Running mcasopt on Spartan¶

Include instructions for:

Writing Slurm job scripts that use this package.
How mcasopt will launch jobs, what their names will be, monitoring their progress, etc.
How mcasopt will resume where it left off, if terminated.

In adapting the example presented below, you may want to change the following details:

The mapping from parameter vectors to model inputs input_fun.
The initial parameter vector x_0.
The model parameters in input_file.
The target criteria in criteria_file.
The path to the MCAS binary mcas_path.

Example mcasopt script¶

The following Python code demonstrates how to use mcasopt to find parameter values that produce results consistent with a set of target criteria, as specified in the file p+14O-vAmos.crit.

Listing 5 The contents of p_plus_14O_vAmos.py.¶

#!/usr/bin/env python3

import mcasopt


# Define a function that returns the MCAS input for parameter vector ``x``.
def input_fun(mcas_input, x):
    # Adjust the MCAS input according to the values in ``x``.
    mcas_input.model.v0 = [x[0], 0.0, x[0]]
    mcas_input.model.vll = [x[1], 0.0, x[1]]
    return mcas_input


def main():
    # Define the initial parameter vector.
    x_0 = [-35.16, 0.675]

    # Load the experiment configuration.
    config = mcasopt.load_config('p+14O-vAmos.toml')

    # Define an evaluator that runs MCAS simulations using Slurm.
    evaluator = mcasopt.McasEvaluator.run_with_slurm(config, input_fun)

    # Run the solver and obtain the approximate solution.
    result = mcasopt.minimise(evaluator, x_0, config.solver)

    # Print the solution vector.
    print(f'Solution: {result.x}')


if __name__ == "__main__":
    main()

Example experiment configuration¶

The following TOML file defines all of the configuration settings required to run mcasopt.

Note

The job-submission settings are defined in the [slurm] settings table (highlighted below).

Listing 6 The contents of p+14O-vAmos.toml.¶

[experiment]
name = "p+14O-vAmos"
input_file = "p+14O-vAmos.inp"
criteria_file = "p+14O-vAmos.crit"
mcas_path = "./mcas/mcas5.4"
amdc_masses_path = "AMDC-masses.dat"

[solver]
# method = "Nelder-Mead"
method = "BFGS"
# bounds = []
# sleep_duration = 60
cache_file = "p+14O-vAmos.cache"
sleep_duration = 0
log_level = "INFO"
epsilon = 1.5e-8

[slurm]
shebang = "#!/bin/bash"
header.partition = "physical"
header.time = "30-00:00:00"
header.ntasks = "1"
header.job-name = "my-mcas-experiment"
header.output = "my-mcas-experiment.log"
modules = ["iompi"]

Example simulation script¶

Note

The jobs should be submitted to the "physical" partition.

Each MCAS simulation will be run using a job script such as the one shown below:

Listing 7 The contents of example_job.sh.¶

#!/bin/bash
#
#SBATCH --partition=physical
#SBATCH --time=30-00:00:00
#SBATCH --ntasks=1
#SBATCH --job-name=my-mcas-experiment
#SBATCH --output=my-mcas-experiment.log

module purge
module load iompi
module load foss
module load scipy-bundle

date
cd "/home/unimelb.edu.au/rgmoss/work/projects/mcasopt/test_mcas_runner_job"
"/home/unimelb.edu.au/rgmoss/work/projects/mcasopt/mcas/mcas5.4" < p+14O-vAmos.inp > p+14O-vAmos.out 2>&1
date

This job can then be scheduled to run with the sbatch command:

sbatch example_job.sh

Example mcasopt script¶

To start run mcasopt on Spartan, you need to write a shell script that activates the mcasopt virtual environment (see Installation) and runs the p_plus_14O_vAmos.py script:

Listing 8 The contents of run_p+14O-vAmos.sh.¶

#!/bin/bash
module load foss
module load scipy-bundle
source ~/venv/bin/activate
python3 p_plus_14O_vAmos.py

This job can then be scheduled to run with the sbatch command:

sbatch run_p+14O-vAmos.sh