Paper Reading -- Deep Learning Infrastructure
The Paper Reading project focuses on deep learning infrastructure, offering an in-depth exploration of the intersection between algorithms and engineering. It is designed for individuals who possess both an engineering background and a solid understanding of algorithm models. This project aims to bridge the gap between the two fields by providing comprehensive resources and tools.
Key Areas of Expertise
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Programming: The project emphasizes several programming languages and environments vital for deep learning and engineering applications, including C++, CUDA, assembly language, Python, and Shell scripting.
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Algorithms: It covers a wide range of algorithmic concepts, especially those related to deep learning, computer vision (CV), and natural language processing (NLP). The project provides insights into training frameworks and inference deployment to enable practical applications of these algorithms.
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Acceleration: A focus on accelerating processes with AI compilers, parallel optimization, and profiling tools is included, which are crucial for enhancing computational efficiency.
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Engineering: Understanding hardware architecture, operating systems, and the Linux kernel is essential, along with skills in distributed systems and Kubernetes (k8s) clusters for effective storage solutions.
Awesome Online Tools
The project provides a variety of online resources to support in-depth technical research and development:
- WikiChip for chip architecture and specifications.
- CPUBenchmark for CPU benchmarking and performance analysis.
- VideoCardBenchmark for GPU benchmarking.
- Godbolt for viewing assembly codes of C++ scripts.
- Quick Bench for conducting C++ code benchmarks.
- C++ Reference as a comprehensive manual for C++ language documentation.
AI Compilers
A detailed introduction to AI compilers and their role in model acceleration can be found through resources like the published articles on platforms like Zhihu.
Deep Learning
The project delves into various aspects of deep learning, with resources on:
- Large Models & AIGC (Artificially Intelligent Generated Content): Insights into learning and working with AIGC models.
- Applications like Autonomous Driving with dedicated learning modules.
- Algorithm Concepts such as handling noisy labels and understanding different CNN models including AlexNet, VGG, and ResNet.
- Deep Learning Frameworks such as PyTorch and ONNX, providing overviews and learning materials.
High Performance Computing (HPC)
Detailed maps and tutorials enhance understanding of HPC concepts:
- Performance Tools Map for optimizing performance, complete with tool usage documentation.
- CUDA and GPU Architecture Learning resources.
- Tutorials with code, like "Hands on CUDA" and OpenMP tutorial, help grasp the nuts and bolts of HPC.
Engineering
Broad engineering aspects are covered, focusing on containerization and protocol buffers:
- Docker & Kubernetes (K8S): Tutorials for beginners and in-depth resources on Docker and NVIDIA-Docker for GPU integration.
- Protobuf & gRPC: Tutorials and best practices guide you through installation, usage, and creating web services with examples and styles.
Programming Languages
Essential insights into assembly languages such as x86 and MIPS are available, alongside C++ programming fundamentals, and scripting with Python and Shell.
Usage Guide
The project is powered by the Paper Pipe tool, which facilitates the download and management of collected research papers from platforms such as Feishu Drive. Users are encouraged to download the papers and store them in a designated directory for organized access and reading.
Overall, the Paper Reading project is a robust resource hub for anyone looking to deepen their understanding of deep learning infrastructure and its practical applications in the technical world.