Running a scale out Task farm on Shaheen using Dask

Dask is an interesting pythonic framework, primarily targeted to run larger than memory workloads on multiple cores and nodes of a cluster of resources.

In this case, we want to demonstrate how Dask can be used to run a swarm of thin tasks which have load-imbalance on a compute resource which is fewer than the tasks, but can dynamically be increased if the task-farm is running at slower pace than tolerable.

Problem statement

As an example, we have a few steps in a task that needs to be done multiple times. One essential condition that the task must fulfill is that it is independent, that is, it can run exclusively without any dependency on another task of the farm.

Below is a pseudo-code with step of a representative task:

  1. Create a directory

  2. Copy task specific input files from a common source directory

  3. Load software environment with the target application installed

  4. Launch the command and its arguments and Log progress to a log file in Present Working Directory

  5. When finished, copy the output in a common output director

  6. Delete the task specific directory

Consider that you must do this a million times. Its would be useful to have an execution framework which:

  • allows to express the workflow

  • allows a way to map tasks to resources (i.e. number of cores for each task, etc .. )

  • gives monitoring capability to keep track of e.g. progress of task farm, CPU load, memory consumption etc.

As a by-product, we get the benefit of:

  • packing thin tasks (e.g. 1 core task) on a single Shaheen node, maximizing the node utilization

  • introducing a hook to dynamically allocate more worker nodes as SLURM jobs and extend the resources available to the scheduler to run the task farm

  • possibility to resume the task farm as a new set of jobs (depending on if you have added some logic) , therefore allowing checkpoint-restart.



We are leveraging Dask’s execution engine on Shaheen’s compute nodes for this purpose.

We break our workflow into three components:

  • A script to express the steps in a workflow. It also allows expressing the parameters and passes a task and its corresponding parameter to the Dask cluster via the client API.

  • A is an executable bash script which does steps common to each task, e.g. setting the environment. The command line (including the options/arguments) are passes as an argument when invoking the wrapper script

  • Two jobscripts to interact with SLURM

    • sched.slurm is a jobscript which invokes Dask scheduler and invokes the when worker nodes are ready. It also submits the worker.slurm script to SLURM depending on the value of NUM_WORKERS set in the script

    • worker.slurm allocates resources for a worker node which is a Shaheen compute node as a SLURM job, where Dask workers will start and will connect to an existing Dask scheduler . This jobscript also allows tuning the configuration of resources available for each Dask worker, i.e CPUs or memory

User workflow script

The script below

#!/usr/bin/env python from dask.distributed import Client,as_completed import os,time, subprocess as sb import numpy as np #client = Client(scheduler_file='scheduler_%s.json'%jobid,direct_to_workers=True) # start local workers as processes client = Client(scheduler_file='scheduler.json') # start local workers as processes def params(filename='foo.txt'): f = open(filename,'r+') files = # List with stripped line-breaks f.close() return files def func(x,out_dir): fo=open(os.path.join(out_dir,'out.log'),'w+') fe=open(os.path.join(out_dir,'err.log'),'w+') srcdir='/scratch/shaima0d/tickets/39404/user_case' EXE='FreeFem++-nw' # Pre-processing steps -- before launching the application o =['rsync','-r','%s'%(os.path.join(srcdir,'pv_LIR_etau_IVCurve_SF.edp')), 'pv_LIR_etau_IVCurve_SF.edp'] ,cwd=out_dir) o =['rsync','-r','%s'%(os.path.join(srcdir,'BF_RefMeshLIR_100x100x95x100.msh')), 'BF_RefMeshLIR_100x100x95x100.msh'] ,cwd=out_dir) o =['rsync','-r','%s'%(os.path.join(os.environ['PWD'],'')), ''] ,cwd=out_dir) # Launch the application along with its optinos as command line arugument to wrapper script o =['./','%s'%(EXE),'pv_LIR_etau_IVCurve_SF.edp','%s'%(x)], stdout=fo,stderr=fe, shell=False,cwd=out_dir) # Post-processing steps # - Copy the output file and rename it to index according to the task # - Delete the task directory if the processing was successful return True base_dir=os.environ['EXP_NAME'] os.makedirs(base_dir,exist_ok=True) # the logic of parameter setting is encapsulated in params function: x = params() outdirs=list() for i in range(len(x)): sample_dir=os.path.join(base_dir,'%s'%(str(i+1))) os.makedirs(sample_dir,exist_ok=True) outdirs.append(sample_dir) print('outdirs[%d]:: '%(len(outdirs)),outdirs),x,outdirs) for future in as_completed(futures): print('result: ',future.result()) client.close()

