pytorch-patterns

PyTorch Development Patterns

Use the project's installed version and conventions. Verify version-sensitive APIs, security settings, and deployment support with documentation-lookup before applying examples; these patterns are reference guidance, not a tested guarantee for every current release. Do not replace an existing toolchain solely to match an example.

Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.

When to Activate

• Writing new PyTorch models or training scripts
• Reviewing deep learning code
• Debugging training loops or data pipelines
• Optimizing GPU memory usage or training speed
• Setting up reproducible experiments

Core Principles

1. Device-Agnostic Code

Always write code that works on both CPU and GPU without hardcoding devices.

# Good: Device-agnostic
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
data = data.to(device)

# Bad: Hardcoded device
model = MyModel().cuda()  # Crashes if no GPU
data = data.cuda()

2. Reproducibility First

Set all random seeds for reproducible results.

# Good: Full reproducibility setup
def set_seed(seed: int = 42) -> None:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# Bad: No seed control
model = MyModel()  # Different weights every run

3. Explicit Shape Management

Always document and verify tensor shapes.

# Good: Shape-annotated forward pass
def forward(self, x: torch.Tensor) -> torch.Tensor:
    # x: (batch_size, channels, height, width)
    x = self.conv1(x)    # -> (batch_size, 32, H, W)
    x = self.pool(x)     # -> (batch_size, 32, H//2, W//2)
    x = x.view(x.size(0), -1)  # -> (batch_size, 32*H//2*W//2)
    return self.fc(x)    # -> (batch_size, num_classes)

# Bad: No shape tracking
def forward(self, x):
    x = self.conv1(x)
    x = self.pool(x)
    x = x.view(x.size(0), -1)  # What size is this?
    return self.fc(x)           # Will this even work?