microsoft/AI-System
AI System
AI-System est une ressource éducative et une boîte à outils conçue pour apprendre les fondements matériels et logiciels des systèmes d'apprentissage profond. Il fournit un programme et des exercices pratiques pour construire une infrastructure IA, allant du développement de noyaux CUDA bas niveau à la gestion système haut niveau.
Le projet inclut une boîte à outils pour développer des opérations sur tenseurs et optimiser les performances GPU via la programmation matérielle directe. Il dispose également d'un framework pour l'entraînement distribué, se concentrant sur la planification des ressources et les protocoles de communication pour gérer des modèles à grande échelle sur plusieurs nœuds de calcul.
Le système couvre l'analyse de sécurité de l'IA pour identifier les vulnérabilités de confidentialité et les attaques adverses, ainsi que l'optimisation des performances via la compilation consciente du matériel, la compression pilotée par la sparsité et les graphes de calcul basés sur des tenseurs. Il fournit en outre des outils pour gérer l'infrastructure IA et coordonner les stratégies de déploiement pour des environnements d'inférence haute performance.
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.
