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H2O
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使用H2O的最佳方式是把它作为R环境的一个大内存扩展,R环境并不直接作用于大的数据集,而是通过扩展通讯协议例如REST API与H2O集群通讯,H2O来处理大量的数据工作。  | 使用H2O的最佳方式是把它作为R环境的一个大内存扩展,R环境并不直接作用于大的数据集,而是通过扩展通讯协议例如REST API与H2O集群通讯,H2O来处理大量的数据工作。  | ||
| + | |||
| + | ==简介==  | ||
| + | H2O - the killer app for Spark  | ||
| + | |||
| + | [[image:sparkling-killer-app.png]]  | ||
==版本==  | ==版本==  | ||
| + | *[https://github.com/h2oai/h2o-3 h2o-3] [https://github.com/h2oai/h2o-3/tree/master/h2o-docs/src h2o-3文档] [http://docs.huihoo.com/h2o/3/ 在线文档]  | ||
| + | *[https://github.com/h2oai/h2o-2 h2o-2] [https://github.com/h2oai/h2o-tutorials h2o-tutorials]  | ||
==安装==  | ==安装==  | ||
| + |  curl -o h2o.zip http://download.h2o.ai/versions/h2o-3.8.1.4.zip  | ||
| + |  unzip h2o.zip  | ||
| + |  cd h2o-3.8.1.4  | ||
| + |  java -jar h2o.jar  | ||
| + |  http://localhost:54321  | ||
| + | |||
| + | ==h2o.js==  | ||
| + | [https://github.com/h2oai/h2o.js h2o.js]: [[Node.js]] bindings to H2O  | ||
| + | |||
| + | ==H2O Flow==  | ||
| + | [https://github.com/h2oai/h2o-flow H2O Flow]  | ||
| + | |||
| + | ==机器学习==  | ||
| + | *[https://github.com/h2oai/deepwater/ Deep Water] [[Deep learning]] in H2O using Native [[GPU]] Backends    | ||
| + | *[http://docs.huihoo.com/h2o/3/data-science/deep-learning.html H2O Deep Learning 文档]  | ||
| + | |||
| + | ==项目==  | ||
| + | [https://github.com/h2oai/awesome-h2o Awesome H2O] [[文件:awesome.png]]  | ||
| − | ==  | + | ==文档==  | 
| + | *[http://docs.huihoo.com/knime/summits/knime-fall-summit-2017-austin/Integrating-high-performance-machine-learning-H2O-and-KNIME.pdf Integrating high performance machine learning: H2O and KNIME]  | ||
| + | *[http://docs.huihoo.com/h2o/h2o-meetups/ 更多幻灯片>>>]  | ||
==图集==  | ==图集==  | ||
<gallery>  | <gallery>  | ||
| + | image:Gartner-2018-Magic-Quadrant-for-Data-Science-and-Machine-Learning.png|Gartner魔力象限  | ||
| + | image:why-h2o.png|Why H2O  | ||
| + | image:H2O-High-Level-Architecture.png|架构  | ||
| + | image:H2O-Distributed-Algorithms.png|分布式算法  | ||
image:H2O-Architecture-Slide.png|架构  | image:H2O-Architecture-Slide.png|架构  | ||
| + | image:h2o-software-stack.png|软件堆栈  | ||
| + | image:h2o-sparkling-water-design.png|Sparkling Water  | ||
| + | image:h2o-sparkling-water-cluster.png|Sparkling Water集群  | ||
| + | image:h2o-frame-data-structures.jpg|H2OFrame  | ||
| + | image:h2o-spark-workflow.jpg|工作流  | ||
| + | image:h2o-run-glm-from-r.png|GLM处理流  | ||
| + | image:h2o-flow.png|H2O Flow  | ||
| + | image:h2o-flow-model.png|模型  | ||
| + | image:h2o-steam.png|Steam  | ||
| + | image:h2o-storm.png|H2O on Storm  | ||
</gallery>  | </gallery>  | ||
==链接==  | ==链接==  | ||
*[http://h2o.ai H2O官网]  | *[http://h2o.ai H2O官网]  | ||
| − | *[https://github.com/h2oai/  | + | *[https://github.com/h2oai/ H2O @ GitHub]  | 
| + | *[http://data.h2o.ai/ H2O World Training Materials]  | ||
[[category:big data]]  | [[category:big data]]  | ||
| 第26行: | 第68行: | ||
[[category:r]]  | [[category:r]]  | ||
[[category:java]]  | [[category:java]]  | ||
| + | [[category:python]]  | ||
| + | [[category:spark]]  | ||
| + | [[category:storm]]  | ||
| + | [[category:hadoop]]  | ||
2018年3月17日 (六) 08:14的最后版本
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您可以在Wikipedia上了解到此条目的英文信息 H2O Thanks, Wikipedia. | 
h2o = fast statistical, machine learning & math runtime for big data
H2O是一个开源分布式的内存处理引擎用于机器学习和大数据,它拥有一个令人印象深刻的数组的算法,支持R、Python和Java语言,同时它也可以作为Apache Spark在后端的执行引擎。
使用H2O的最佳方式是把它作为R环境的一个大内存扩展,R环境并不直接作用于大的数据集,而是通过扩展通讯协议例如REST API与H2O集群通讯,H2O来处理大量的数据工作。
目录 | 
[编辑] 简介
H2O - the killer app for Spark
[编辑] 版本
[编辑] 安装
curl -o h2o.zip http://download.h2o.ai/versions/h2o-3.8.1.4.zip unzip h2o.zip cd h2o-3.8.1.4 java -jar h2o.jar http://localhost:54321
[编辑] h2o.js
h2o.js: Node.js bindings to H2O
[编辑] H2O Flow
[编辑] 机器学习
- Deep Water Deep learning in H2O using Native GPU Backends
 - H2O Deep Learning 文档
 
[编辑] 项目
[编辑] 文档
[编辑] 图集
[编辑] 链接
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