How to train PyTorch model on CentOS

Efficient training of PyTorch models on CentOS systems requires steps, and this article will provide detailed guides.

1. Environmental preparation:

1. Python and dependencies installation: CentOS systems usually preinstall Python, but the version may be older. It is recommended to use yum or dnf to install Python 3 and upgrade pip : sudo yum update python3 (or sudo dnf update python3 ), pip3 install --upgrade pip .

2. CUDA and cuDNN (GPU acceleration): If you use NVIDIA GPU, you need to install the CUDA Toolkit and cuDNN library. Please visit NVIDIA's official website to download the corresponding version of the installation package and strictly follow the official guidelines to install it.

3. Virtual environment creation (recommended): It is recommended to use venv or conda to create a virtual environment to isolate project dependencies and avoid version conflicts. For example, use venv : python3 -m venv myenv , source myenv/bin/activate .

2. PyTorch installation:

Visit the PyTorch official website and select the appropriate installation command based on the system configuration (CPU or CUDA version). For example, in the CUDA 11.3 environment:

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

3. Model training process:

1. Dataset preparation: Prepare the training set and validation set. You can use public data sets or collect data yourself and ensure that the data format is compatible with the model code.

2. Model code writing: Use PyTorch to write model code, including model architecture, loss functions, and optimizer definitions.

3. Training model: Run training scripts on CentOS system. Make sure the environment is configured correctly, especially GPU environment variables.

4. Training process monitoring: Monitor indicators such as loss value and accuracy, and adjust model parameters or training strategies in a timely manner.

5. Model saving and loading: After training is completed, save model parameters for subsequent loading for inference or continue training. torch.save(model.state_dict(), 'your_model.pth')

6. Model Testing: Use the test set to evaluate model performance.

4. Example of PyTorch training loop:

The following is a simplified PyTorch training loop example, which needs to be modified according to actual conditions:

 import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from your_dataset import YourDataset # Replace with your dataset class YourModel(nn.Module):
    def __init__(self):
        super(YourModel, self).__init__()
        # ... Model layer definition...

    def forward(self, x):
        # ... Forward transmission...
        Return x

train_data = YourDataset(train=True)
val_data = YourDataset(train=False)
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
val_loader = DataLoader(val_data, batch_size=32, shuffle=False)

model = YourModel()
criteria = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

num_epochs = 10 # training rounds for epoch in range(num_epochs):
    model.train()
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criteria(outputs, labels)
        loss.backward()
        optimizer.step()
        # ... Print training process information...

    model.eval()
    with torch.no_grad():
        # ... Verify the model, calculate the performance indicators of the verification set...

torch.save(model.state_dict(), 'model.pth')

Please modify YourModel , YourDataset , loss function, optimizer and training parameters in the code according to your specific model and dataset. Remember to activate the virtual environment before running the code.

The above is the detailed content of How to train PyTorch model on CentOS. For more information, please follow other related articles on the PHP Chinese website!