pytorch-patternslisted

# PyTorch Development Patterns 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. ```python # 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. ```python # 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. ```python # 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