사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/affaan-m/ecc --skill pytorch-patterns시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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 run3. 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(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?Model Architecture Patterns
Clean nn.Module Structure
python
# Good: Well-organized module
class ImageClassifier(nn.Module):
def __init__(self, num_classes: int, dropout: float = 0.5) -> None:
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(64 * 16 * 16, num_classes),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.features(x)
x = x.view(x.size(0), -1)
return self.classifier(x)
# Bad: Everything in forward
class ImageClassifier(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = F.conv2d(x, weight=self.make_weight()) # Creates weight each call!
return xProper Weight Initialization
python
# Good: Explicit initialization
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, nn.BatchNorm2d):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
model = MyModel()
model.apply(model._init_weights)Training Loop Patterns
Standard Training Loop
python
# Good: Complete training loop with best practices
def train_one_epoch(
model: nn.Module,
dataloader: DataLoader,
optimizer: torch.optim.Optimizer,
criterion: nn.Module,
device: torch.device,
scaler: torch.amp.GradScaler | None = None,
) -> float:
model.train() # Always set train mode
total_loss = 0.0
for batch_idx, (data, target) in enumerate(dataloader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad(set_to_none=True) # More efficient than zero_grad()
# Mixed precision training
with torch.amp.autocast("cuda", enabled=scaler is not None):
output = model(data)
loss = criterion(output, target)
if scaler is not None:
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
total_loss += loss.item()
return total_loss / len(dataloader)Validation Loop
python
# Good: Proper evaluation
@torch.no_grad() # More efficient than wrapping in torch.no_grad() block
def evaluate(
model: nn.Module,
dataloader: DataLoader,
criterion: nn.Module,
device: torch.device,
) -> tuple[float, float]:
model.eval() # Always set eval mode — disables dropout, uses running BN stats
total_loss = 0.0
correct = 0
total = 0
for data, target in dataloader:
data, target = data.to(device), target.to(device)
output = model(data)
total_loss += criterion(output, target).item()
correct += (output.argmax(1) == target).sum().item()
total += target.size(0)
return total_loss / len(dataloader), correct / totalData Pipeline Patterns
Custom Dataset
python
# Good: Clean Dataset with type hints
class ImageDataset(Dataset):
def __init__(
self,
image_dir: str,
labels: dict[str, int],
transform: transforms.Compose | None = None,
) -> None:
self.image_paths = list(Path(image_dir).glob("*.jpg"))
self.labels = labels
self.transform = transform
def __len__(self) -> int:
return len(self.image_paths)
def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:
img = Image.open(self.image_paths[idx]).convert("RGB")
label = self.labels[self.image_paths[idx].stem]
if self.transform:
img = self.transform(img)
return img, labelEfficient DataLoader Configuration
python
# Good: Optimized DataLoader
dataloader = DataLoader(
dataset,
batch_size=32,
shuffle=True, # Shuffle for training
num_workers=4, # Parallel data loading
pin_memory=True, # Faster CPU->GPU transfer
persistent_workers=True, # Keep workers alive between epochs
drop_last=True, # Consistent batch sizes for BatchNorm
)
# Bad: Slow defaults
dataloader = DataLoader(dataset, batch_size=32) # num_workers=0, no pin_memoryCustom Collate for Variable-Length Data
python
# Good: Pad sequences in collate_fn
def collate_fn(batch: list[tuple[torch.Tensor, int]]) -> tuple[torch.Tensor, torch.Tensor]:
sequences, labels = zip(*batch)
# Pad to max length in batch
padded = nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=0)
return padded, torch.tensor(labels)
dataloader = DataLoader(dataset, batch_size=32, collate_fn=collate_fn)Checkpointing Patterns
Save and Load Checkpoints
python
# Good: Complete checkpoint with all training state
def save_checkpoint(
model: nn.Module,
optimizer: torch.optim.Optimizer,
epoch: int,
loss: float,
path: str,
) -> None:
torch.save({
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"loss": loss,
}, path)
def load_checkpoint(
path: str,
model: nn.Module,
optimizer: torch.optim.Optimizer | None = None,
) -> dict:
checkpoint = torch.load(path, map_location="cpu", weights_only=True)
model.load_state_dict(checkpoint["model_state_dict"])
if optimizer:
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
return checkpoint
# Bad: Only saving model weights (can't resume training)
torch.save(model.state_dict(), "model.pt")Performance Optimization
Mixed Precision Training
python
# 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
python
# Good: Trade compute for memory
from torch.utils.checkpoint import checkpoint
class LargeModel(nn.Module):
def forward(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)
return self.head(x)torch.compile for Speed
python
# 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
python
# 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 repeatedly
for 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().
