 2.5 for Intel® Client GPUs and Intel® Data Center GPU Max Series on both Linux and Windows, which brings Intel GPUs and the SYCL* software stack into the official PyTorch stack with consistent user experience to embrace more AI application scenarios.

Software Prerequisite#

To use PyTorch on Intel GPUs, you need to install the Intel GPUs driver first. For installation guide, visitIntel GPUs Driver Installation.

Please skip the Intel® Deep Learning Essentials installation section if you install from binaries. For building from source, please refer toPyTorch Installation Prerequisites for Intel GPUs for both Intel GPU Driver and Intel® Deep Learning Essentials Installation.

Installation#

Binaries#

Now that we haveIntel GPU Driver installed, use the following commands to installpytorch,torchvision,torchaudio.

For release wheels

pip3installtorchtorchvisiontorchaudio--index-urlhttps://download.pytorch.org/whl/xpu

For nightly wheels

pip3install--pretorchtorchvisiontorchaudio--index-urlhttps://download.pytorch.org/whl/nightly/xpu

From Source#

Now that we haveIntel GPU Driver and Intel® Deep Learning Essentials installed. Follow guides to buildpytorch,torchvision,torchaudio from source.

Build from source fortorch refer toPyTorch Installation Build from source.

Build from source fortorchvision refer toTorchvision Installation Build from source.

Build from source fortorchaudio refer toTorchaudio Installation Build from source.

Check availability for Intel GPU#

To check if your Intel GPU is available, you would typically use the following code:

importtorchprint(torch.xpu.is_available())# torch.xpu is the API for Intel GPU support

If the output isFalse, double check driver installation for Intel GPUs.

Minimum Code Change#

If you are migrating code fromcuda, you would change references fromcuda toxpu. For example:

# CUDA CODEtensor=torch.tensor([1.0,2.0]).to("cuda")# CODE for Intel GPUtensor=torch.tensor([1.0,2.0]).to("xpu")

The following points outline the support and limitations for PyTorch with Intel GPU:

  1. Both training and inference workflows are supported.

  2. Both eager mode andtorch.compile is supported. The featuretorch.compile is also supported on Windows from PyTorch* 2.7 with Intel GPU, refer toHow to use torch.compile on Windows CPU/XPU.

  3. Data types such as FP32, BF16, FP16, and Automatic Mixed Precision (AMP) are all supported.

Examples#

This section contains usage examples for both inference and training workflows.

Inference Examples#

Here is a few inference workflow examples.

Inference with FP32#

importtorchimporttorchvision.modelsasmodelsmodel=models.resnet50(weights="ResNet50_Weights.DEFAULT")model.eval()data=torch.rand(1,3,224,224)model=model.to("xpu")data=data.to("xpu")withtorch.no_grad():model(data)print("Execution finished")

Inference with AMP#

importtorchimporttorchvision.modelsasmodelsmodel=models.resnet50(weights="ResNet50_Weights.DEFAULT")model.eval()data=torch.rand(1,3,224,224)model=model.to("xpu")data=data.to("xpu")withtorch.no_grad():d=torch.rand(1,3,224,224)d=d.to("xpu")# set dtype=torch.bfloat16 for BF16withtorch.autocast(device_type="xpu",dtype=torch.float16,enabled=True):model(data)print("Execution finished")

Inference withtorch.compile#

importtorchimporttorchvision.modelsasmodelsimporttimemodel=models.resnet50(weights="ResNet50_Weights.DEFAULT")model.eval()data=torch.rand(1,3,224,224)ITERS=10model=model.to("xpu")data=data.to("xpu")foriinrange(ITERS):start=time.time()withtorch.no_grad():model(data)torch.xpu.synchronize()end=time.time()print(f"Inference time before torch.compile for iteration{i}:{(end-start)*1000} ms")model=torch.compile(model)foriinrange(ITERS):start=time.time()withtorch.no_grad():model(data)torch.xpu.synchronize()end=time.time()print(f"Inference time after torch.compile for iteration{i}:{(end-start)*1000} ms")print("Execution finished")

Training Examples#

Here is a few training workflow examples.

