microsoft/DeepSpeedExamples
DeepSpeedExamples
DeepSpeedExamples is a collection of reference implementations for training and deploying large scale AI models using the DeepSpeed optimization library. It provides Python code examples for training massive models across multiple GPUs through distributed optimization techniques.
The repository includes optimized patterns for deploying and running large language model predictions in production environments. It also serves as a guide for model compression to reduce memory footprints and as a source for performance benchmarks to measure execution speed and resource utilization.
The project covers distributed AI optimization, large scale model training, and model inference. These implementations incorporate memory management, pipeline-parallel execution, and quantization-based compression.
Features
- Distributed Training - Implements Python code examples for configuring data and model parallelism to train massive models across multiple GPUs.
- Large Scale Training - Provides reference implementations and configurations for training massive models across distributed infrastructure.
- Distributed Memory Optimizers - Utilizes ZeRO-based memory optimization to partition model states and gradients across distributed workers.
- Data-Parallel Training - Implements distributed data parallelism to synchronize gradients across multiple hardware nodes for large batch training.
- Large-Scale Model Training - Implements specialized methodologies for training large-scale models that exceed single-device capacity.
- Model Inference - Ships standardized implementation examples for running predictions with large-scale models in production.
- Model Inference Optimizations - Provides optimized implementation patterns for running large language model predictions in production environments.
- Memory Optimization Techniques - Implements memory management and offloading strategies to reduce the footprint of massive neural networks.
- Model Inference - Provides optimized engines and integrations for generating high-performance predictions from trained models.
- Weight Offloading - Implements weight offloading to move model parameters between GPU and CPU memory to enable larger models.
- Pipeline Parallelism Partitioners - Provides implementations for partitioning neural networks into sequential layers across GPUs for pipeline-parallel training.
- Model Inference Deployment - Provides optimized implementation patterns for deploying large-scale models into production environments.
- Distributed Training Examples - Provides a comprehensive collection of reference implementations for training and deploying massive AI models.
- Model Performance Benchmarking - Includes scripts for measuring execution speed and resource utilization to benchmark model performance.
- Model Quantization - Implements quantization techniques to reduce model weight precision for lower memory usage and faster inference.
- Model Compression - Includes methods for reducing the size and computational requirements of neural networks to increase efficiency.
- Training Configurations - Provides JSON-based configuration files to manage hardware scaling and optimization parameters for training.
- Model Execution Benchmarks - Provides reference scripts to measure the execution speed and resource utilization of neural network model workloads.
- LLM Training and Optimization - Practical examples for RLHF training with DeepSpeed.
- Open Source Models - Optimized training and inference examples for large language models.
- RLHF Frameworks - Examples for affordable and efficient reinforcement learning training.
