How to build models with PyTorch

· Category: AI & Machine Learning

Short answer

PyTorch is a Python-first deep learning framework that uses dynamic computation graphs and an intuitive object-oriented API for model building.

Steps

  1. Install PyTorch and confirm GPU support with torch.cuda.is_available.
  2. Create tensors and move them to the GPU using the to method.
  3. Define models by subclassing nn.Module and implementing the forward method.
  4. Choose an optimizer and loss function from torch.optim and nn.
  5. Write a training loop that performs forward passes, loss computation, backward passes, and optimizer steps.

Tips

  • Use torch.nn.DataParallel or DistributedDataParallel for multi-GPU training.
  • Leverage torchvision and torchtext for standard datasets and transforms.
  • Profile code with PyTorch Profiler to identify bottlenecks.
  • Use torch.compile in newer versions for graph-level optimizations.

Common issues

  • Forgetting to call optimizer.zero_grad before backward passes.
  • Mismatched tensor shapes in layer definitions causing runtime errors.
  • CPU-GPU tensor mismatches when not explicitly moving data.
  • Memory fragmentation on GPU leading to out-of-memory errors.

Example

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(784, 10)
    def forward(self, x):
        return self.fc(x)

model = Net()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

This snippet defines a simple PyTorch model, demonstrating the nn.Module subclass pattern and optimizer setup.