Page History

...

from dask.distributed import Client,Scheduler
from dask_jobqueue import SLURMCluster
cluster = SLURMCluster(cores=4,memory="31GB47GB")
client = Client(cluster)
cluster.scale(cores=16)

Wait until the "client" command gives the following information:

...

Client

• Scheduler: tcp://10.0.96.6:44771
• Dashboard: http://10.0.96.6:8787/status

...

Cluster

• Workers: 16
• Cores: 16
• Memory: 115.52 GiB

Image Added

You can monitor the Dask cluster activities by following the instructions for the Dask-JupyterLab extension.

...

For further tuning on job performance and memory utilisation, please refer to Using dask and xarray (superseded).