How can set up a multi nodes job for a spark application through a batch file?Management of available file descriptors within a Hadoop clusterHow to set up the parameter of a spark job according to the available nodes?

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How can set up a multi nodes job for a spark application through a batch file?


Management of available file descriptors within a Hadoop clusterHow to set up the parameter of a spark job according to the available nodes?






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0















I tried the following script but looks like there is some errors. Can someone tell me please if I was missing something in this configuration above?



#!/bin/bash
#SBATCH --nodes=2
#SBATCH --time=00:20:00
#SBATCH --mem=80G
#SBATCH --cpus-per-task=4
#SBATCH --ntasks-per-node=2
#SBATCH --output=sparkjob-%j.out
#SBATCH --mail-type=ALL
#SBATCH --error=/project/6008168/moudi/error6_hours.out
#SBATCH --exclusive

## --------------------------------------
## 0. Preparation
## --------------------------------------

# load the Spark module
module load spark/2.3.0
module load python/3.7.0
source "/home/moudi/ENV3.7.0/bin/activate"

# identify the Spark cluster with the Slurm jobid
export SPARK_IDENT_STRING=$SLURM_JOBID

# prepare directories
export SPARK_WORKER_DIR=$SPARK_WORKER_DIR:-$HOME/.spark/2.3.0/worker
export SPARK_LOG_DIR=$SPARK_LOG_DIR:-$HOME/.spark/2.3.0/logs
export SPARK_LOCAL_DIRS=$SPARK_LOCAL_DIRS:-/tmp/spark

mkdir -p $SPARK_LOG_DIR $SPARK_WORKER_DIR

## --------------------------------------
## 1. Start the Spark cluster master
## --------------------------------------

start-master.sh
sleep 5
MASTER_URL=$(grep -Po '(?=spark://).*' $SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-org.apache.spark.deploy.master*.out)

## --------------------------------------
## 2. Start the Spark cluster workers
## --------------------------------------

# get the resource details from the Slurm job
export SPARK_WORKER_CORES=$SLURM_CPUS_PER_TASK:-1
export SLURM_SPARK_MEM=$(printf "%.0f" $(($SLURM_MEM_PER_NODE *95/100)))
export SPARK_DAEMON_MEMORY=$SPARK_MEM
export SPARK_WORKER_MEMORY=$SPARK_MEM
export SPARK_EXECUTOR_MEMORY=$SPARK_MEM

# start the workers on each node allocated to the job
export SPARK_NO_DAEMONIZE=1
srun --output=$SPARK_LOG_DIR/spark-%j-workers.out --label start-slave.sh $MASTER_URL &

## --------------------------------------
## 3. Submit a task to the Spark cluster
## --------------------------------------

spark-submit --master $MASTER_URL --total-executor-cores $((SLURM_NTASKS * SLURM_CPUS_PER_TASK)) /project/6008168/moudi/mainold.py

## --------------------------------------
## 4. Clean up
## --------------------------------------

# stop the workers
scancel $SLURM_JOBID.0

# stop the master









share|improve this question




























    0















    I tried the following script but looks like there is some errors. Can someone tell me please if I was missing something in this configuration above?



    #!/bin/bash
    #SBATCH --nodes=2
    #SBATCH --time=00:20:00
    #SBATCH --mem=80G
    #SBATCH --cpus-per-task=4
    #SBATCH --ntasks-per-node=2
    #SBATCH --output=sparkjob-%j.out
    #SBATCH --mail-type=ALL
    #SBATCH --error=/project/6008168/moudi/error6_hours.out
    #SBATCH --exclusive

    ## --------------------------------------
    ## 0. Preparation
    ## --------------------------------------

    # load the Spark module
    module load spark/2.3.0
    module load python/3.7.0
    source "/home/moudi/ENV3.7.0/bin/activate"

