Hasari SLURM Batch file.

dbn-partitioned-none_class.slurm.sh

#!/bin/bash
#----------------------------------------------------
# Hasari SLURM Batch Job
# Usage: put this file somewhere in your $STORE. It is recommended to create job-specific folders, 
#        and to put your batch files in those folders.  Then your batch, output, error, etc will 
#        all be in one folder.
#----------------------------------------------------
#SLURM SBATCH Parameters
#----------------------------------------------------
#SBATCH -J dbn-partitioned-none_class     # Job name
#SBATCH -o dbn-partitioned-none_class_out.%j.txt # Name of stdout output file(%j expands to jobId)
#SBATCH -e dbn-partitioned-none_class_err.%j.txt # Name of stderr output file(%j expands to jobId)
#SBATCH -p defq           # Default Queue
#SBATCH -N 1              # Total number of nodes requested
#SBATCH -n 32             # Total number of cpus requested, 32 will make sure we are getting a whole node
#SBATCH --exclusive       # Another way to make sure we are getting a whole node
#SBATCH --mail-user hasari@gmail.com    # user to send emails to
#SBATCH --mail-type ALL                 # (equivalent to  BEGIN,  END,  FAIL  and REQUEUE)
#----------------------------------------------------
# Notes: 
# - the '\' is a line continuation.
# - srun is unnecessary here since you have only one task in this batch.
# - you don't need a trailing "&". (If you think you do, let me know, you probably don't.)
#----------------------------------------------------
# JOB CODE
#----------------------------------------------------
srun scala -classpath $HOME/dev/scalearn/target/scalearn-1.0.0-SNAPSHOT-jar-with-dependencies.jar \ 
           org.scalearn.deeplearning.mnist.MnistDBNTrainer \
           $STORE/dataset \
           $STORE/output \
           partitioned 0.0 0.005 1 15 false none true false