title: Zeppelin Interpreter Memory - driver memory
body: <p>Im unsuccessfully trying to increase the driver memory for my spark interpreter.
  I just set spark.driver.memory in interpreter settings and everything looks great at first.
  But in the docker container that zeppelin runs there is</p>
  
  <p>Zeppelin 0.6.2
  Spark 2.0.1</p>
  
  <p>2:06 /usr/lib/jvm/java-8-openjdk-amd64/bin/java -cp /usr/zeppelin/int.....-2.7.2/share/hadoop/tools/lib/* <strong>-Xmx1g</strong> ..... --class org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer /usr/zeppelin/interpreter/spark/zeppelin-spark_2.11-0.6.2.jar 42651</p>
  
  <p>a max heap setting that kind of breaks everything.
  My main issue is I am trying to run the Latent Dirchilet Allocation of mllib and it always runs out of memory and just dies on the driver.
  The docker container has 26g RAM now so that should be enough.
  Zeppelin itself should be fine with its 1g ram.
  But the spark driver simply needs more.</p>
  
  <p>My Executor process have RAM but the driver is reported in the UI as</p>
  
  <p>Executor ID Address Status  RDD Blocks  <strong>Storage Memory</strong>  Disk Used   Cores   Active Tasks    Failed Tasks    Complete Tasks  Total Tasks Task Time (GC Time) Input   Shuffle Read    Shuffle Write   Thread Dump
  driver  172.17.0.6:40439    Active  0   <strong>0.0 B / 404.7 MB</strong>    0.0 B   20  0   0   1   1   1.4 s (0 ms)    0.0 B   0.0 B   0.0 B   Thread Dump</p>
  
  <p>pretty abysmal</p>
  
  <p>Setting ZEPPELIN_INTP_MEM='-Xms512m -Xmx12g' does not seem to change anything.
  I though zeppelin-env.sh is not loaded correctly so I passed this variable directly in the docker create -e ZE... but that did not change anything.</p>
  
  <p>SPARK_HOME is set and the it connects to a standalone spark cluster. But that part works. Only the driver runs out of memory.</p>
  
  <p>But I tried starting a local[*] process with 8g driver memory and 6g executor but the same abysmal 450mb driver memory.</p>
  
  <p>the intrepreter reports a java heap out of memory error and that breaks that halts the LDAModel training.</p>