MiniTorch requires Python 3.8 or higher. To check your version of Python, run either:

>>> python --version
>>> python3 --version

We recommend creating a global MiniTorch workspace directory that you will use for all modules.

>>> mkdir workspace; cd workspace

We also highly recommand setting up a virtual environment. The virtual environment lets you install packages that are only used for your assignments and do not impact the rest of the system. We suggest venv or anaconda.

For example, if you choose venv, run the following command:

>>> python -m venv venv
>>> source venv/bin/activate

The first line should be run only once, whereas the second needs to be run whenever you open a new terminal to get started for the class. You can tell if the second line works by checking if your terminal starts with (venv). See for further instructions on how this works.

Each assignment is distributed through a Git repo. We assume the knowledge of git throughout the course. See for a tutorial about using git and GitHub.

You should fork the template of the assignment and then edit yours in your forked repo. Once you have forked the template code, you can clone your own version by running the following command:

>>> git clone {{ASSIGNMENT}}
>>> cd {{ASSIGNMENT}}

The last step is to install packages. There are several packages used throughout these assignments, and you can install them in your virtual enviroment by running:

>>> python -m pip install -r requirements.txt
>>> python -m pip install -r requirements.extra.txt
>>> python -m pip install -Ue .

For anaconda users, you need to run an extra command to install llvmlite:

>>> conda install llvmlite

Make sure that everything is installed by running python and then checking:

>>> import minitorch
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