开发者的福音:PyTorch 2.5现已支持英特尔独立显卡训练
openlab_96bf3613
更新于 27天前
《PyTorch 2.5重磅更新:性能优化+新特性》中的一个新特性就是:正式支持在英特尔®独立显卡上训练模型!
本文将在Intel®Core™ Ultra 7 155H自带的Arc™集成显卡上展示使用Pytorch2.5搭建并训练AI模型的全流程。
一. 搭建开发环境
首先,请安装显卡驱动,参考指南:
https://dgpu-docs.intel.com/driver/client/overview.html
然后,请下载并安装Anaconda,链接↓↓
https://www.anaconda.com/download
并用下面的命令创建并激活名为pytorch_arc的虚拟环境:
conda create -n pytorch_arc python=3.11 #创建虚拟环境
conda activate pytorch_arc #激活虚拟环境
python -m pip install --upgrade pip #升级pip到最新版本
接着,安装Pytorch XPU版;
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
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最后,执行命令,验证安装。看到返回结果为“True”,证明环境搭建成功!
>>> import torch
>>> torch.xpu.is_available()
二. 训练ResNet模型
执行下载的训练代码,实现在Intel® Arc™集成显卡上训练ResNet50模型。代码下载链接:
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)
los***ackward()
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. 总结
使用PyTorch在英特尔独立显卡上训练模型将为AI行业新增计算硬件选择!
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