Welcome! ​

dmosopt aims to simplify distributed multi-objective surrogate-assisted optimization while staying out of your way.

Overview ​

At its core, dmosopt implements a unified, generic optimization API via the dmosopt.run(dopt_params=...) function. The dopt_params argument takes a dictionary that allows users to define arbitrary multi-objective optimization problems, including the objectives, sampling space, constraints and optimization strategies. At the most basic level, all dmosopt scripts will take the following form:

from dmosopt import dmosopt

dmosopt.run({
    "opt_id": "example_optimization",
    ...  #  configuation options
})

from dmosopt import dmosopt

dmosopt.run({
    "opt_id": "example_optimization",
    ...  #  configuation options
})

The opt_id serves a unique namespace for the resulting output file that captures the best solutions as well as various meta data about the optimization progress.

The output file format is HDF5, supporting high-performance, parallel data access.

dmosopt relies on MPI and more specifically distwq to support scaling and parallelization.

Where to go from here ​