View source for Ray - UC Berkeley RISELab

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* [[Libraries & Frameworks]]
* [[Forecasting#Time Series Forecasting - Statistical|Time Series Forecasting - Statistical]]
* [[Reinforcement Learning (RL)]]

Ray is a high-performance distributed execution framework targeted at large-scale machine learning and reinforcement learning applications. It achieves scalability and fault tolerance by abstracting the control state of the system in a global control store and keeping all other components stateless. It uses a shared-[[memory]] distributed object store to efficiently handle large data through shared [[memory]], and it uses a bottom-up hierarchical scheduling architecture to achieve low-latency and high-throughput scheduling. It uses a lightweight API based on dynamic task graphs and actors to express a wide range of applications in a flexible manner.[http://rise.cs.berkeley.edu/projects/ray/ Ray | UC Berkeley RISELab]


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