Dask-jobqueue should be configured for your cluster so that it knows how many
resources to request of each job and how to break up those resources. You can
specify configuration either with keyword arguments when creating a
object, or with a configuration file.
You can pass keywords to the Cluster objects to define how Dask-jobqueue should define a single job:
cluster = PBSCluster( # Dask-worker specific keywords cores=24, # Number of cores per job memory='100GB', # Amount of memory per job shebang='#!/usr/bin/env zsh', # Interpreter for your batch script (default is bash) processes=6, # Number of Python processes to cut up each job local_directory='$TMPDIR', # Location to put temporary data if necessary # Job scheduler specific keywords resource_spec='select=1:ncpus=24:mem=100GB', queue='regular', project='my-project', walltime='02:00:00', )
Note that the
memory keywords above correspond not to your
full desired deployment, but rather to the size of a single job which should
be no larger than the size of a single machine in your cluster.
Separately you will specify how many jobs to deploy using the scale method. You can either specify the number of workers, or the total number of cores or memory that you want.
cluster.scale(jobs=2) # launch 2 workers, each of which starts 6 worker processes cluster.scale(cores=48) # Or specify cores or memory directly cluster.scale(memory="200 GB") # Or specify cores or memory directly
These all accomplish the same thing. You can chose whichever makes the most sense to you.
Specifying all parameters to the Cluster constructor every time can be error prone, especially when sharing this workflow with new users. Instead, we recommend using a configuration file like the following:
# jobqueue.yaml file jobqueue: pbs: cores: 24 memory: 100GB processes: 6 shebang: "#!/usr/bin/env zsh" interface: ib0 local-directory: $TMPDIR resource-spec: "select=1:ncpus=24:mem=100GB" queue: regular project: my-project walltime: 00:30:00
See Configuration Examples for real-world examples.
If you place this in your
~/.config/dask/ directory then Dask-jobqueue will
use these values by default. You can then construct a cluster object without
keyword arguments and these parameters will be used by default.
cluster = PBSCluster()
You can still override configuration values with keyword arguments
cluster = PBSCluster(processes=12)
If you have imported
dask_jobqueue then a blank
jobqueue.yaml will be
added automatically to
~/.config/dask/jobqueue.yaml. You should use the
section of that configuration file that corresponds to your job scheduler.
Above we used PBS, but other job schedulers operate the same way. You should
be able to share these with colleagues. If you can convince your IT staff
you can also place such a file in
/etc/dask/ and it will affect all people
on the cluster automatically.
For more information about configuring Dask, see the Dask configuration documentation