smartalecH

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smartalecH

SLURM param sweeper

Get the code here.

This python library generates SLURM submission scripts which launch multiple jobs to 'sweep' a given set of parameters; that is, a job is run for every possible configuration of the params.

For example, you might have a script which accepts parameters a, b and c, which you call (passing a=1, b=2, c=3) like

smartalecH

# ---------------------------------------------------- #
# BYU supercomputer
# ---------------------------------------------------- #

# Interactive jobs - use salloc
salloc --nodes=1 --ntasks-per-node=1 --time=1:00:00 --mem-per-cpu=2GB # Doesn't support GPUS for now...

# ---------------------------------------------------- #
# GaTech PACE cluster
# ---------------------------------------------------- #

smartalecH

#!/bin/bash

#
# Build GCC with support for offloading to NVIDIA GPUs.
#

set -o nounset -o errexit

# Location of the installed CUDA toolkit
cuda=/usr/local/cuda

smartalecH

import meep as mp
try:
    import meep.adjoint as mpa
except:
    import adjoint as mpa
import numpy as np
from autograd import numpy as npa
from autograd import tensor_jacobian_product
from enum import Enum
from matplotlib import pyplot as plt

smartalecH

import meep as mp
try:
    import meep.adjoint as mpa
except:
    import adjoint as mpa
import numpy as np
from autograd import numpy as npa
from autograd import tensor_jacobian_product
from enum import Enum
from matplotlib import pyplot as plt

smartalecH

import meep as mp
try:
    import meep.adjoint as mpa
except:
    import adjoint as mpa
import numpy as np
from autograd import numpy as npa
from autograd import tensor_jacobian_product
from enum import Enum
from matplotlib import pyplot as plt

smartalecH

import meep as mp
import numpy as np
import matplotlib.pyplot as plt

resolution = 10
r_bank = np.linspace(1.0,1.0+1/resolution,100)
sum_bank = []
for r in r_bank:
    print(r)
    sim = mp.Simulation(

smartalecH

"""                                                                             
Checks that the constraint function with constraint L applied to a circle
with diameter d has a return value based on the convention that a violation
(L > d) is >=0 and a non-violation is ~0. 
"""

from typing import Tuple

import matplotlib