Installation

There is a quick way to get started, but we advise setting up a virtual environment and guide through the process in the section Proper Python setup with virtual environment

Quick setup (not recommended)

If you just want to use the software, the easiest way is to run from your system’s command line in the root folder of the project

pip install --user .

This will install the current version into your local folder of third-party libraries. Note that pytorch_gum_uncertainty_propagation runs with Python version 3.10. Usage in any Python environment on your computer is then possible by

import pytorch_gum_uncertainty_propagation

or, for example, for the custom activation function QuadLU:

from pytorch_gum_uncertainty_propagation.modules import QuadLU

Updating to the newest version

Updates can be installed via the same command

pip install --user .

Proper Python setup with virtual environment (recommended)

The setup described above allows the quick and easy use of pytorch_gum_uncertainty_propagation, but it also has its downsides. When working with Python we should rather always work in so-called virtual environments, in which our project specific dependencies are satisfied without polluting or breaking other projects’ dependencies and to avoid breaking all your dependencies in case of an update of our Python distribution.

Set up a virtual environment

If you are not familiar with Python virtual environments you can get the motivation and an insight into the mechanism in the official docs.

You have the option to set up pytorch_gum_uncertainty_propagation using the Python built-in tool venv. The commands differ slightly between Windows and Mac/Linux .

Create a `venv` Python environment on Windows

In your Windows PowerShell execute the following to set up a virtual environment in a folder of your choice.

PS C:> cd C:\LOCAL\PATH\TO\ENVS
PS C:\LOCAL\PATH\TO\ENVS> py -3 -m venv pytorch_gum_unc_prop_env
PS C:\LOCAL\PATH\TO\ENVS> pytorch_gum_unc_prop_env\Scripts\activate

Proceed to the next step .

Create a `venv` Python environment on Mac & Linux

In your terminal execute the following to set up a virtual environment in a folder of your choice.

$ cd /LOCAL/PATH/TO/ENVS
$ python3 -m venv pytorch_gum_unc_prop_env
$ source pytorch_gum_unc_prop_env/bin/activate

Proceed to the next step .

Install pytorch_gum_uncertainty_propagation via `pip`

Once you activated your virtual environment, you can install pytorch_gum_uncertainty_propagation via:

pip install .

Collecting pytorch_gum_uncertainty_propagation
[...]
Successfully installed pytorch_gum_uncertainty_propagation-[...] [...]

That’s it!

Optional Jupyter Notebook dependencies

If you are familiar with Jupyter Notebooks, you find some examples in the src/examples subfolder of the source code repository. To execute these you need additional dependencies which you get by appending [examples] to pytorch_gum_uncertainty_propagation in the above installation command, e.g.

(pytorch_gum_unc_prop_env) $ python -m piptools sync [examples-requirements.txt](examples-requirements.txt)

Install known to work dependencies’ versions

In case errors arise within pytorch_gum_uncertainty_propagation, the first thing you can try is installing the known to work configuration of dependencies against which we run our test suite. This you can easily achieve with our requirements file. This is done with the following sequence of commands after activating:

(pytorch_gum_unc_prop_env) $ pip install --upgrade pip-tools
Collecting pip-tools
[...]
Successfully installed pip-tools-6.11.0
(pytorch_gum_unc_prop_env) $ python -m piptools sync
Collecting [...]
[...]
Successfully installed [...]
(pytorch_gum_unc_prop_env) $

Optional dependencies

For the different stages of using or developing pytorch_gum_uncertainty_propagation we have provided several requirements files with pinned versions. You can append any of the following file names to the above piptools sync command depending on your task, e.g. $ python -m piptools sync all-requirements.txt. Below you find a description of the files purposes, despite their self-explanatory names:

all-requirements.txt: if you want to cover all cases and simply install all optional dependencies in a known to work configuration
examples-requirements.txt: for experimenting with the provided examples
dev-requirements.txt: for starting to work on the code. Please contact us, if you have any questions.
docs-requirements.txt: if you want to build the documentation locally