507 citations · 1k across the 22 of their papers we have counts for
6 papers · 1 filter
Elixir: Train a Large Language Model on a Small GPU Cluster
Haichen Huang, Jiarui Fang, Hongxin Liu +2
In recent years, large language models have achieved great success due to their unprecedented size. However, training these models poses a challenge for most researchers as it requ…
EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
Jiangsu Du, Ziming Liu, Jiarui Fang +4
Large transformer models display promising performance on a wide range of natural language processing (NLP) tasks. Although the AI community has expanded the model scale to the tri…
A Frequency-aware Software Cache for Large Recommendation System Embeddings
Jiarui Fang, Geng Zhang, Jiatong Han +5
Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a…
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
Yizheng Huang, Huaizheng Zhang, Yuanming Li +2
The success of today's AI applications requires not only model training (Model-centric) but also data engineering (Data-centric). In data-centric AI, active learning (AL) plays a v…
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
Shenggan Cheng, Xuanlei Zhao, Guangyang Lu +7
Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, whic…
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning
Jie Zhu, Shenggui Li, Yang You
Federated learning is proposed by Google to safeguard data privacy through training models locally on users' devices. However, with deep learning models growing in size to achieve…