with_dask

#!/usr/bin/env python

import dask

# ONLY RUN THIS CELL ONCE
# NOTE: you may need to change the project number
from dask_jobqueue import PBSCluster
from dask.distributed import Client
proj = YOURPROJECTNUMBERINASTRING

# Setup your PBSCluster
cluster = PBSCluster(
    cores=1, # The number of cores you want
    memory='40GB', # Amount of memory
    processes=1, # How many processes
    queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory='$TMPDIR', # Use your local directory
    resource_spec='select=1:ncpus=1:mem=20GB', # Specify resources
    project=proj, # Input your project ID here
    walltime='02:00:00', # Amount of wall time
    interface='ib0', # Interface to use
)

# Scale up
#cluster.scale(18)
cluster.adapt(minimum=1,maximum=2)

# Change your url to the dask dashboard so you can see it
dask.config.set({'distributed.dashboard.link':'https://jupyterhub.hpc.ucar.edu/stable/user/{USER}/proxy/{port}/status'})

# Setup your client
client = Client(cluster)

client

# ----------------------------------------------------- Continue with whatever you want


# ----------------------------------------------------- Close dask
cluster.close()
client.shutdown()

# ----------------------------------------------------- Dasked
ensemble_mean = xr.open_dataset('/glade/work/cassiacai/ensemble_mean.nc', chunks = {})

# Now do the rest of the code...