Installation¶

Linux and Mac Users¶

The latest stable version of PyKEEN can be downloaded and installed from PyPI with:

$ pip install pykeen

The latest version of PyKEEN can be installed directly from the source on GitHub with:

$ pip install git+https://github.com/pykeen/pykeen.git

Windows Users¶

We’ve added experimental support for Windows as of !95. However, be warned, it’s much less straightforward to install PyTorch and therefore PyKEEN on Windows.

First, to install PyTorch, you must install Anaconda and follow the instructions on the PyTorch website. For example, if you’re using CUDA version 10.2, use the following command:

$ conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Then, assuming your python and pip command are linked to the same place where conda is installing, you can proceed with the normal installation (or the installation from GitHub as shown above):

$ pip install pytorch

If you’re having trouble with pip or sqlite, you might also have to use conda install pip setuptools wheel sqlite. See our AppVeyor configuration on GitHub for inspiration.

If you know better ways to install on Windows or would like to share some references, we’d really appreciate it.

Development¶

Alternatively, the latest code can be installed in development mode with:

$ git clone https://github.com/pykeen/pykeeen.git pykeen
$ cd pykeen
$ pip install -e .

If you’re interested in making contributions, please see our contributing guide.

To automatically ensure compliance to our style guide, please install pre-commit hooks using the following code block from in the same directory.

$ pip install pre-commit
$ pre-commit install

Extras¶

PyKEEN has several extras for installation that are defined in the [options.extras_require] section of the setup.cfg. They can be included with installation using the bracket notation like in pip install pykeen[docs] or pip install -e .[docs]. Several can be listed, comma-delimited like in pip install pykeen[docs,plotting].

Name	Description
`plotting`	Plotting with `seaborn` and generation of word clouds
`mlflow`	Tracking of results with `mlflow`
`wandb`	Tracking of results with `wandb`
`docs`	Building of the documentation
`templating`	Building of templated documentation, like the README