How to do data preprocessing with PyTorch on CentOS
Apr 14, 2025 pm 02:15 PMEfficiently processing PyTorch data on CentOS system requires the following steps:
-
Dependency installation: First update the system and install Python 3 and pip:
sudo yum update -y sudo yum install python3 -y sudo yum install python3-pip -y
Then, download and install CUDA Toolkit and cuDNN from the official NVIDIA website according to your CentOS version and GPU model.
-
Virtual Environment Configuration (recommended): Use conda to create and activate a new virtual environment, for example:
conda create -n pytorch python=3.8 conda activated pytorch
-
PyTorch installation: In the activated virtual environment, use conda or pip to install PyTorch. The version that supports CUDA is as follows:
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch # Adjust cudatoolkit version number to match your CUDA version
Or use pip (you may need to specify the CUDA version):
pip install torch torchvision torchaudio
-
Data preprocessing and enhancement: Use the
torchvision.transforms
module for data preprocessing and enhancement. The following examples show image resizing, random horizontal flip, conversion to tensor, and normalization:import torch import torchvision from torchvision import transforms transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset = torchvision.datasets.ImageFolder(root='path/to/data', transform=transform) dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
-
Custom dataset: For custom datasets, inherit the
torch.utils.data.Dataset
class and implement__getitem__
and__len__
methods. For example:import os from PIL import Image from torch.utils.data import Dataset class MyDataset(Dataset): def __init__(self, root_path, labels): self.root_path = root_path self.labels = labels # list of labels for corresponding image self.image_files = [f for f in os.listdir(root_path) if f.endswith(('.jpg', '.png'))] # Assume that the image is in jpg or png format def __getitem__(self, index): img_path = os.path.join(self.root_path, self.image_files[index]) img = Image.open(img_path) label = self.labels[index] return img, label def __len__(self): return len(self.image_files)
-
Data loading: Use
torch.utils.data.DataLoader
to load and batch data:from torch.utils.data import DataLoader my_dataset = MyDataset('path/to/your/data', [0,1,0,1, ...]) # Replace 'path/to/your/data' and tag list data_loader = DataLoader(dataset=my_dataset, batch_size=64, shuffle=True, num_workers=0) # num_workers Adjust based on your CPU core number
Remember to replace the placeholder path and label with your actual data. The
num_workers
parameter can be adjusted according to the number of CPU cores to improve data loading speed.
Through the above steps, you can complete the data preprocessing of PyTorch on CentOS. If you have any questions, please refer to the official PyTorch documentation or seek community support.
The above is the detailed content of How to do data preprocessing with PyTorch on CentOS. For more information, please follow other related articles on the PHP Chinese website!

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