pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
npx skills add mturac/everything-openai-codex --skill pytorch-patterns --agent codex
Same command for any agent — swap --agent for claude-code, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to Activate
- Core Principles
- 1. Device-Agnostic Code
- 2. Reproducibility First
- 3. Explicit Shape Management
- Model Architecture Patterns
- Clean nn.Module Structure
- Proper Weight Initialization
- Training Loop Patterns
- Standard Training Loop
- Validation Loop
- Data Pipeline Patterns
- Custom Dataset
- Efficient DataLoader Configuration
What does the pytorch-patterns skill do?
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
How do I install it?
Run `npx skills add mturac/everything-openai-codex --skill pytorch-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mturac/everything-openai-codex, a repository with 84 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.