Difference between revisions of "Ray - UC Berkeley RISELab"
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* [[Libraries & Frameworks]] | * [[Libraries & Frameworks]] | ||
| + | * [[Forecasting#Time Series Forecasting - Statistical|Time Series Forecasting - Statistical]] | ||
| + | * [[Reinforcement Learning (RL)]] | ||
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| + | 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] | ||
<youtube>1TtaHGPx7Co</youtube> | <youtube>1TtaHGPx7Co</youtube> | ||
| + | <youtube>Ayc0ca150HI</youtube> | ||
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Latest revision as of 21:45, 2 March 2024
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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.Ray | UC Berkeley RISELab
