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