Spark on YARN too less vcores used

I'm using Spark in a YARN cluster (HDP 2.4) with the following settings:

• 1 Masternode
  • 64 GB RAM (50 GB usable)
  • 24 cores (19 cores usable)
• 5 Slavenodes
  • 64 GB RAM (50 GB usable) each
  • 24 cores (19 cores usable) each
• YARN settings
  • memory of all containers (of one host): 50 GB
  • minimum container size = 2 GB
  • maximum container size = 50 GB
  • vcores = 19
  • minimum #vcores/container = 1
  • maximum #vcores/container = 19

When I run my spark application with the command spark-submit --num-executors 30 --executor-cores 3 --executor-memory 7g --driver-cores 1 --driver-memory 1800m ... YARN creates 31 containers (one for each executor process + one driver process) with the following settings:

• Correct: Master container with 1 core & ~1800 MB RAM
• Correct: 30 slave containers with ~7 GB RAM each
• BUT INCORRECT: each slave container only runs with 1 core instead of 3, according to the YARN ResourceManager UI (it shows only 31 of 95 in use, instead of 91 = 30 * 3 + 1), see screenshot below

My question here: Why does the spark-submit parameter --executor-cores 3 have no effect?

Ok, seems to be the same issue as discussed here: yarn is not honouring yarn.nodemanager.resource.cpu-vcores The solution also worked for me.