Jtdylktuy

I can't find the working Spark option to require executors with a GPU.

I'm trying to setup a HADOOP cluster in order to run Machine Learning algorithms on available GPUs via Spark.

So far I'm trying out my setup with a minimal cluster (1 resource manager and 2 node managers (each with 8cores, 32Gb RAM, 1 Nvidia GPU), everybody running Ubuntu 18.04.

Resource discovery is working as expected (I see my 16 cores, 56Gb memory and 2 yarn.io/gpu)

The documentation provides a way, by using "--conf spark.yarn.executor.resource.yarn.io/gpu=1" but this does not work for me (no effect at all, both in spark-submit command parameter or in $SPARK_CONF/metrics.properties).

As YARN 3 is the first one to provide GPU isolation, I try to avoid a rollback to an older(/more documented) version.

I guess this could be set in code through SparkContext and would be happy to know how, but as I'm more on the admin side than ML engineer, I rather set this in conf files once and for all. Anyway at this point, any solution would be appreciated.

Anyone happy to provide the good syntax to allocate GPU with resources isolation enabled ?

Love you guys,
Kevin

(Yarn 3.1.1/3.2.0 on HortonWorks HDP)

As Spark doesn't like much YARN resources as of hadoop 3.0.0 (Spark is said to work with Hadoop 2.6+ but it implicitly means "up to 3.0 excluded"), a workaround was to set yarn.resource-types.yarn.io/gpu.minimum-allocation to 1, and from within my python code, cancel the executor order (spark doesn't launch the AM with 0 executor asked from command line)

sc = SparkContext(conf=SparkConf().setAppName("GPU on AM only").set("spark.executor.instances", 0))

Ugly but sufficient for our current workloads, hoping for a "Spark for Hadoop 3.0+" distribution soon enough.

EDIT: You can compile Spark for Hadoop 3.1 profile, from the current state of their github repository, then you have access to the spark.yarn..resource.yarn.io/gpu properties !

​I'll share my findings about isolation here too:

After about 2 weeks of various tries we finally settled on a full wipe of every host for a clean install from scratch.
​Still nothing working.
​
​Then we tried a "one worker" setup to set a countable resource manually to try the allocation mechanism and then....
​NOTHING hortonWORKS !
​
​But my Googling was better suited then.
​It seems to be a Hadoop related issue about custom resources and CapacityScheduler, enjoy:

https://issues.apache.org/jira/browse/YARN-9161
https://issues.apache.org/jira/browse/YARN-9205

For now (3.1.1/3.2.0) the capacity.CapacityScheduler is broken by a hardcoded enum containing only vCores and RAM parameters.
You just have to switch your scheduler class to org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler
You also want to replace "capacity" by "Fair" in the line
yarn.scheduler.fair.resource-calculator=org.apache.hadoop.yarn.util.resource.DominantResourceCalculator

Your GPUs will not be visible on yarn ui2 but will still be on the NodeManagers, and most importantly, will be allocated properly.
It was a mess to find out indeed.