OpenMDAO is an open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python. It enables you to decompose your models, making them easier to build and maintain, while still solving them in a tightly coupled manner with efficient parallel numerical methods.
The OpenMDAO project is primarily focused on supporting gradient-based optimization with analytic derivatives to allow you to explore large design spaces with hundreds or thousands of design variables, but the framework also has a number of parallel computing features that can work with gradient-free optimization, mixed-integer nonlinear programming, and traditional design space exploration.
If you are using OpenMDAO, please cite us!
Documentation for the latest version can be found here.
Documentation archives for prior versions can be found here.
While the API is relatively stable, OpenMDAO remains in active development. There will be periodic changes to the API. User's are encouraged to pin their version of OpenMDAO to a recent release and update periodically.
OpenMDAO 3.x.y represents the current version and is no longer
considered BETA. It requires Python 3.8 or later and is
To install the latest release, run
pip install --upgrade openmdao.
OpenMDAO 2.10.x is the last version to support Python2.x and will
only receive critical bug fixes going forward.
To install this older release, run
pip install "openmdao<3"
(the quotes around
openmdao<3 are required).
PLEASE NOTE: This repository was previously named OpenMDAO/blue. If you had cloned that repository, please update your repository name and remotes to reflect these changes. You can find instructions here.
The OpenMDAO 1.7.4 code repository is now named OpenMDAO1, and has moved
here. To install it, run:
pip install "openmdao<2"
(the quotes around
openmdao<2 are required).
The legacy OpenMDAO v0.x (versions 0.13.0 and older) of the OpenMDAO-Framework are here.
OpenMDAO includes several optional sets of dependencies:
test for installing the developer tools (e.g., testing, coverage),
docs for building the documentation and
visualization for some extra visualization tools.
all will include all of the optional dependencies.
This is the easiest way to install OpenMDAO. To install only the runtime dependencies:
pip install openmdao
To install all the optional dependencies:
pip install openmdao[all]
Install from a Cloned Repository
This allows you to install OpenMDAO from a local copy of the source code.
git clone http://github.com/OpenMDAO/OpenMDAO pip install OpenMDAO
Install for Development
If you would like to make changes to OpenMDAO it is recommended you
install it in editable mode (i.e., development mode) by adding the
flag when calling
pip, this way any changes you make to the source code will
be included when you import OpenMDAO in Python. You will also want to
install the packages necessary for running OpenMDAO's tests and documentation
generator. You can install everything needed for development by running:
pip install -e OpenMDAO[all]
Users are encouraged to run the unit tests to ensure OpenMDAO is performing correctly. In order to do so, you must install the testing dependencies.
Install OpenMDAO and its testing dependencies:
pip install openmdao[test]
testflo openmdao -n 1
If everything works correctly, you should see a message stating that there were zero failures. If the tests produce failures, you are encouraged to report them as an issue. If so, please make sure you include your system spec, and include the error message.
If tests fail, please include your system information, you can obtain that by running the following commands in python and copying the results produced by the last line.
import platform, sys info = platform.uname() (info.system, info.version), (info.machine, info.processor), sys.version
Which should produce a result similar to:
(('Windows', '10.0.17134'), ('AMD64', 'Intel64 Family 6 Model 94 Stepping 3, GenuineIntel'), '3.6.6 | packaged by conda-forge | (default, Jul 26 2018, 11:48:23) ...')