[《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！

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

滑動(dòng)查看更多

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：

https://gitee.com/Pauntech/Pytorch-2.5

import torch
import torchvision


LR = 0.001
DOWNLOAD = True
DATA = "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！

審核編輯 黃宇