Re: issue while running the code in standalone mode: "Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory"

Thanks for your response. I gave correct master url. Moreover as i mentioned in my post, i could able to run the sample program by using spark-submit. But it is not working when i'm running from my machine. Any clue on this? Thanks in advance. -- View this message in context: http://apache-sp

Re: issue while running the code in standalone mode: "Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory"

mail.com> wrote: > >> Hi, >> >> When i trying to execute the program from my laptop by connecting to HDP >> environment (on which Spark also configured), i'm getting the warning >> ("Initial job has not accepted any resources; check your cluster UI to >

Re: issue while running the code in standalone mode: "Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory"

1124023636-0004/2 is now RUNNING > 14/11/24 16:07:10 INFO client.AppClient$ClientActor: Executor updated: > app-20141124023636-0004/3 is now RUNNING > 14/11/24 16:07:24 WARN scheduler.TaskSchedulerImpl: Initial job has not > accepted any resources; check your cluster UI to ensure th

issue while running the code in standalone mode: "Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory"

Hi, When i trying to execute the program from my laptop by connecting to HDP environment (on which Spark also configured), i'm getting the warning ("Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory&qu

Re: Check your cluster UI to ensure that workers are registered and have sufficient memory

I occur to this issue with the spark on yarn version 1.0.2. Is there any hints? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Check-your-cluster-UI-to-ensure-that-workers-are-registered-and-have-sufficient-memory-tp5358p19133.html Sent from the Apache

Re: TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

Try to use --executor-memory 12g with spark-summit. Or you can set it in conf/spark-defaults.properties and rsync it to all workers and then restart. -Xiangrui On Fri, Jun 27, 2014 at 1:05 PM, Peng Cheng wrote: > I give up, communication must be blocked by the complex EC2 network topology > (thou

Re: TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

I give up, communication must be blocked by the complex EC2 network topology (though the error information indeed need some improvement). It doesn't make sense to run a client thousands miles away to communicate frequently with workers. I have moved everything to EC2 now. -- View this message in

Re: TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

Expanded to 4 nodes and change the workers to listen to public DNS, but still it shows the same error (which is obviously wrong). I can't believe I'm the first to encounter this issue. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/TaskSchedulerImpl-Initial

TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

river shows repeatedly: 14/06/25 04:46:29 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory Looks like its either a bug or misinformation. Can someone confirm this so I can submit a JIRA? --

Check your cluster UI to ensure that workers are registered and have sufficient memory

: DAGScheduler: Submitting 4 missing tasks from Stage 0 (MappedRDD[1] at textFile at :12) YarnClientClusterScheduler: Adding task set 0.0 with 4 tasks WARN YarnClientClusterScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient