DeepSpeed

DeepSpeed Official Website, an open-source deep learning optimization library developed by Microsoft for large-scale model distributed training

What is DeepSpeed?

DeepSpeed is a tool for large-scale model distributed training launched by Microsoft, mainly implementing the ZeRO parallel training algorithm. It is an open-source deep learning optimization library developed by Microsoft, aiming to improve the efficiency and scalability of large-scale model training. It accelerates training through various techniques, including model parallelization, gradient accumulation, dynamic precision scaling, and local mixed precision. DeepSpeed also provides auxiliary tools such as distributed training management, memory optimization, and model compression to help developers better manage and optimize large-scale deep learning training tasks. Additionally, DeepSpeed is built on PyTorch and can be easily migrated with simple modifications. DeepSpeed has been applied in many large-scale deep learning projects, including language models, image classification, object detection, and more.

DeepSpeed Official Website:https://www.deepspeed.ai/

GitHub Open Source Address:https://github.com/microsoft/DeepSpeed

DeepSpeed enables model training similar to ChatGPT with just one click, increasing speed by 15 times compared to SOTA RLHF systems, and reducing costs across all scales.

DeepSpeed is a deep learning optimization library that makes distributed training and inference simple, efficient, and effective.

Three Core Functions of DeepSpeed Chat

DeepSpeed Chat is a powerful conversational AI model with core functions for simplifying training and enhancing inference experience. It provides optimizations through the following three aspects:

1. Simplified training and inference: DeepSpeed Chat simplifies the training process and provides an inference API, allowing developers to easily perform multiple training steps and conduct conversational interaction tests after completion. This way, developers can achieve training and inference with just one script, simplifying the entire development process.
2. DeepSpeed-RLHF module: The DeepSpeed-RLHF module adopts the training mode from the InstructGPT paper and provides data abstraction and mixing functions, supporting developers to use multiple different data sources for training. This allows developers to choose and combine data more flexibly, improving the training effect and performance of the model.
3. DeepSpeed-RLHF system: The DeepSpeed team integrates the training engine and inference engine into a unified mixed engine (DeepSpeed Hybrid Engine, abbreviated as DeepSpeed-HE) for RLHF training. This system can switch seamlessly between inference and training modes and utilize various optimizations of DeepSpeed-Inference. This way, DeepSpeed-HE demonstrates unparalleled efficiency and performance in large-scale training.

The advantages of the DeepSpeed-RLHF system lie in its efficiency, economy, and scalability:

Efficient and economical: DeepSpeed-HE is more than 15 times faster than existing systems, making RLHF training fast and cost-effective. For example, training an OPT-13B model on Azure cloud with DeepSpeed-HE takes only 9 hours, and training an OPT-30B model takes only 18 hours, with costs of less than $300 and $600, respectively.

Excellent scalability: DeepSpeed-HE can support training models with over ten billion parameters and demonstrates excellent scalability on multi-node multi-GPU systems. Even a model with 13 billion parameters can be trained in just 1.25 hours. For a model with 175 billion parameters, training with DeepSpeed-HE also takes less than a day.

Popularizing RLHF training: DeepSpeed-HE can support training models with over 13 billion parameters with just a single GPU. This allows data scientists and researchers who cannot use multi-GPU systems to easily create lightweight and large, powerful RLHF models to meet different usage scenario needs.

DeepSpeed Chat, with its powerful functions and optimizations, makes training and inference of conversational AI models more efficient, economical, and scalable, thus promoting the development and popularization of RLHF training.