This repository curates resources and practical guides for deep learning infrastructure, bridging the gap between theoretical algorithms and robust engineering implementation. It targets engineers and researchers focused on optimizing AI model deployment, covering low-level programming, hardware architecture, distributed systems, and AI compilers. The project offers a hands-on approach to building and optimizing deep learning systems.

How It Works

The project adopts a multi-faceted approach, integrating low-level programming (C++, CUDA, Assembly) with high-level AI concepts (Deep Learning, CV, NLP). Core components include AI compilers for model acceleration, parallel optimization, and performance profiling. Engineering aspects span hardware architecture, OS/kernel internals, distributed systems (Kubernetes), and storage, emphasizing practical application through tutorials and code examples.

Quick Start & Requirements

Setup involves downloading collected paper PDFs from Feishu Drive into a paper-pdfs directory. Practical implementation tutorials cover CUDA, Docker & Kubernetes, and Protobuf/gRPC. Prerequisites likely include a development environment with C++, Python, and potentially GPU access for CUDA-related content. Specific hardware or software versions are not detailed but implied by the topics.

Highlighted Details

• Curated online tools for hardware/software benchmarking and code analysis (e.g., WikiChip, Godbolt, Quick-Bench).
• In-depth exploration of AI compilers and model acceleration techniques.
• Practical tutorials on essential infrastructure components: Docker, Kubernetes, gRPC.
• Focus on bridging algorithmic understanding with engineering implementation for deep learning systems.

Maintenance & Community

No specific information on contributors, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The README does not specify a software license.

Limitations & Caveats

Appears to be a personal learning resource with "todo" items indicating ongoing development. Reliance on Feishu Drive for data presents a non-standard, potentially private, setup requirement. It may not represent a formal, production-ready framework.