Train with FP32#

importtorchimporttorchvisionLR=0.001DOWNLOAD=TrueDATA="datasets/cifar10/"transform=torchvision.transforms.Compose([torchvision.transforms.Resize((224,224)),torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),])train_dataset=torchvision.datasets.CIFAR10(root=DATA,train=True,transform=transform,download=DOWNLOAD,)train_loader=torch.utils.data.DataLoader(dataset=train_dataset,batch_size=128)train_len=len(train_loader)model=torchvision.models.resnet50()criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR,momentum=0.9)model.train()model=model.to("xpu")criterion=criterion.to("xpu")print(f"Initiating training")forbatch_idx,(data,target)inenumerate(train_loader):data=data.to("xpu")target=target.to("xpu")optimizer.zero_grad()output=model(data)loss=criterion(output,target)loss.backward()optimizer.step()if(batch_idx+1)%10==0:iteration_loss=loss.item()print(f"Iteration [{batch_idx+1}/{train_len}], Loss:{iteration_loss:.4f}")torch.save({"model_state_dict":model.state_dict(),"optimizer_state_dict":optimizer.state_dict(),},"checkpoint.pth",)print("Execution finished")

Train with AMP#

Note: Training withGradScaler requires hardware support forFP64.FP64 is not natively supported by the Intel® Arc™ A-Series Graphics. If you run your workloads on Intel® Arc™ A-Series Graphics, please disableGradScaler.

importtorchimporttorchvisionLR=0.001DOWNLOAD=TrueDATA="datasets/cifar10/"use_amp=Truetransform=torchvision.transforms.Compose([torchvision.transforms.Resize((224,224)),torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),])train_dataset=torchvision.datasets.CIFAR10(root=DATA,train=True,transform=transform,download=DOWNLOAD,)train_loader=torch.utils.data.DataLoader(dataset=train_dataset,batch_size=128)train_len=len(train_loader)model=torchvision.models.resnet50()criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR,momentum=0.9)scaler=torch.amp.GradScaler(device="xpu",enabled=use_amp)model.train()model=model.to("xpu")criterion=criterion.to("xpu")print(f"Initiating training")forbatch_idx,(data,target)inenumerate(train_loader):data=data.to("xpu")target=target.to("xpu")# set dtype=torch.bfloat16 for BF16withtorch.autocast(device_type="xpu",dtype=torch.float16,enabled=use_amp):output=model(data)loss=criterion(output,target)scaler.scale(loss).backward()scaler.step(optimizer)scaler.update()optimizer.zero_grad()if(batch_idx+1)%10==0:iteration_loss=loss.item()print(f"Iteration [{batch_idx+1}/{train_len}], Loss:{iteration_loss:.4f}")torch.save({"model_state_dict":model.state_dict(),"optimizer_state_dict":optimizer.state_dict(),},"checkpoint.pth",)print("Execution finished")

Train withtorch.compile#

importtorchimporttorchvisionLR=0.001DOWNLOAD=TrueDATA="datasets/cifar10/"transform=torchvision.transforms.Compose([torchvision.transforms.Resize((224,224)),torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),])train_dataset=torchvision.datasets.CIFAR10(root=DATA,train=True,transform=transform,download=DOWNLOAD,)train_loader=torch.utils.data.DataLoader(dataset=train_dataset,batch_size=128)train_len=len(train_loader)model=torchvision.models.resnet50()criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR,momentum=0.9)model.train()model=model.to("xpu")criterion=criterion.to("xpu")model=torch.compile(model)print(f"Initiating training with torch compile")forbatch_idx,(data,target)inenumerate(train_loader):data=data.to("xpu")target=target.to("xpu")optimizer.zero_grad()output=model(data)loss=criterion(output,target)loss.backward()optimizer.step()if(batch_idx+1)%10==0:iteration_loss=loss.item()print(f"Iteration [{batch_idx+1}/{train_len}], Loss:{iteration_loss:.4f}")torch.save({"model_state_dict":model.state_dict(),"optimizer_state_dict":optimizer.state_dict(),},"checkpoint.pth",)print("Execution finished")