PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 20, 2020798 citations

DeepSpeed

View Full Paper
JRJeff RasleySRSamyam RajbhandariOROlatunji Ruwase

Key Points

  • Introduce DeepSpeed, an open-source PyTorch-compatible optimization library engineered to improve the scale, speed, cost, and usability of training extreme-scale deep learning models.
  • Implemented the Zero Redundancy Optimizer (ZeRO) to partition memory states across data-parallel processes without increasing communication overhead.
  • Developed high-performance custom transformer kernels to accelerate computational throughput during model pretraining.
  • Enabled efficient scaling and distributed training for neural networks exceeding 100 billion parameters, including the 17-billion-parameter Turing-NLG model.
  • Achieved record-breaking pretraining execution speeds for BERT transformer architectures.

Abstract

Explore new techniques in Microsoft's open source library called DeepSpeed, which advances large model training by improving scale, speed, cost, and usability, unlocking the ability to train 100-billion-parameter models. DeepSpeed is compatible with PyTorch. One piece of our library, called ZeRO, is a new parallelized optimizer that greatly reduces the resources needed for model and data parallelism while massively increasing the number of parameters that can be trained. Researchers have used these breakthroughs to create Turing Natural Language Generation (Turing-NLG), which at the time of its release was the largest publicly known language model at 17 billion parameters. In addition we will also go over our latest transformer kernel advancements that led the DeepSpeed team to achieve the world fastest BERT pretraining record.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Rasley et al. (2020) studied this question.

synapsesocial.com/papers/6a0ec1fd2eca052da647c0a7https://doi.org/10.1145/3394486.3406703
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Mixed Precision Training2017 · 885 citations
  2. 2Deep Learning with Limited Numerical Precision2015 · 1,080 citations
  3. 3Rethinking the Inception Architecture for Computer Vision2016 · 31,518 citations
  4. 4Going deeper with convolutions2015 · 47,259 citations
  5. 5Long Short-Term Memory1997 · 101,683 citations