1. Add the --executor-memory command-line argument while submitting a job to spark-submit to set on-heap memory for the worker node. For example, we could use --executor-memory 4g to allocate 4 GB of memory.
   
2. Add the --conf command-line argument to set the off-heap memory for the worker node:

--conf "spark.executor.extraJavaOptions=-Dorg.bytedeco.javacpp.maxbytes=8G"

3. Add the --conf command-line argument to set the off-heap memory for the master node. For example, we could use --conf "spark.driver.memoryOverhead=-Dorg.bytedeco.javacpp.maxbytes=8G" to allocate 8 GB of memory.
4. Add the --driver-memory command-line argument to specify the on-heap memory for the master node. For example, we could use --driver-memory 4g to allocate 4 GB of memory.
5. Configure garbage collection for the worker nodes by calling workerTogglePeriodicGC() and workerPeriodicGCFrequency() while you set up the distributed neural network using SharedTrainingMaster:

new SharedTrainingMaster.Builder(voidConfiguration, minibatch)
   .workerTogglePeriodicGC(true) 
   .workerPeriodicGCFrequency(frequencyIntervalInMs) 
   .build();

6. Enable Kryo optimization in DL4J by adding the following dependency to the pom.xml file:

<dependency>
   <groupId>org.nd4j</groupId>
   <artifactId>nd4j-kryo_2.11</artifactId>
  <version>1.0.0-beta3</version>
 </dependency>

7. Configure KryoSerializer with SparkConf:

SparkConf conf = new SparkConf();
 conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
 conf.set("spark.kryo.registrator", "org.nd4j.Nd4jRegistrator");

8. Add locality configuration to spark-submit, as shown here:

--conf spark.locality.wait=0