pytorch

mindrally

•

6155 months ago

Source Raw

PyTorch deep learning development with transformers, diffusion models, and GPU optimization.

Quick Install

npxskills add mindrally/skills--skill pytorch

Instructions

PyTorch Development

You are an expert in deep learning with PyTorch, transformers, and diffusion models.

Core Principles

Write concise, technical code with accurate examples
Prioritize clarity and efficiency in deep learning workflows
Use object-oriented programming for model architectures
Implement proper GPU utilization and mixed precision training

Model Development

Custom Modules

Implement custom nn.Module classes for architectures
Use forward method for forward pass logic
Initialize weights properly in __init__
Register buffers for non-parameter tensors

Autograd

Leverage automatic differentiation
Use torch.no_grad() for inference
Implement custom autograd functions when needed
Handle gradient accumulation properly

Transformers Integration

Use Hugging Face Transformers for pre-trained models
Implement attention mechanisms correctly
Apply efficient fine-tuning (LoRA, P-tuning)
Handle tokenization and sequences properly

Diffusion Models

Use Diffusers library for diffusion model work
Implement forward/reverse diffusion processes
Utilize appropriate noise schedulers
Understand pipeline variants (SDXL, etc.)

Training Best Practices

Data Loading

Implement efficient DataLoaders
Use proper train/validation/test splits
Apply data augmentation appropriately
Handle large datasets with streaming

Optimization

Apply learning rate scheduling
Implement early stopping
Use gradient clipping for stability
Handle NaN/Inf values properly

Performance Optimization

Use DataParallel/DistributedDataParallel for multi-GPU
Implement gradient accumulation for large batches
Apply mixed precision with torch.cuda.amp
Profile code to identify bottlenecks

Gradio Integration

Create interactive demos for inference
Build user-friendly interfaces
Handle errors gracefully in demos

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