    # identify the Spark cluster with the Slurm jobid
    export SPARK_IDENT_STRING=$SLURM_JOBID

    # prepare directories
    export SPARK_WORKER_DIR=$SPARK_WORKER_DIR:-$HOME/.spark/2.3.0/worker
    export SPARK_LOG_DIR=$SPARK_LOG_DIR:-$HOME/.spark/2.3.0/logs
    export SPARK_LOCAL_DIRS=$SPARK_LOCAL_DIRS:-/tmp/spark

    mkdir -p $SPARK_LOG_DIR $SPARK_WORKER_DIR

    ## --------------------------------------
    ## 1. Start the Spark cluster master
    ## --------------------------------------

    start-master.sh
    sleep 5
    MASTER_URL=$(grep -Po '(?=spark://).*' $SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-org.apache.spark.deploy.master*.out)

    ## --------------------------------------
    ## 2. Start the Spark cluster workers
    ## --------------------------------------

    # get the resource details from the Slurm job
    export SPARK_WORKER_CORES=$SLURM_CPUS_PER_TASK:-1
    export SLURM_SPARK_MEM=$(printf "%.0f" $(($SLURM_MEM_PER_NODE *95/100)))
    export SPARK_DAEMON_MEMORY=$SPARK_MEM
    export SPARK_WORKER_MEMORY=$SPARK_MEM
    export SPARK_EXECUTOR_MEMORY=$SPARK_MEM

    # start the workers on each node allocated to the job
    export SPARK_NO_DAEMONIZE=1
    srun --output=$SPARK_LOG_DIR/spark-%j-workers.out --label start-slave.sh $MASTER_URL &

    ## --------------------------------------
    ## 3. Submit a task to the Spark cluster
    ## --------------------------------------

    spark-submit --master $MASTER_URL --total-executor-cores $((SLURM_NTASKS * SLURM_CPUS_PER_TASK)) /project/6008168/moudi/mainold.py

    ## --------------------------------------
    ## 4. Clean up
    ## --------------------------------------

    # stop the workers
    scancel $SLURM_JOBID.0

    # stop the master









    share|improve this question
























      0












      0








      0








      I tried the following script but looks like there is some errors. Can someone tell me please if I was missing something in this configuration above?



      #!/bin/bash
      #SBATCH --nodes=2
      #SBATCH --time=00:20:00
      #SBATCH --mem=80G
      #SBATCH --cpus-per-task=4
      #SBATCH --ntasks-per-node=2
      #SBATCH --output=sparkjob-%j.out
      #SBATCH --mail-type=ALL
      #SBATCH --error=/project/6008168/moudi/error6_hours.out
      #SBATCH --exclusive

      ## --------------------------------------
      ## 0. Preparation
      ## --------------------------------------

      # load the Spark module
      module load spark/2.3.0
      module load python/3.7.0
      source "/home/moudi/ENV3.7.0/bin/activate"

      # identify the Spark cluster with the Slurm jobid
      export SPARK_IDENT_STRING=$SLURM_JOBID

      # prepare directories
      export SPARK_WORKER_DIR=$SPARK_WORKER_DIR:-$HOME/.spark/2.3.0/worker
      export SPARK_LOG_DIR=$SPARK_LOG_DIR:-$HOME/.spark/2.3.0/logs
      export SPARK_LOCAL_DIRS=$SPARK_LOCAL_DIRS:-/tmp/spark

      mkdir -p $SPARK_LOG_DIR $SPARK_WORKER_DIR

      ## --------------------------------------
      ## 1. Start the Spark cluster master
      ## --------------------------------------

      start-master.sh
      sleep 5
      MASTER_URL=$(grep -Po '(?=spark://).*' $SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-org.apache.spark.deploy.master*.out)

      ## --------------------------------------
      ## 2. Start the Spark cluster workers
      ## --------------------------------------

