Setting up your computer#

This chapter provides instructions for setting up your computer.

Computer hardware#

To execute the notebooks in this collection, it is highly recommended to use a computer with a CUDA compatible NVidia Graphics card with at least 4GB of memory. These notebooks were tests on a Windows 10 Laptop with an NVidia RTX 3050 Mobile GPU.

Setting up Python and Conda environments#

When working with Python, we will make use of many plugins and software libraries which need to be organized. One way of doing this, is by managing Conda environments. A conda environment can be seen as a virtual desktop, or virtual computer, accessible via the terminal. If you install some software into one Conda environment, it may not be accessible from another environment. If a Conda environment breaks, e.g. incompatible software was installed, you can just make a new one and start over.

See also

Install Mini-Forge#

Download and install miniforge. We recommend the distribution miniforge of conda. If you already have an old [Ana]conda installation you haven’t touched for a while, it is recommended to uninstall it and install mini-forge instead.

For ease-of-use, it is recommended to install it for your use only and to add Conda to the PATH variable during installation.

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Setting up a conda environment#

You can create a conda environment using this commands from the terminal. It is highly recommended to install the GPU-version on a computer with an NVidia graphics card. The CPU version should work too, but some deep-learning notebooks may require a powerful GPU.

CPU-only version#

conda env create -f https://raw.githubusercontent.com/haesleinhuepf/xai/main/docs/00_setup/environment-cpu.yml

Activating the environment#

Activate the environment:

conda activate xai

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