OneFlow框架0.9.1dev版本，成功安装并运行

安装cuda和cudnn: （此步骤可以忽略，pip安装框架时会自动安装依赖的cuda和cudnn环境）

conda install cudatoolkit==11.8.0
python3 -m pip install nvidia-cudnn-cu11==8.7.0.84

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下载安装包的下载地址页面：

wget https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu118

并从中找到对应python版本的安装包地址。

下载框架安装包：（python3.10环境下）

wget https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu118/82503845d553c722041d884d109fc1314568a4c1/oneflow-0.9.1.dev20240201%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

手动安装依赖包：

pip install packaging
pip install typing_extensions
pip install flowvision

安装框架包：

pip install ./oneflow-0.9.1.dev20240201+cu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

测试代码：

import oneflow as flow
import oneflow.nn as nn
import flowvision
import flowvision.transforms as transforms
BATCH_SIZE = 64
EPOCH_NUM = 1
DEVICE = "cuda" if flow.cuda.is_available() else "cpu"
print("Using {} device".format(DEVICE))
training_data = flowvision.datasets.CIFAR10(
    root="data",
    train=True,
    transform=transforms.ToTensor(),
    download=True,
    source_url="https://oneflow-public.oss-cn-beijing.aliyuncs.com/datasets/cifar/cifar-10-python.tar.gz",
)

train_dataloader = flow.utils.data.DataLoader(
    training_data, BATCH_SIZE, shuffle=True
)
model = flowvision.models.mobilenet_v2().to(DEVICE)
model.classifer = nn.Sequential(nn.Dropout(0.2), nn.Linear(model.last_channel, 10))
model.train()
loss_fn = nn.CrossEntropyLoss().to(DEVICE)

param_groups = [
    {'params':model.features.parameters(), 'lr':1e-3},
    {'params':model.adaptive_avg_pool2d.parameters(), 'lr':1e-4},
    {'params':model.classifier.parameters(), 'lr':1e-5},
]
optimizer = flow.optim.SGD(param_groups)

for t in range(EPOCH_NUM):
    print(f"Epoch {t+1}\n-------------------------------")
    size = len(train_dataloader.dataset)
    for batch, (x, y) in enumerate(train_dataloader):
        x = x.to(DEVICE)
        y = y.to(DEVICE)

        # Compute prediction error
        pred = model(x)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        current = batch * BATCH_SIZE
        if batch % 5 == 0:
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

运行效果：