Dragonfly is an open source python library for scalable Bayesian optimisation.

Bayesian optimisation is used for optimising black-box functions whose evaluations are usually expensive. Beyond vanilla optimisation techniques, Dragonfly provides an array of tools to scale up Bayesian optimisation to expensive large scale problems. These include features/functionality that are especially suited for high dimensional optimisation (optimising for a large number of variables), parallel evaluations in synchronous or asynchronous settings (conducting multiple evaluations in parallel), multi-fidelity optimisation (using cheap approximations to speed up the optimisation process), and multi-objective optimisation (optimising multiple functions simultaneously).

Dragonfly is compatible with Python2 (>= 2.7) and Python3 (>= 3.5) and has been tested on Linux, macOS, and Windows platforms.


for detailed instructions on installing Dragonfly and its dependencies.

Quick Installation:
If you have done this kind of thing before, you should be able to install
Dragonfly via pip.

$ sudo apt-get install python-dev python3-dev gfortran # On Ubuntu/Debian
$ pip install numpy
$ pip install dragonfly-opt -v

Testing the Installation:
You can import Dragonfly in python to test if it was installed properly.
If you have installed via source, make sure that you move to a different directory
to avoid naming conflicts.

$ python
>>> from dragonfly import minimise_function
>>> # The first argument below is the function, the second is the domain, and the third is the budget.
>>> min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 10);  
>>> min_val, min_pt
(-0.32122746026750953, array([-0.7129672]))

Due to stochasticity in the algorithms, the above values for min_val, min_pt may be
different. If you run it for longer (e.g.
min_val, min_pt, history = minimise_function(lambda x: x ** 4 - x**2 + 0.1 * x, [[-10, 10]], 100)),
you should get more consistent values for the minimum.

If the installation fails or if there are warning messages, see detailed instructions


Quick Start

Dragonfly can be
used directly in the command line by calling
or be imported in python code via the maximise_function function in the main library.
To help get started, we have provided some examples in the
examples directory.
See our readthedocs getting started pages
(command line,
for examples and use cases.

Command line:
Below is an example usage in the command line.

$ cd examples
$ dragonfly-script.py --config synthetic/branin/config.json --options options_files/options_example.txt

In Python code:
The main APIs for Dragonfly are defined in
For their definitions and arguments, see
dragonfly/apis/opt.py and
You can import the main API in python code via,

from dragonfly import minimise_function, maximise_function
func = lambda x: x ** 4 - x**2 + 0.1 * x
domain = [[-10, 10]]
max_capital = 100
min_val, min_pt, history = minimise_function(func, domain, max_capital)
print(min_val, min_pt)
max_val, max_pt, history = maximise_function(lambda x: -func(x), domain, max_capital)
print(max_val, max_pt)

Here, func is the function to be maximised,
domain is the domain over which func is to be optimised,
and max_capital is the capital available for optimisation.
for more detailed examples.

For a comprehensive list of uses cases, including multi-objective optimisation,
multi-fidelity optimisation, neural architecture search, and other optimisation
methods (besides Bayesian optimisation), see our readthe docs pages
(command line,



Kirthevasan Kandasamy: github,
Karun Raju Vysyaraju: github,
Willie Neiswanger: github,
Biswajit Paria: github,
Chris Collins: github,


Research and development of the methods in this package were funded by
DOE grant DESC0011114, NSF grant IIS1563887, the DARPA D3M program, and AFRL.


If you use any part of this code in your work, please cite
this manuscript.

    title={{Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian
            Optimisation with Dragonfly}},
    author={Kandasamy, Kirthevasan and Vysyaraju, Karun Raju and Neiswanger,
            Willie and Paria, Biswajit and Collins, Christopher R. and Schneider, Jeff and
            Poczos, Barnabas and Xing, Eric P},
    journal={arXiv preprint arXiv:1903.06694},