MPoL Installation

MPoL requires python >= 3.8.

Using pip

Stable versions are hosted on PyPI. You can install the latest version by

$ pip install MPoL

Or if you require a specific version of MPoL (e.g., 0.2.0), you can install via

$ pip install MPoL==0.2.0

We recommend that you install the latest version of MPoL. Though, if you are working on a project across several compute environments, you may wish to ensure that each environment has the same version of MPoL installed.

From source

If you’d like to install the package from source to access the latest development version, download or git clone the MPoL repository and install

$ git clone https://github.com/MPoL-dev/MPoL.git
$ cd MPoL
$ pip install .

If you have trouble installing please raise a github issue with the particulars of your system.

If you’re interested in contributing to the MPoL package, please see the Developer Documentation.

Upgrading

If you installed from PyPI, to upgrade to the latest stable version of MPoL, do

$ pip install --upgrade MPoL

If you installed from source, update the repository

$ cd MPoL
$ git pull
$ pip install .

You can determine your current installed version by

$ python
>>> import mpol
>>> print(mpol.__version__)

Documentation

The documentation served online (here) corresponds to the main branch. This represents the current state of MPoL and is usually the best place to reference MPoL functionality. However, this documentation may be more current than last tagged version or the version you have installed. If you require the new features detailed in the documentation, then we recommend installing the package from source (as above).

In the (foreseeably rare) situation where the latest online documentation significantly diverges from the package version you wish to use (but there are reasons you do not want to build the main branch from source), you can access the documentation for that version by building and viewing the documentation locally. To do so, clone the repository as above, checkout the version tag, and build the docs locally.

Using CUDA acceleration

MPoL uses PyTorch for its Neural Networks as seen in the Optimization Loop tutorial. If you are interested in using PyTorch’s full potential by utilizing a Nvidia graphics card, then the CUDA tool kit will need to be installed (TensorVision is also required). More information on this is available in the GPU Tutorial page. It is worth noting that PyTorch may need to be (re)installed separately using a specific pip for your system.

More information on this can be found on the PyTorch homepage: pytorch.org.