PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
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name
pytorch-patterns
description
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
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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.
# 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 setupdefset_seed(seed: int = ) -> :
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic =
torch.backends.cudnn.benchmark =
model = MyModel()
42
None
True
False
# Bad: No seed control
# Different weights every run
3. Explicit Shape Management
Always document and verify tensor shapes.
# Good: Shape-annotated forward passdefforward(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)returnself.fc(x) # -> (batch_size, num_classes)# Bad: No shape trackingdefforward(self, x):
x = self.conv1(x)
x = self.pool(x)
x = x.view(x.size(0), -1) # What size is this?returnself.fc(x) # Will this even work?
# Good: AMP with GradScaler
scaler = torch.amp.GradScaler("cuda")
for data, target in dataloader:
with torch.amp.autocast("cuda"):
output = model(data)
loss = criterion(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
Gradient Checkpointing for Large Models
# Good: Trade compute for memoryfrom torch.utils.checkpoint import checkpoint
classLargeModel(nn.Module):
defforward(self, x: torch.Tensor) -> torch.Tensor:
# Recompute activations during backward to save memory
x = checkpoint(self.block1, x, use_reentrant=False)
x = checkpoint(self.block2, x, use_reentrant=False)
returnself.head(x)
torch.compile for Speed
# Good: Compile the model for faster execution (PyTorch 2.0+)
model = MyModel().to(device)
model = torch.compile(model, mode="reduce-overhead")
# Modes: "default" (safe), "reduce-overhead" (faster), "max-autotune" (fastest)
Quick Reference: PyTorch Idioms
Idiom
Description
model.train() / model.eval()
Always set mode before train/eval
torch.no_grad()
Disable gradients for inference
optimizer.zero_grad(set_to_none=True)
More efficient gradient clearing
.to(device)
Device-agnostic tensor/model placement
torch.amp.autocast
Mixed precision for 2x speed
pin_memory=True
Faster CPU→GPU data transfer
torch.compile
JIT compilation for speed (2.0+)
weights_only=True
Secure model loading
torch.manual_seed
Reproducible experiments
gradient_checkpointing
Trade compute for memory
Anti-Patterns to Avoid
# Bad: Forgetting model.eval() during validation
model.train()
with torch.no_grad():
output = model(val_data) # Dropout still active! BatchNorm uses batch stats!# Good: Always set eval mode
model.eval()
with torch.no_grad():
output = model(val_data)
# Bad: In-place operations breaking autograd
x = F.relu(x, inplace=True) # Can break gradient computation
x += residual # In-place add breaks autograd graph# Good: Out-of-place operations
x = F.relu(x)
x = x + residual
# Bad: Moving data to GPU inside the training loop repeatedlyfor data, target in dataloader:
model = model.cuda() # Moves model EVERY iteration!# Good: Move model once before the loop
model = model.to(device)
for data, target in dataloader:
data, target = data.to(device), target.to(device)
# Bad: Using .item() before backward
loss = criterion(output, target).item() # Detaches from graph!
loss.backward() # Error: can't backprop through .item()# Good: Call .item() only for logging
loss = criterion(output, target)
loss.backward()
print(f"Loss: {loss.item():.4f}") # .item() after backward is fine# Bad: Not using torch.save properly
torch.save(model, "model.pt") # Saves entire model (fragile, not portable)# Good: Save state_dict
torch.save(model.state_dict(), "model.pt")
Remember: PyTorch code should be device-agnostic, reproducible, and memory-conscious. When in doubt, profile with torch.profiler and check GPU memory with torch.cuda.memory_summary().
