microsoft/AI-System
AI System
AI-System is an educational resource and toolkit designed for learning the hardware and software foundations of deep learning systems. It provides a curriculum and practical exercises for building AI infrastructure, ranging from low-level CUDA kernel development to high-level system management.
The project includes a toolkit for developing tensor operations and optimizing GPU performance through direct hardware programming. It also features a framework for distributed training, focusing on resource scheduling and communication protocols to manage large-scale models across multiple computing nodes.
The system covers AI security analysis to identify privacy vulnerabilities and adversarial attacks, as well as performance optimization via hardware-aware compilation, sparsity-driven compression, and tensor-based computation graphs. It further provides tools for managing AI infrastructure and coordinating deployment strategies for high-performance inference environments.
Features
- AI & Machine Learning Education - Provides a comprehensive curriculum and practical exercises for learning the hardware and software foundations of deep learning systems.
- Deep Learning Infrastructure - Implements tools and strategies for resource scheduling and deployment tailored for high-performance inference environments.
- Distributed Training Frameworks - Provides a system for implementing large-scale model training across multiple computing nodes using specialized algorithms.
- Distributed Training Orchestration - Implements systems for managing parallelization and synchronization of large-scale model training across computing clusters.
- Distributed Training - Implements the setup and execution of large-scale model training across multiple computing nodes.
- AI Security Frameworks - Provides a framework for analyzing vulnerabilities and implementing protection methods for artificial intelligence models.
- AI System Components - Provides practical exercises for constructing low-level AI primitives such as tensor operations and CUDA kernels.
- Deep Learning Curriculum - Provides a structured learning path and exercises for the hardware and software foundations of deep learning.
- Deep Learning Computation Tutorials - Offers educational content on how matrix operations and computer architectures are optimized for deep neural networks.
- System Design Learning - Offers educational resources on the hardware, software, and algorithmic foundations for designing robust deep learning systems.
- CUDA Compute Kernels - Provides implementation of custom GPU kernels in C++ to parallelize heavy mathematical tensor operations.
- AI Infrastructure Managers - Provides a framework for coordinating resource scheduling and deployment strategies for high-performance inference environments.
- AI Security and Governance - Identifies and fixes security and privacy vulnerabilities within artificial intelligence models to implement effective protections.
- Computational Graphs - Represents mathematical operations as directed graphs to optimize memory allocation and matrix multiplication sequences.
- Hardware-Aware Compilers - Transforms high-level algorithmic descriptions into machine code optimized for specific GPU and TPU architectures.
- Neural Network Compression - Reduces model size and computational overhead using pruning, quantization, and sparsity-driven compression.
- Performance Optimization - Provides techniques for applying compilation, sparsity, and compression to increase overall AI system efficiency.
- AI Model Security Analysis - Implements verification checkpoints to detect and mitigate privacy vulnerabilities and adversarial attacks within the model pipeline.
- AI Inference Infrastructure - Teaches resource scheduling and deployment strategies to maintain high-performance AI inference environments.
- AI Security - Teaches how to analyze and mitigate privacy vulnerabilities and adversarial attacks in artificial intelligence models.
