mvlearn can be installed by using pip, GitHub, or through the conda-forge channel into an existing conda environment.

IMPORTANT NOTE: mvlearn has an optional dependency to torch and tqdm, so special instructions must be followed to include these optional dependencies in the installation (if you do not have those packages already) in order to access all the features within mvlearn. More details can be found in Including optional torch dependencies for full functionality.

Installing the released version with pip

Below we assume you have the default Python3 environment already configured on your computer and you intend to install mvlearn inside of it. If you want to create and work with Python virtual environments, please follow instructions on venv and virtual environments.

First, make sure you have the latest version of pip3 (the Python3 package manager) installed. If you do not, refer to the Pip documentation and install pip3 first.

Install the current release of mvlearn with pip3:

$ pip3 install mvlearn

To upgrade to a newer release use the --upgrade flag:

$ pip3 install --upgrade mvlearn

If you do not have permission to install software systemwide, you can install into your user directory using the --user flag:

$ pip3 install --user mvlearn

Alternatively, you can manually download mvlearn from GitHub or PyPI. To install one of these versions, unpack it and run the following from the top-level source directory using the Terminal:

$ pip3 install -e .

This will install mvlearn and the required dependencies (see below).

Including optional torch dependencies for full functionality

Due to the size of the torch dependency, it is an optional installation. Because it, and tqdm, are only used by Deep CCA and SplitAE, they are not included in the basic mvlearn download. If you wish to use functionality associated with these dependencies (Deep CCA and SplitAE), you must install additional dependencies. You can install them independently, or to install everything from PyPI, simply call:

$ pip3 install mvlearn[torch]

To upgrade the package and torch requirements:

$ pip3 install --upgrade mvlearn[torch]

If you have the package locally, from the top level folder call:

$ pip3 install -e .[torch]

Installing the released version with conda-forge

Here, we assume you have created a conda environment with one of the accepted python versions, and you intend to install the full mvlearn release into it (with torch dependencies included). For more information about using conda-forge feedstocks, see the about page, or the mvlearn feedstock.

To install mvlearn with conda, run:

$ conda install -c conda-forge mvlearn

To list all versions of mvlearn available on your platform, use:

$ conda search mvlearn --channel conda-forge

Python package dependencies

mvlearn requires the following packages:

  • graspy >=0.1.1
  • matplotlib >=3.0.0
  • numpy >=1.17.0
  • pandas >=0.25.0
  • scikit-learn >=0.19.1
  • scipy >=1.1.0
  • seaborn >=0.9.0
  • joblib >=0.11
  • python-picard >= 0.4

with optional dependencies

  • torch >=1.1.0
  • tqdm

Currently, mvlearn is supported for Python 3.6, 3.7, and 3.8.

Hardware requirements

The mvlearn package requires only a standard computer with enough RAM to support the in-memory operations and free memory to install required packages.

OS Requirements

This package is supported for Linux and macOS and can also be run on Windows machines.


mvlearn uses the Python pytest testing package. If you don't already have that package installed, follow the directions on the pytest homepage.