10 Principles of PyTorch

Welcome to this concise guide on the principles of PyTorch. Whether you're a beginner or have some experience, understanding these principles can make your journey smoother. Let's gets started!

1. Tensors: The Building Blocks

Tensors in PyTorch are multi-dimensional arrays. They are similar to NumPy's ndarrays but can run on GPUs.

import torch

# Create a 2x3 tensor
tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(tensor)

2. Dynamic Computation Graph

PyTorch uses dynamic computation graphs, meaning the graph is built on-the-fly as operations are executed. This provides flexibility for modifying the graph during runtime.

# Define two tensors
a = torch.tensor([2.], requires_grad=True)
b = torch.tensor([3.], requires_grad=True)

# Compute result
c = a * b
c.backward()

# Gradients
print(a.grad)  # Gradient w.r.t a

3. GPU Acceleration

PyTorch allows easy switching between CPU and GPU. Utilize .to(device) for optimal performance.

device = "cuda" if torch.cuda.is_available() else "cpu"
tensor = tensor.to(device)

4. Autograd: Automatic Differentiation

PyTorch's autograd provides automatic differentiation for all operations on tensors. Set requires_grad=True to track computations.

x = torch.tensor([2.], requires_grad=True)
y = x**2
y.backward()
print(x.grad)  # Gradient of y w.r.t x

5. Modular Neural Networks with nn.Module

PyTorch provides the nn.Module class to define neural network architectures. Create custom layers by subclassing.

import torch.nn as nn

class SimpleNN(nn.Module):

    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(1, 1)
        
    def forward(self, x):
        return self.fc(x)

6. Predefined Layers and Loss Functions

PyTorch offers various predefined layers, loss functions, and optimization algorithms in the nn module.

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

7. Dataset and DataLoader

For efficient data handling and batching, PyTorch offers the Dataset and DataLoader classes.

from torch.utils.data import Dataset, DataLoader

class CustomDataset(Dataset):
    # ... (methods to define)
    
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

8. Model Training Loop

Typically, training in PyTorch follows the pattern: forward pass, compute loss, backward pass, and parameter update.

for epoch in range(epochs):
    for data, target in data_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()

9. Model Serialization

Save and load your models using torch.save() and torch.load().

# Save
torch.save(model.state_dict(), 'model_weights.pth')

# Load
model.load_state_dict(torch.load('model_weights.pth'))

10. Eager Execution and JIT

While PyTorch operates in eager mode by default, it offers Just-In-Time (JIT) compilation for production-ready models.

scripted_model = torch.jit.script(model)
scripted_model.save("model_jit.pt")