First steps#

The purpose of this guide is to guide through the installation of geomstats and illustrate the possible uses of geomstats. Install is possible via pip3, conda or by cloning the Github repository.


From a terminal (OS X & Linux), you can install geomstats and its requirements with pip3 as follows:

pip3 install geomstats

This method installs the latest version of geomstats that is uploaded on PyPi.


From a terminal (OS X & Linux) or an Anaconda prompt (Windows), you can install geomstats and its requirements with conda as follows:

conda install -c conda-forge geomstats

This method installs the latest version of geomstats that is uploaded on conda-forge. Note that geomstats is only available with Python3.


From a terminal (OS X & Linux), you can install geomstats and its requirements via Git as follows:

git clone
cd geomstats
pip3 install .

This methods installs the latest GitHub version. Developers should install this version, together with the development requirements and the optional requirements to enable the autograd and pytorch backends:

pip3 install .[dev,opt]

If you use the flag -e, geomstats will be installed in editable mode, i.e. local changes are immediately reflected in your installation.


Geomstats can run seamlessly with numpy or pytorch. Note that pytorch requirement is optional, as geomstats can be used with numpy only. By default, the numpy backend is used. The visualizations are only available with this backend.

To get the autograd and pytorch versions compatible with geomstats, install the optional requirements:

pip3 install geomstats[opt]

To install only the requirements for a given backend do:

pip3 install geomstats[<backend_name>]

You can choose your backend by setting the environment variable GEOMSTATS_BACKEND to numpy, autograd or pytorch, and importing the backend module. From the command line:

export GEOMSTATS_BACKEND=<backend_name>

and in the Python3 code:

import geomstats.backend as gs


To use geomstats for learning algorithms on Riemannian manifolds, you need to follow three steps: - instantiate the manifold of interest, - instantiate the learning algorithm of interest, - run the algorithm. The data should be represented by the structure gs.array, which represents numpy arrays or pytorch tensors, depending on the choice of backend.

As an example, the following code snippet illustrates the use of K-means on simulated data on the 5-dimensional hypersphere.

from geomstats.geometry.hypersphere import Hypersphere
from geomstats.learning.online_kmeans import OnlineKMeans

sphere = Hypersphere(dim=5)

data = sphere.random_uniform(n_samples=10)

clustering = OnlineKMeans(sphere, n_clusters=4)
clustering =

The following code snippet shows the use of tangent Principal Component Analysis on simulated data on the space of 3D rotations.

from geomstats.geometry.special_orthogonal import SpecialOrthogonal
from geomstats.learning.pca import TangentPCA

so3 = SpecialOrthogonal(n=3, point_type="vector")

data = so3.random_uniform(n_samples=10)

tpca = TangentPCA(so3, n_components=2)
tpca =
tangent_projected_data = tpca.transform(data)

All geometric computations are performed behind the scenes. The user only needs a high-level understanding of Riemannian geometry. Each algorithm can be used with any of the manifolds and metric implemented in the package.

To see additional examples, visit the page Code Examples.