#!/usr/bin/python3

#
# Useful debugging info when useing dask with SLURM jobs
#
#import logging
#logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)

#
# A example of using threads on local host
#
#from dask.distributed import Client, progress
#client = Client(processes=False, threads_per_worker=4,
#                n_workers=1, memory_limit='4GB')

#
# A example of using SLURM jobs on the farm
#
from dask_jobqueue import SLURMCluster
cluster = SLURMCluster(
    queue='shared',
    project="myproj",
    cores=1,
    memory="4 GB",
    python="/usr/bin/python3",
    job_extra=['--account=shared', '--partition=shared']
)
cluster.scale(jobs=4)  # ask for 4 jobs

from dask.distributed import Client
client = Client(cluster)

#
# Print dask client info
#
client

import dask.array as da
x = da.random.random((10000, 10000), chunks=(1000, 1000))
y = da.random.random((10000, 10000), chunks=(1000, 1000))

z = da.arcsin(x).dot(da.arccos(y))

z.compute()
print(z)

#import numpy as np
#zz = np.asarray(z)
#print(zz)