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Installation

ropt is distributed on PyPI and can be installed with any standard Python package manager. It requires Python 3.11 or newer.

Install the core package

Using pip:

pip install ropt

The core install includes the SciPy-based optimizer and sampler backends, which are sufficient for most basic optimization tasks.

Optional extras

ropt exposes a few optional dependency groups that enable additional functionality:

Extra Pulls in Enables
pandas pandas Exporting Results to data frames via results_to_dataframe.
hpc pysqa, cloudpickle Running evaluations on HPC clusters via HPCServer.
external cloudpickle Running evaluations in an external Python process via the external backend.

Install with:

pip install "ropt[pandas]"
pip install "ropt[pandas,hpc,external]"

Plugin packages

Additional optimization backends are provided as standalone packages that register themselves through Python entry points. Once installed they become available to ropt automatically:

Package Adds
ropt-dakota Algorithms from the Dakota toolkit.
ropt-nomad The MADS algorithm via NOMAD.
ropt-pymoo Algorithms from pymoo.

Install any of them alongside ropt:

pip install ropt ropt-pymoo

After installation, select a method from the plugin by setting the backend.method field in your configuration (see Configuration) to a "plugin/method" string such as "pymoo/nelder-mead".

Verifying the installation

A quick sanity check:

# Print the current version:
from ropt.version import __version__
print(__version__)

# Verify the SciPy backend is available:
from ropt.workflow import find_backend_plugin
print(find_backend_plugin("slsqp"))  # should print "scipy"

If scipy is printed, the default backend plugin is working. Any additional plugin packages you installed can be verified by checking their methods in the same way.

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