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Deep Learning Ecosystem Wiki 🧠

A comprehensive guide to modern deep learning frameworks, tools, and infrastructure

Table of Contents

• Overview
• Research Frameworks
  • PyTorch
  • TensorFlow
  • Keras
  • JAX
  • MLX
  • PyTorch Lightning
• Inference Solutions
• Low-Level Tools
• Edge Computing & Embedded Systems
• High-Level Libraries

Overview

This wiki provides a comprehensive breakdown of the current deep learning ecosystem, focusing on practical applications and real-world usage. Whether you're a researcher, developer, or ML engineer, you'll find valuable insights into choosing the right tools for your projects.

Research Frameworks

Key Features:

• Dynamic computation graphs
• Intuitive Python-first design
• Extensive research community support

Use Cases:

• Research projects
• Rapid prototyping
• NLP with Hugging Face
• Computer vision applications

Resources:

• Official Documentation
• GitHub Repository
• Tutorials

Key Features:

• Production-ready deployment
• TPU support
• Comprehensive ecosystem

Use Cases:

• Production environments
• Large-scale deployments
• Google Cloud integration

Framework Comparison Table

Framework	Best For	Learning Curve	Production Ready
PyTorch	Research	Moderate	Yes
TensorFlow	Production	Steep	Yes
JAX	High Performance	Steep	Partial
MLX	Apple Silicon	Moderate	Yes
Lightning	Distributed Training	Moderate	Yes

Inference Solutions

Performance Comparison

graph TB
  A[Inference Solutions] --> B[vLLM]
  A --> C[TensorRT]
  A --> D[Triton]
  B --> E[LLM Specialized]
  C --> F[NVIDIA Optimized]
  D --> G[Matrix Operations]

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Features Matrix

Solution	Speed	Memory Usage	Platform Support
vLLM	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	GPU
TensorRT	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	NVIDIA GPU
Triton	⭐⭐⭐⭐	⭐⭐⭐	CPU/GPU

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.