Dask-jobqueue

Dask-jobqueue

Dask-jobqueue is a Python library which makes it easy to deploy Dask on common job queuing systems typically found in high performance supercomputers, academic research institutions, and other clusters. Dask is a Python library for parallel computing which scales Python code from multi-core local machines to large distributed clusters in the cloud. Since Dask-jobqueue provides interfaces for OAR and Slurm based clusters, it can be used to facilite the passage between OAR and Slurm based resource managers. Source code, issues and pull requests can be found here.

Installing

pip can be used to install dask-jobqueue and its dependencies:

flille:

pip install dask-jobqueue --upgrade # Install everything from last released version

otherwise, conda can be used to install dask-jobqueue from the conda-forge:

flille:

conda install dask-jobqueue -c conda-forge

Basic usage

Here is a Python script example which requests for starting a batch script on a well defined resource (2 core, 24GB, at least 1 GPU, specific cluster - chifflet -, for 1 hour)

from dask_jobqueue import OARCluster as Cluster
from dask.distributed import Client
import os

cluster = Cluster(
queue='default',
# Should be specified if you belongs to more than one GGA
project='<your grant access group>',
# cores per job, required parameter
cores=2,
# memory per job, required parameter
memory='24GB',
# walltime for each worker job
walltime='1:0:0',

job_extra=[
'-t besteffort',
# reserve node from specific cluster
'-p chifflet',
# reserve node with at least 1 GPU
'-p "gpu_count >= 1"'
],

# another way to reserve node with GPU
#resource_spec='gpu=1'
)

client = Client(cluster)

# call your favorite batch script
client.submit(os.system, "./hello-world.sh").result()

client.close()
cluster.close()

The example script can be launched on frontend as follow:

flille:

python3 this-script.py

How is Dask-jobqueue interacting with OAR in practice?

The example above will generate the following job script:

#!/usr/bin/env bash

#OAR -n dask-worker
#OAR -q default
#OAR -l walltime=1:0:0
#OAR -t besteffort
#OAR -p chifflet
#OAR -p "gpu_count >= 1"

/usr/bin/python3 -m distributed.cli.dask_worker tcp://172.16.47.106:39655 --nthreads 1 --nprocs 2 --memory-limit 11.18GiB --name dummy-name --nanny --death-timeout 60 --protocol tcp://

Each of these jobs are sent to the job queue independently. Once the job starts, a dask worker process will start up and connect back to the scheduler running within this process.

Advanced usage

Use a configuration file to specify resources

About the resource request, user's configuration can also be specified in ~/.config/dask/jobqueue.yaml file as follow:

jobqueue:
  oar:
    name: dask-worker
    # Dask worker options
    cores: 2
    memory: '24GB'
    processes: 1
    #interface: null # Network interface to use: eth0 or ib0
    death-timeout: 60 # Number of seconds to wait if a worker can not find a scheduler
    #local-directory: null # Location of fast local storage like /scratch or $TMPDIR
    #extra: []
    # OAR resource manager options
    #shebang: "#!/usr/bin/env bash"
    queue: 'default'
    project: null
    walltime: '1:00:00'
    #env-extra: []
    resource-spec: null
    job-extra: []
    log-directory: null
    # Scheduler options
    scheduler-options: {}

The cluster can be then instantiated with one single line as follow:

cluster = OARCluster()

Cluster parameters

dask-jobqueue parameter	OAR command example	Slurm command example	Description
queue	#OAR -q	#SBATCH -p	Destination queue for each worker job
project	#OAR --project	#SBATCH -A	Accounting group associated with each worker job
cores	#OAR -l core=2	#SBATCH --cpu-per-task=2	Total cores per job
memory		#SBATCH --mem=24GB	Total memory per job
walltime	#OAR -l walltime=hh:mm:ss	#SBATCH -t hh:mm:ss	Walltime for each worker job
name	#OAR -n	#SBATCH -J	Name of worker, always set to the default value dask-worker
resource_spec	#OAR -l host=1/core=2, gpu=1	Not supported	Request resources and specify job placement
job_extra	#OAR -O, -E	#SBATCH -o, -e	Log directory
job_extra	#OAR -p parasilo	#SBATCH -C sirocoo	Property request
job_extra	#OAR -t besteffort	#SBATCH -t besteffort	Besteffort job
job_extra	#OAR -r now	#SBATCH --begin=now	Advance reservation
job_extra	#OAR --checkpoint 150	#SBATCH --checkpoint 150	Checkpoint
job_extra	#OAR -a jobid	#SBATCH --dependency state:jobid	Jobs dependency

Start multiple computations at once using 'scale' parameter

Dask-jobqueue creates a Dask Scheduler in the Python process where the cluster object is instantiated with the defined resource. Several ways exist to interact with the cluster through the Client (e.g. .submit or .map functions). Details about the Client of Dask can be found here.

In Dask, a Worker is a Python object and node serving data and performing computations; Jobs are resources submitted to, and managed by, the job queueing system (e.g. OAR, Slurm, etc..).

In Dask-jobqueue, a single Job may include one or more Workers.

The number of Workers can be set by the processes parameter as shown in the configuration above, if your job can be cut into many processes.

To specify the number of Jobs, you can use the scale command. The number of Jobs can either be specified directly as shown in the example above, or indirectly by the cores or memory request:

# 2 job with 1 worker for each will be launched
cluster.scale(2)
# specify total cores 
cluster.scale(cores=4)
# specify total memory
cluster.scale(memory="48GB")

The cluster generated a traditional job script and submits it number of times as specified to the job queue. You can see the generated job script as follow:

print(cluster.job_script())

Note: All experiment above is tested on Grid5000, OAR based cluster. Plafrim and Cleps are used as Slurm based clusters to run the same experiment, in order to find the common concepts between OAR and Slurm. Since heterogenities are still observed between Pafrim and Cleps today, the "Slurm command example" column of the table above will be updated when Slurm will be fully supported by the Inria's national computing infrastructure.