《PyTorch 2.5重磅更新：性能優(yōu)化+新特性》中的一個(gè)新特性就是：正式支持在英特爾?獨(dú)立顯卡上訓(xùn)練模型！

PyTorch?2.5
獨(dú)立顯卡類型	支持的操作系統(tǒng)
?Intel??數(shù)據(jù)中心GPU?Max系列	Linux
Intel??Arc?系列	Linux/Windows

本文將在Intel?Core? Ultra 7 155H自帶的Arc?集成顯卡上展示使用Pytorch2.5搭建并訓(xùn)練AI模型的全流程。

一，搭建開(kāi)發(fā)環(huán)境

首先，請(qǐng)安裝顯卡驅(qū)動(dòng)，參考指南：

https://dgpu-docs.intel.com/driver/client/overview.html

然后，請(qǐng)下載并安裝Anaconda，鏈接↓↓

https://www.anaconda.com/download

并用下面的命令創(chuàng)建并激活名為pytorch_arc的虛擬環(huán)境：

conda create -n pytorch_arc python=3.11    #創(chuàng)建虛擬環(huán)境conda activate pytorch_arc                 #激活虛擬環(huán)境python?-m?pip?install?--upgrade?pip????????#升級(jí)pip到最新版本

接著，安裝Pytorch XPU版；

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

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最后，執(zhí)行命令，驗(yàn)證安裝。看到返回結(jié)果為“True”，證明環(huán)境搭建成功！

>>> import torch>>> torch.xpu.is_available()

二，訓(xùn)練ResNet模型

執(zhí)行下載的訓(xùn)練代碼，實(shí)現(xiàn)在Intel??Arc?集成顯卡上訓(xùn)練ResNet50模型。代碼下載鏈接：

?

import torchimport torchvision?LR = 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")for batch_idx, (data, target) in enumerate(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")

三，總結(jié)

使用PyTorch在英特爾獨(dú)立顯卡上訓(xùn)練模型將為AI行業(yè)新增計(jì)算硬件選擇！