从YARN开始的H2O无法正常工作 [英] h2o starting on YARN not working

问题描述

在CDH群集上启动H2o时，出现以下错误.我从他的网站下载了所有内容，并按照教程进行操作.我运行的命令是

When I start H2o on a cdh cluster I get the following error. I downloaded everything formt he wbesite and followed the tutorial. The command I ran was

hadoop jar h2odriver.jar -nodes 2 -mapperXmx 1g -output hdfsOutputDirName  

它表明没有使用容器.尚不清楚这些设置将在hadoop上进行.我已经给所有设置记忆.内存的0.0是没有意义的，为什么容器不使用内存.集群现在是否还在运行?

It shows that containers are not being used. It's not clear what settings these would be on hadoop. I have given all settings memory. It's the 0.0 for memory that doesnt make sense, and why are the containers not using memory. Is the cluster even running now?

----- YARN cluster metrics -----
Number of YARN worker nodes: 3

----- Nodes -----
Node: http://data-node-3:8042 Rack: /default, RUNNING, 1 containers used, 1.0 / 6.0 GB used, 1 / 4 vcores used
Node: http://data-node-1:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used
Node: http://data-node-2:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used

----- Queues -----
Queue name:            root.default
    Queue state:       RUNNING
    Current capacity:  0.00
    Capacity:          0.00
    Maximum capacity:  -1.00
    Application count: 0

Queue 'root.default' approximate utilization: 0.0 / 0.0 GB used, 0 / 0 vcores used

----------------------------------------------------------------------

WARNING: Job memory request (2.2 GB) exceeds queue available memory capacity (0.0 GB)
WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0)

----------------------------------------------------------------------

For YARN users, logs command is 'yarn logs -applicationId application_1462681033282_0008'

推荐答案

我在Cloudera Manager纱线配置中进行了以下更改

I made the following changes in Cloudera Manager yarn configuration

Setting                                     Value
yarn.scheduler.maximum-allocation-vcores    8 
yarn.nodemanager.resource.cpu-vcores        4
yarn.nodemanager.resource.cpu-vcores        4
yarn.scheduler.maximum-allocation-mb        16 GB

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