      # get the resource details from the Slurm job
      export SPARK_WORKER_CORES=$SLURM_CPUS_PER_TASK:-1
      export SLURM_SPARK_MEM=$(printf "%.0f" $(($SLURM_MEM_PER_NODE *95/100)))
      export SPARK_DAEMON_MEMORY=$SPARK_MEM
      export SPARK_WORKER_MEMORY=$SPARK_MEM
      export SPARK_EXECUTOR_MEMORY=$SPARK_MEM

      # start the workers on each node allocated to the job
      export SPARK_NO_DAEMONIZE=1
      srun --output=$SPARK_LOG_DIR/spark-%j-workers.out --label start-slave.sh $MASTER_URL &

      ## --------------------------------------
      ## 3. Submit a task to the Spark cluster
      ## --------------------------------------

      spark-submit --master $MASTER_URL --total-executor-cores $((SLURM_NTASKS * SLURM_CPUS_PER_TASK)) /project/6008168/moudi/mainold.py

      ## --------------------------------------
      ## 4. Clean up
      ## --------------------------------------

      # stop the workers
      scancel $SLURM_JOBID.0

      # stop the master









      share|improve this question














      I tried the following script but looks like there is some errors. Can someone tell me please if I was missing something in this configuration above?



      #!/bin/bash
      #SBATCH --nodes=2
      #SBATCH --time=00:20:00
      #SBATCH --mem=80G
      #SBATCH --cpus-per-task=4
      #SBATCH --ntasks-per-node=2
      #SBATCH --output=sparkjob-%j.out
      #SBATCH --mail-type=ALL
      #SBATCH --error=/project/6008168/moudi/error6_hours.out
      #SBATCH --exclusive

      ## --------------------------------------
      ## 0. Preparation
      ## --------------------------------------

      # load the Spark module
      module load spark/2.3.0
      module load python/3.7.0
      source "/home/moudi/ENV3.7.0/bin/activate"

      # identify the Spark cluster with the Slurm jobid
      export SPARK_IDENT_STRING=$SLURM_JOBID

      # prepare directories
      export SPARK_WORKER_DIR=$SPARK_WORKER_DIR:-$HOME/.spark/2.3.0/worker
      export SPARK_LOG_DIR=$SPARK_LOG_DIR:-$HOME/.spark/2.3.0/logs
      export SPARK_LOCAL_DIRS=$SPARK_LOCAL_DIRS:-/tmp/spark

      mkdir -p $SPARK_LOG_DIR $SPARK_WORKER_DIR

      ## --------------------------------------
      ## 1. Start the Spark cluster master
      ## --------------------------------------

      start-master.sh
      sleep 5
      MASTER_URL=$(grep -Po '(?=spark://).*' $SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-org.apache.spark.deploy.master*.out)

      ## --------------------------------------
      ## 2. Start the Spark cluster workers
      ## --------------------------------------

      # get the resource details from the Slurm job
      export SPARK_WORKER_CORES=$SLURM_CPUS_PER_TASK:-1
      export SLURM_SPARK_MEM=$(printf "%.0f" $(($SLURM_MEM_PER_NODE *95/100)))
      export SPARK_DAEMON_MEMORY=$SPARK_MEM
      export SPARK_WORKER_MEMORY=$SPARK_MEM
      export SPARK_EXECUTOR_MEMORY=$SPARK_MEM

      # start the workers on each node allocated to the job
      export SPARK_NO_DAEMONIZE=1
      srun --output=$SPARK_LOG_DIR/spark-%j-workers.out --label start-slave.sh $MASTER_URL &

      ## --------------------------------------
      ## 3. Submit a task to the Spark cluster
      ## --------------------------------------

      spark-submit --master $MASTER_URL --total-executor-cores $((SLURM_NTASKS * SLURM_CPUS_PER_TASK)) /project/6008168/moudi/mainold.py

      ## --------------------------------------
      ## 4. Clean up
      ## --------------------------------------

      # stop the workers
      scancel $SLURM_JOBID.0

      # stop the master






      hpc multi-threading mpio slurm






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked May 1 at 14:30









      moudimoudi

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