Difference between revisions of "Ray - UC Berkeley RISELab"
(Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M...") |
m |
||
| Line 9: | Line 9: | ||
* [[Libraries & Frameworks]] | * [[Libraries & Frameworks]] | ||
| + | * [[Time Series Forecasting Methods - 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] | ||
<youtube>1TtaHGPx7Co</youtube> | <youtube>1TtaHGPx7Co</youtube> | ||
| + | <youtube>Ayc0ca150HI</youtube> | ||
| + | <youtube>3hDmFljRsSg</youtube> | ||
| + | <youtube>tqUe0gcfqAU</youtube> | ||
Revision as of 09:43, 17 August 2020
Youtube search... ...Google search
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
