多节点上的h2o和hadoop上的h2o有什么区别？ [英] What's the difference between h2o on multi-nodes and h2o on hadoop?

问题描述

在H2O网站上，

H2O的核心代码是用Java编写的。在H2O内部，分布式键/值存储用于跨所有节点和机器访问和引用数据，模型，对象等。这些算法在H2O的分布式Map / Reduce框架之上实现，并利用Java Fork / Join框架进行多线程。

H2O’s core code is written in Java. Inside H2O, a Distributed Key/Value store is used to access and reference data, models, objects, etc., across all nodes and machines. The algorithms are implemented on top of H2O’s distributed Map/Reduce framework and utilize the Java Fork/Join framework for multi-threading.

这意味着如果H2O在单节点群集上运行，它将不能比其他库更好地工作？但是可以在多个节点群集上很好地工作。

Does this mean H2O will not work better than other libraries if it runs on single node cluster? But will work well on multiple nodes cluster. Is that right?

在多节点上的h2o和在hadoop上的h2o有什么区别？

Also what's the difference between h2o on multi-nodes and h2o on hadoop?

推荐答案

请参阅有关如何在Hadoop上运行H2O的文档： http://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html#hadoop-users

please see the documentation on how to run H2O on Hadoop:http://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html#hadoop-users

以及此演示文稿

您可以将 H2O on Hadoop视为H2O认证的Hadoop集成。但是，您不需要Hadoop在多节点环境中运行H2O，如果需要，可以始终手动执行此操作。

you can think of "H2O on Hadoop" as H2O's certified integration for Hadoop. However, you don't need Hadoop to run H2O in a multi-node environment, you could always do this manually if you wanted to.

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