pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
Best for Deep learning engineers bui…Works with GitHubLow risk
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@affaan-m- language
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Apr 17, 2026
✦overview.md
Key Features
- ·Device-agnostic code for CPU/GPU compatibility
- ·Full reproducibility setup with random seed control
- ·Explicit tensor shape management and documentation
- ·Idiomatic patterns for robust training pipelines
- ·Best practices for efficient model architectures and data loading
Use Cases
- →Writing new PyTorch models or training scripts
- →Reviewing deep learning code for best practices
- →Debugging training loops or data pipelines
- →Optimizing GPU memory usage or training speed
- →Setting up reproducible experiments
Best for
- ✓Deep learning engineers building PyTorch applications
- ✓Teams requiring reproducible ML experiments
- ✓Projects optimizing for GPU efficiency
Not ideal for
- !Non-PyTorch frameworks
- !Simple one-off scripts without reproducibility needs
FAQs
name
pytorch-patterns
description
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
origin:ECC
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 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
$install
1-click copynpx skills add affaan-m/everything-claude-code --skill pytorch-patternsSafety assessment
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