10 citations · 13 across the 6 of their papers we have counts for
6 papers
Saturn: Efficient Multi-Large-Model Deep Learning
Kabir Nagrecha, Arun Kumar
In this paper, we propose Saturn, a new data system to improve the efficiency of multi-large-model training (e.g., during model selection/hyperparameter optimization). We first ide…
Saturn: An Optimized Data System for Large Model Deep Learning Workloads
Kabir Nagrecha, Arun Kumar
Large language models such as GPT-3 & ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. These models are rapidly being…
InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
Kabir Nagrecha, Lingyi Liu, Pablo Delgado +1
Deep learning-based recommender models (DLRMs) have become an essential component of many modern recommender systems. Several companies are now building large compute clusters rese…
Systems for Parallel and Distributed Large-Model Deep Learning Training
Kabir Nagrecha
Deep learning (DL) has transformed applications in a variety of domains, including computer vision, natural language processing, and tabular data analysis. The search for improved…
Hydra: A System for Large Multi-Model Deep Learning
Kabir Nagrecha, Arun Kumar
Scaling up model depth and size is now a common approach to raise accuracy in many deep learning (DL) applications, as evidenced by the widespread success of multi-billion or even…
Model-Parallel Model Selection for Deep Learning Systems
Kabir Nagrecha
As deep learning becomes more expensive, both in terms of time and compute, inefficiencies in machine learning (ML) training prevent practical usage of state-of-the-art models for…