Revolutionizing Large-Scale Deep Learning with Microsoft DeepSpeed

Author(s): Dr. Mandar Karhade, MD. PhD.

Originally published on Towards AI.

Microsoft democratizes and standardizes at-scale LLM training

No, not the hydroget! I am not that cool…. DeepSpeed, developed by Microsoft, is a deep learning optimization library that has redefined the possibilities in training and inference of large-scale models. This advanced software suite is designed to handle extreme scale and speed in deep learning (DL) tasks, facilitating the training and deployment of models with billions or even trillions of parameters​

DeepSpeed’s capabilities are vast and varied. It enables the training and inference of large models more efficiently, reducing the computational and memory resources required. This is achieved through system throughput optimizations, the ability to scale across thousands of GPUs, and the capability to operate on resource-constrained systems. Furthermore, DeepSpeed optimizes inference processes by reducing latency, increasing throughput, and employing model compression techniques to minimize size and computational expenses​

DeepSpeed is built on four innovation pillars, each addressing different aspects of deep learning optimization:

DeepSpeed-Training: This pillar focuses on enhancing the efficiency and usability of large-scale DL training. It encompasses technologies like ZeRO, 3D-Parallelism, DeepSpeed-MoE (Mixture of Experts), and ZeRO-Infinity, contributing to the effective and efficient training of large models​DeepSpeed-Inference: It brings together various innovations in parallelism technology, such as tensor, pipeline, expert, and ZeRO-parallelism. These are combined with high-performance custom inference… Read the full blog for free on Medium.

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Published via Towards AI