CentOS与PyTorch的集成方法有哪些 - 问答

在CentOS上集成PyTorch可以通过多种方法实现，以下是一些常见的方法：

使用pip安装PyTorch

更新系统：
```
sudo yum update -y
```

安装依赖项：

sudo yum install -y python3 python3-pip python3-devel

安装PyTorch：

CPU版本：

pip3 install torch torchvision torchaudio

CUDA 11.3版本：

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

CUDA 11.6版本：

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116

CUDA 11.7版本：

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117

CUDA 12.0版本：

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu120

验证安装：

import torch
print(torch.__version__)
print(torch.cuda.is_available())

使用conda安装PyTorch

安装Anaconda或Miniconda：
- 从Anaconda官网下载并安装适合CentOS的安装包。

创建虚拟环境：

conda create -n pytorch_env python3.9
conda activate pytorch_env

安装PyTorch：

CPU版本：

conda install pytorch torchvision torchaudio cpuonly -c pytorch

CUDA 11.3版本：

conda install pytorch torchvision torchaudio cudatoolkit 11.3 -c pytorch

CUDA 11.6版本：

conda install pytorch torchvision torchaudio cudatoolkit 11.6 -c pytorch

CUDA 11.7版本：

conda install pytorch torchvision torchaudio cudatoolkit 11.7 -c pytorch

CUDA 12.0版本：

conda install pytorch torchvision torchaudio cudatoolkit 12.0 -c pytorch

验证安装：

import torch
print(torch.__version__)
print(torch.cuda.is_available())

使用Docker进行部署

创建Dockerfile：

FROM pytorch/pytorch:latest
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python", "app.py"]

构建Docker镜像：
```
docker build -t pytorch-resnet18 .
```

运行Docker容器：

docker run -p 5000:5000 pytorch-resnet18

模型部署方法

TorchScript：

追踪（Tracing）：

import torch
import torchvision.models as models

model = models.resnet18()
example = torch.rand(1, 3, 224, 224)
traced_script_module = torch.jit.trace(model, example)
traced_script_module.save("traced_model.pt")

脚本化（Scripting）：

import torch

class MyModule(torch.nn.Module):
    def __init__(self, n, m):
        super(MyModule, self).__init__()
        self.weight = torch.nn.Parameter(torch.rand(n, m))

    def forward(self, input):
        if input.sum() > 0:
            output = self.weight.mv(input)
        else:
            output = self.weight @ input
        return output

my_module = MyModule(10, 20)
scripted_module = torch.jit.script(my_module)
scripted_module.save("scripted_model.pt")

ONNX：

转换为ONNX：

import torch
import torchvision.models as models
from torch.utils.data import DataLoader, TensorDataset
from PIL import Image

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc = nn.Linear(16 * 16 * 16, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

model = SimpleCNN()
model.load_state_dict(torch.load('model.pth'))
model.eval()

def preprocess_image(image_path):
    transform = transforms.Compose([
        transforms.Resize((32, 32)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    image = Image.open(image_path)
    image_tensor = transform(image).unsqueeze(0)
    return image_tensor

# 推理流程
image_path = 'test_image.jpg'
input_tensor = preprocess_image(image_path)
with torch.no_grad():
    output = model(input_tensor)
    _, predicted = torch.max(output.data, 1)
print(f"Predicted class: {predicted.item()}")

# 转换为ONNX
dummy_input = input_tensor.clone().detach()
torch.onnx.export(model, dummy_input, "simple_cnn.onnx", verbose=True)

使用ONNX Runtime进行推理：

import onnx
import onnxruntime as ort

# 加载ONNX模型
onnx_model = onnx.load("simple_cnn.onnx")
onnx.checker.check_model(onnx_model)

# 创建推理会话
ort_session = ort.InferenceSession("simple_cnn.onnx")

# 推理
input_name = ort_session.get_inputs()[0].name
output_name = ort_session.get_outputs()[0].name
ort_inputs = {input_name: input_tensor.numpy()}
ort_outs = ort_session.run([output_name], ort_inputs)
print(f"Predicted class: {ort_outs[0][0]}")

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