What is DeepSpeed?
DeepSpeed provides a suite of system innovations for optimizing deep learning processes, enabling the handling of models at an unprecedented scale. It facilitates the training and inference of dense or sparse models with billions or trillions of parameters, significantly improving system throughput and scalability across thousands of GPUs. The platform is also designed for efficiency on resource-constrained GPU systems.
Key innovations encompass training optimizations like ZeRO (Zero Redundancy Optimizer), 3D-Parallelism, and specialized techniques for Mixture-of-Experts (MoE) models. For inference, it integrates parallelism technologies with high-performance custom kernels and communication optimizations to achieve low latency and high throughput. Additionally, it offers compression techniques to reduce model size and inference costs, alongside specific initiatives like DeepSpeed4Science aimed at applying AI system technology to scientific discovery.
Features
- ZeRO Optimizations: Reduce memory redundancy for training massive models.
- 3D Parallelism: Combines data, pipeline, and tensor parallelism for scaling.
- DeepSpeed-MoE: Efficiently train and infer Mixture-of-Experts models.
- ZeRO-Infinity: Offload model states to CPU/NVMe memory for extreme scale training.
- DeepSpeed-Inference: Optimized kernels and parallelism for low-latency, high-throughput inference.
- DeepSpeed-Compression: Techniques like ZeroQuant and XTC for model size reduction and faster inference.
- DeepSpeed4Science: AI system innovations tailored for scientific discovery applications.
- Model Implementations for Inference (MII): Simplified deployment of optimized models.
- Autotuning: Automatically configures system parameters for optimal performance.
Use Cases
- Training large language models (e.g., GPT, BLOOM, MT-NLG).
- Accelerating deep learning model training pipelines.
- Deploying large models for low-latency inference.
- Reducing the memory footprint of large models during training and inference.
- Scaling deep learning tasks across large GPU clusters.
- Compressing pre-trained models for efficient deployment.
- Enabling large-scale scientific computations using AI models.
- Training models on systems with limited GPU memory.
- Optimizing Mixture-of-Experts (MoE) model performance.
