def get_slurm_dask_client(n_workers, n_cores, n_processes):
cluster = SLURMCluster(cores=n_cores,
processes=n_processes,
memory='80GB',
interface='ib0',
queue='standard',
job_extra=['-e slurm-%j.err', '-o slurm-%j.out',
'--time=72:00:00 --requeue'])
header_lines = cluster.job_header.split('\n')
mem_pos = find_mem_pos(header_lines)
header_lines = header_lines[:mem_pos]+header_lines[mem_pos+1:]
cluster.job_header = '\n'.join(header_lines)
print(cluster.job_script())
# Scale cluster to n_workers
cluster.scale(n_workers)
# Wait for cluster to start
time.sleep(30)
client = Client(cluster)
print(client.scheduler_info())
return client
# Generate synthetic receiver data from true model.
true_d, _, _ = solver.forward(m=true_model.m)
return true_d.data
cluster = SLURMCluster(cores=2,
memory="10GB",
queue="standard",
interface="ib0",
job_extra=['--time=72:00:00 --requeue'])
header_lines = cluster.job_header.split('\n')
mem_pos = find_mem_pos(header_lines)
header_lines = header_lines[:mem_pos] + header_lines[mem_pos + 1:]
cluster.job_header = '\n'.join(header_lines)
print(cluster.job_script())
print("Waiting for workers")
cluster.scale(18)
client = Client(cluster)
# Get parameters.
js = open('parameters.json')
par = json.load(js)
# Load the data
shots = numpy.fromfile('shots.file', dtype=numpy.float32)
shots = numpy.reshape(shots, (6559, 369, 50))
# Set up source/receiver data and geometry.
src_coordinates = numpy.empty((par['nshots'], len(par['shape'])))
