Can it help me fix GPU Out-of-Memory (OOM) errors?

Absolutely. The skill implements memory-saving strategies like gradient checkpointing, Automatic Mixed Precision (AMP), and efficient CUDA memory management.

Does it improve data loading performance?

It optimizes DataLoader configurations with best practices like persistent workers, prefetch factors, and pinned memory to ensure your GPU isn't waiting for data.

Will this refactor my code into PyTorch Lightning?

Yes, it understands PyTorch Lightning patterns and can help reorganize standard training loops into structured, modular LightningModule classes.

Does this skill support the latest PyTorch 2.x features?

Yes, it specifically targets PyTorch 2.x patterns including torch.compile for automatic kernel fusion and memory-efficient attention mechanisms.

PyTorch Refactoring & Optimization

Name: PyTorch Refactoring & Optimization
Author: SnakeO

bySnakeO

データサイエンスとML

Refactors PyTorch code to improve maintainability, performance, and adherence to PyTorch 2.x best practices.

概要

This skill acts as an elite deep learning engineer to transform working PyTorch code into exemplary, production-grade implementations. It identifies architectural smells such as DRY violations and deep nesting while injecting modern PyTorch 2.x optimizations like torch.compile, Automatic Mixed Precision (AMP), and memory-efficient attention. By enforcing the Single Responsibility Principle and modular nn.Module design, it ensures your models are not only faster to train but also significantly easier to maintain and scale.

主な機能

Implementation of Automatic Mixed Precision (AMP) for faster training and reduced VRAM
0 GitHub stars
GPU memory optimization through gradient checkpointing and CUDA cache management
DataLoader performance tuning including pin_memory and multi-worker configurations
Automated PyTorch 2.x optimization using torch.compile and kernel fusion
Modular nn.Module refactoring to eliminate code duplication and improve readability

ユースケース

Optimizing large-scale model training to prevent Out-of-Memory (OOM) errors
Modernizing legacy PyTorch 1.x research code to utilize PyTorch 2.x performance features
Refactoring monolithic training scripts into structured PyTorch Lightning modules

概要

主な機能

Implementation of Automatic Mixed Precision (AMP) for faster training and reduced VRAM
0 GitHub stars
GPU memory optimization through gradient checkpointing and CUDA cache management
DataLoader performance tuning including pin_memory and multi-worker configurations
Automated PyTorch 2.x optimization using torch.compile and kernel fusion
Modular nn.Module refactoring to eliminate code duplication and improve readability

ユースケース

Optimizing large-scale model training to prevent Out-of-Memory (OOM) errors
Modernizing legacy PyTorch 1.x research code to utilize PyTorch 2.x performance features
Refactoring monolithic training scripts into structured PyTorch Lightning modules