DeepHyper installation requires Python 3.7.

We recommend creating isolated Python environments on your local machine using virtualenv or miniconda, for example:

conda create -n deephyper python=3.7
conda activate deephyper

Some features of DeepHyper are using the MPI library, which you have to install yourself depending on your system.

User installation

From PyPI:

pip install deephyper

From github:

git clone https://github.com/deephyper/deephyper.git
cd deephyper/
pip install -e .


If you want to install Horovod with DeepHyper (MPI is required):

pip install 'deephyper[balsam,hvd]'


From Pypi:

pip install 'deephyper[analytics]'

Then to make DeepHyper accessible in a notebook create a new IPython kernel with (before running the command make sure that your virtual environment is activated if you are using one):

python -m ipykernel install --user --name deephyper --display-name "Python (deephyper)"

Now when you will open a Jupyter notebook the “Python (deephyper)” kernel will be available.

Documentation & Tests installation

If you want to install deephyper with test and documentation packages.

From pypi:

pip install 'deephyper[tests,docs]'

From github:

git clone https://github.com/deephyper/deephyper.git
cd deephyper/
pip install -e '.[tests,docs]'


To build the documentation you just need to be in the deephyper/docs folder and run make html assuming you have make command line utility installed on your computer. Then you can see the build documentation inside the doc/s_build folder just by opening the index.html file with your web browser.

Useful informations

The documentation is made with Sphinx and the following extensions are used :





automatically insert docstrings from modules


inline code documentation


automatically test code snippets in doctest blocks


link between Sphinx documentation of different projects


write “todo” entries that can be shown or hidden on build


checks for documentation coverage


include math, rendered in the browser by MathJax


conditional inclusion of content based on config values


include links to the source code of documented Python objects


create .nojekyll file to publish the document on GitHub pages

Sphinx uses reStructuredText files, click on this link if you want to have an overview of the corresponding syntax and mechanism.


Our documentation try to take part of the inline documentation in the code to auto-generate documentation from it. For that reason we highly recommend you to follow specific rules when writing inline documentation : https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html.