Azure empowers easy-to-use, high-performance, and hyperscale model training using DeepSpeed


Written by Kushal Datta, Principal Software Engineer


This blog was written in collaboration with the DeepSpeed team, the Azure ML team, and the Azure HPC team at Microsoft.


Large-scale transformer-based deep learning models trained on large amounts of data have shown great results in recent years in several cognitive tasks and are behind new products and features that augment human capabilities. These models have grown several orders of magnitude in size during the last five years. Starting from a few million parameters of the original transformer model all the way to the latest 530 billion-parameter Megatron-Turing (MT-NLG 530B) model as shown in Figure 1. There is a growing need for customers to train and fine-tune large models at an unprecedented scale.


Figure 1: Landscape of large models and hardware capabilities.Figure 1: Landscape of large models and hardware capabilities.


Azure Machine Learning (AzureML) brings large fleets of the latest GPUs powered by the InfiniBand interconnect to tackle large-scale AI training. We already train some of the largest models including Megatron/Turing and GPT-3 on Azure. Previously, to train these models, users needed to set up and maintain a complex distributed training infrastructure that usually required several manual and error-prone steps. This led to a subpar experience both in terms of usability and performance.


Today, we are proud to announce a breakthrough in our software stack, using DeepSpeed and 1024 A100s to scale the training of a 2T parameter model with a streamlined user experience at 1K+ GPU scale. We are bringing these software innovations to you through AzureML (including a fully optimized PyTorch environment) that offers great performance and an easy-to-use interface for large-scale training.


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