More detail -
```17/07/03 04:08:15 WARN YarnAllocator: Container marked as failed:
container_1499050738160_0001_01_000011 on host:
ip-10-0-24-85.ap-southeast-2.compute.internal. Exit status: 52.
Diagnostics: Exception from container-launch.
Container id: container_1499050738160_0001_01_000011
Exit code: 52
Stack trace: ExitCodeException exitCode=52:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:582)
at org.apache.hadoop.util.Shell.run(Shell.java:479)
at
org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:773)
at
org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
at
org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
at
org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)```
On Thursday, July 6, 2017 at 9:45:10 AM UTC+10, iamhasib wrote:
>
> We just deployed Jupyter with EMR in AWS and running it memory issues
> whenever we run any job in Jupyter.
>
> We have pretty large servers (3 servers, all m4.2xlarge servers) and test
> data volume is pretty tiny.
>
> The error messages are shown on ecnlosed images.
>
> If someone could shed lights, I appreciate a lot.
>
>
>
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