Deepspeed：微软的大规模分布式训练工具。

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模型训练工具

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https://github.com/microsoft/DeepSpeed

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DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; learn how.

Extreme Speed and Scale for DL Training and Inference

DeepSpeed enables world's most powerful language models like MT-530B and BLOOM. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed you can:

  • Train/Inference dense or sparse models with billions or trillions of parameters
  • Achieve excellent system throughput and efficiently scale to thousands of GPUs
  • Train/Inference on resource constrained GPU systems
  • Achieve unprecedented low latency and high throughput for inference
  • Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs

DeepSpeed's four innovation pillars

DeepSpeed-Training

DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: DeepSpeed-Training

DeepSpeed-Inference

DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar. Learn more: DeepSpeed-Inference

DeepSpeed-Compression

To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar. Learn more: DeepSpeed-Compression

DeepSpeed4Science

In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called DeepSpeed4Science, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: DeepSpeed4Science website and tutorials

DeepSpeed Software Suite

DeepSpeed Library

The DeepSpeed library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see DeepSpeed Adoption).

Model Implementations for Inference (MII)

Model Implementations for Inference (MII) is an open-sourced repository for making low-latency and high-throughput inference accessible to all data scientists by alleviating the need to apply complex system optimization techniques themselves. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions.

DeepSpeed on Azure

DeepSpeed users are diverse and have access to different environments. We recommend to try DeepSpeed on Azure as it is the simplest and easiest method. The recommended method to try DeepSpeed on Azure is through AzureML recipes. The job submission and data preparation scripts have been made available here. For more details on how to use DeepSpeed on Azure, please follow the Azure tutorial.