599 citations · 1.8k across the 17 of their papers we have counts for
18 papers
Ray Data: A dynamic and scalable system for heterogeneous data processing
Frank Sifei Luan, Ron Yifeng Wang, Yile Gu +12
While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Existing distributed data processing systems assume homogeneous res…
Exoshuffle-CloudSort
Frank Sifei Luan, Stephanie Wang, Samyukta Yagati +7
We present Exoshuffle-CloudSort, a sorting application running on top of Ray using the Exoshuffle architecture. Exoshuffle-CloudSort runs on Amazon EC2, with input and output data…
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff +4
Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult generalization. Recently, Unsupervised Environment Design (UED) emerged as a new paradi…
Balsa: Learning a Query Optimizer Without Expert Demonstrations
Zongheng Yang, Wei-Lin Chiang, Sifei Luan +3
Query optimizers are a performance-critical component in every database system. Due to their complexity, optimizers take experts months to write and years to refine. In this work,…
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
Abdus Salam Azad, Edward Kim, Qiancheng Wu +4
The capability of a reinforcement learning (RL) agent heavily depends on the diversity of the learning scenarios generated by the environment. Generation of diverse realistic scena…
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Eric Liang, Zhanghao Wu, Michael Luo +3
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the…
Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica +3
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…
NeuroCard: One Cardinality Estimator for All Tables
Zongheng Yang, Amog Kamsetty, Sifei Luan +4
Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex que…
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
Siyuan Zhuang, Zhuohan Li, Danyang Zhuo +5
Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including…
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
Michael Luo, Jiahao Yao, Richard Liaw +2
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinfor…
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho, Eric Liang, Ion Stoica +2
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. Properly…
Deep Unsupervised Cardinality Estimation
Zongheng Yang, Eric Liang, Amog Kamsetty +7
Cardinality estimation has long been grounded in statistical tools for density estimation. To capture the rich multivariate distributions of relational tables, we propose the use o…
Learning to Optimize Join Queries With Deep Reinforcement Learning
Sanjay Krishnan, Zongheng Yang, Ken Goldberg +2
Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space. The design and implementation of heuristics…
Tune: A Research Platform for Distributed Model Selection and Training
Richard Liaw, Eric Liang, Robert Nishihara +3
Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonabl…
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz +6
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation…
Ray: A Distributed Framework for Emerging AI Applications
Philipp Moritz, Robert Nishihara, Stephanie Wang +8
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requir…
Real-Time Machine Learning: The Missing Pieces
Robert Nishihara, Philipp Moritz, Stephanie Wang +7
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dyn…
SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica +1
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a clus…