Ray - UC Berkeley RISELab - Revision history

BPeat at 02:45, 3 March 2024

2024-03-03T02:45:46Z

BPeat at 23:57, 12 September 2020

2020-09-12T23:57:50Z

BPeat at 14:43, 17 August 2020

2020-08-17T14:43:21Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2020-08-17T14:36:56Z

Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

New page

{{#seo:
|title=PRIMO.ai
|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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[http://www.youtube.com/results?search_query=Ray+RISELab+artificial+intelligence+deep+machine+learning Youtube search...]
[http://www.google.com/search?q=Ray+RISELab+artificial+intelligence+deep+machine+learning ...Google search]

* [[Libraries & Frameworks]]

<youtube>1TtaHGPx7Co</youtube>

← Older revision		Revision as of 02:45, 3 March 2024
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	* [[Reinforcement Learning (RL)]]		* [[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]	+	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]

@@ Line 9: / Line 9: @@
 * [[Libraries & Frameworks]]
-* [[Time Series Forecasting Methods - Statistical]]
+* [[Forecasting#Time Series Forecasting - Statistical|Time Series Forecasting - Statistical]]
 * [[Reinforcement Learning (RL)]]

← Older revision		Revision as of 14:43, 17 August 2020
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>