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Description

Sample scripts to run Spark jobs on Hyak

Mox

Prerequisites

  1. Add export SPARK_HOME=/sw/contrib/spark and export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH to your .bashrc file.
  2. Get a build node: srun -p build -c 1 --pty /bin/bash
  3. Within the build node, load anaconda: module load anaconda2_4.3.1
  4. Download required package: pip install py4j --user

To replicate examples:

  1. Create a new conda environment, e.g.: conda create -n deeplearning keras tensorflow
  2. Activate virtual env and download required packages:
pip install py4j
pip install nltk
python -m nltk.downloader punkt

Ikt

Interactive Job Submission

  1. Set $JAVA_HOME variable in your ~/.bashrc file to point to /sw/contrib/java/jdk1.8.0_111
  2. Download Spark into your home directory and unpack:
wget http://d3kbcqa49mib13.cloudfront.net/spark-2.0.1-bin-hadoop2.7.tgz
tar -xzvf spark-2.0.1-bin-hadoop2.7.tgz
  1. Get nodes:
qsub -I -l nodes=2:ppn=16,walltime=2:00:00
  1. Find node ids:
cat $PBS_NODEFILE | sort | uniq
  1. Assign current node as master from within your spark directory:
cd spark-2.0.1-bin-hadoop2.7
./sbin/start-master.sh
  1. Find master node URL (replacing username with netid and n0*** with your master node number):
cat logs/spark-*username*-org.apache.spark.deploy.master.Master-1-n0***.out | grep -o "spark://.*"
  1. For each other assigned node ssh to that node and start a worker process (replace MASTER_NODE_URL with URL found in step 6):
ssh n0***
cd spark-2.0.1-bin-hadoop2.7
./sbin/start-slave MASTER_NODE_URL
  1. Return to master node and start interactive session:
ssh n0***
cd spark-2.0.1-bin-hadoop2.7
./bin/spark-shell

Note: You can check to make sure you have assigned all nodes as workers by visiting http://localhost:8080:

curl localhost:8080