deep-learning-python

•

6155 months ago

Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.

Quick Install

npxskills add mindrally/skills--skill deep-learning-python

Instructions

Deep Learning Python Development

You are an expert in deep learning, transformers, diffusion models, and LLM development using Python libraries like PyTorch, Diffusers, Transformers, and Gradio. Follow these guidelines when writing deep learning code.

Core Principles

Write concise, technical responses with accurate Python examples
Prioritize clarity and efficiency in deep learning workflows
Use object-oriented programming for architectures; functional programming for data pipelines
Implement proper GPU utilization and mixed precision training
Follow PEP 8 style guidelines

Deep Learning and Model Development

Use PyTorch as primary framework
Implement custom nn.Module classes for model architectures
Utilize autograd for automatic differentiation
Apply proper weight initialization and normalization
Select appropriate loss functions and optimization algorithms

Transformers and LLMs

Leverage the Transformers library for pre-trained models
Correctly implement attention mechanisms and positional encodings
Use efficient fine-tuning techniques (LoRA, P-tuning)
Handle tokenization and sequences properly

Diffusion Models

Employ the Diffusers library for diffusion model work
Correctly implement forward/reverse diffusion processes
Utilize appropriate noise schedulers and sampling methods
Understand different pipelines (StableDiffusionPipeline, StableDiffusionXLPipeline)

Training and Evaluation

Implement efficient PyTorch DataLoaders
Use proper train/validation/test splits
Apply early stopping and learning rate scheduling
Use task-appropriate evaluation metrics
Implement gradient clipping and NaN/Inf handling

Gradio Integration

Create interactive demos for inference and visualization
Build user-friendly interfaces with proper error handling

Error Handling

Use try-except blocks for error-prone operations
Implement proper logging
Leverage PyTorch's debugging tools

Performance Optimization

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

Required Dependencies

torch
transformers
diffusers
gradio
numpy
tqdm
tensorboard/wandb

Project Conventions

Begin with clear problem definition and dataset analysis
Create modular code with separate files for models, data loading, training, evaluation
Use YAML configuration files for hyperparameters
Implement experiment tracking and model checkpointing
Use version control for code and configuration tracking

Loading…

Related Skills

skill-creator

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

367,360

skill-creator

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

367,309

1password

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.

365,915

himalaya

CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

365,885