Wrapper script

Here you can set the environment to run the target application and invoke it via the arguments passed in from the script where the was invoked.

#!/bin/bash module swap PrgEnv-cray PrgEnv-gnu module load freefem/4.7 module list echo "running $@" $@

In the above example, the software environment is set by loading some installed modules on Shaheen. However, this can be replaced by sourcing a conda environment, if your software was installed in your project directory using conda package manager.

SLURM jobscripts

There are two scripts needed in this workflow

Scheduler script

The scheduler script will, e.g. look as below. Until line 59, its a boiler plate code, which remains pretty much same for any workload. You can control and modify the ports dask_dashboard port and the job’s wall time if you like. Lines 59-64 invoke the script on the head node which will spawn work on the available worker nodes. There is a sleep command to wait until all worker nodes are up and running.

#!/bin/bash -l #SBATCH --ntasks=1 #SBATCH --cpus-per-task=32 #SBATCH --partition=workq #SBATCH --hint=nomultithread #SBATCH --time=01:00:00 module load dask NUM_WORKERS=4 WORKER_JOB_PREFIX=test_workers export EXP_NAME=experiment_${SLURM_JOBID} export LC_ALL=C.UTF-8 export LANG=C.UTF-8 # get tunneling info export XDG_RUNTIME_DIR="" node=$(hostname -s) user=$(whoami) gateway=${EPROXY_LOGIN} submit_host=${SLURM_SUBMIT_HOST} port=8889 dask_dashboard=9000 if [ -f 'scheduler.json' ]; then rm scheduler.json fi srun -u --hint=nomultithread dask-scheduler --scheduler-file=scheduler.json --dashboard-address=${node}:${dask_dashboard} --port=6192 --interface=ipogif0 & echo $node on $gateway pinned to port $port # print tunneling instructions jupyter-log echo -e " To connect to the compute node ${node} on Shaheen running your jupyter notebook server, you need to run following two commands in a terminal 1. Command to create ssh tunnel from you workstation/laptop to cdlX: ssh -L ${dask_dashboard}:localhost:${dask_dashboard} ${user}@${submit_host} 2. Command to create ssh tunnel to run on cdlX: ssh -L ${dask_dashboard}:${node}:${dask_dashboard} ${user}@${gateway} Copy the link provided below by jupyter-server and replace the nid0XXXX with localhost before pasting it in your browser on your workstation/laptop " while [ ! -f 'scheduler.json' ] ; do sleep 2 echo "Waiting for dask scheduler to start" done for ((i=1; i< $((NUM_WORKERS + 1)); i++)) do sbatch -J ${WORKER_JOB_PREFIX} worker.slurm done sleep 180 echo "Starting workload" python -u scancel -n ${WORKER_JOB_PREFIX} exit 0 wait

Worker script

The worker.slurm script is submitted automatically by the sched.slurm script. However, if you feel that the task farm requires more resource, you can submit additional worker jobs manually to extend the compute resources



When all scripts are ready, you can simply submit the sched.slurm job to start the workflow:

As on optional step, for additional workers more than that set in the sched.slurm script as parameter NUM_WORKERS a job can manually be submitted to SLURM queue to extend the compute resource.



For instructions on how to connect to the Dask Dashboard and monitor the progress, please following: