Python Setup Recommendations

February 27, 2026 · View on GitHub

The code in this book is largely self-contained, and I have made an effort to minimize external dependencies. However, to keep the book accessible, readable, and well under 2000 pages, a few Python packages are necessary.

This section introduces two beginner-friendly methods for installing the required packages so you can run the code examples.

There are, of course, many other ways to install and manage Python packages. If you are an experienced Python user and already have your own setup or preferences, feel free to skip this section.

If neither of the two options below works for you, please do not hesitate to reach out, for example, by opening a Discussion.

Option 1: Using `pip` (built-in, works everywhere)

If you are using a recent version of Python already, you can install packages using the built-in pip installer.

I used Python 3.12 for this book. However, older versions like 3.11 and 3.10 will also work fine. You can check your Python version by running:

python --version

If you are using Python 3.9 or older, consider installing the latest from python.org or using a tool like pyenv to manage versions. However, if you are installing a new Python version, please make sure that it is supported by PyTorch by checking the recommendation on the official PyTorch website. PyTorch typically lags a few months behind the latest Python release, so newly released Python versions are not supported or recommended immediately.

To install new packages, as needed, (for example, PyTorch and Jupyter Lab), run:

pip install torch jupyterlab

Alternatively, you can install all required Python package used in this book all once via the requirements.txt file:

pip install -r https://raw.githubusercontent.com/rasbt/reasoning-from-scratch/refs/heads/main/requirements.txt

Option 2: Use `uv` (faster and widely recommended)

While pip remains the classic and official way to install Python packages, uv is a modern and widely recommended Python package manager that automatically:

Creates and manages a virtual environment
Installs packages quickly
Keeps a lockfile for reproducible installs
Supports pip-like commands

Installing `uv` and Python packages

To install uv, you can use the commands below (also see the official Installation page for the latest recommendations).

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Once installed, you can install new Python packages similar to how you would do it via pip as described in the previous section, except that you replace pip with uv pip. For example

uv pip install torch jupyterlab

However, if you are using uv, which I recommend and use myself, it's even better to use the native uv syntax instead of uv pip, as described below.

Recommended `uv` workflow

Instead of using uv pip, I recommend and use the native uv worklow.

First, clone the GitHub repository to your local machine:

git clone https://github.com/rasbt/reasoning-from-scratch.git

Next, navigate into this folder, e.g., on Linux and MacOS:

cd reasoning-from-scratch

Then, since this folder contains a pyproject.toml file, you are already good to go: uv will automatically create a (by default invisible) virtual environment folder (.venv) for this reasoning-from-scratch project into which it installs all the dependencies the first time you run a script or open Jupyter Lab.

You will probably not need it but in general, you can install additional packages, which are not already part of the requirements listed in pyproject.toml, via uv add:

uv add llms_from_scratch

The above command will then add the package to the virtual environment and pyproject.toml file.

Running code via `uv`

This section describes the uv commands to run Jupyter Lab and Python scripts.

To open Jupyter Lab, execute:

uv run jupyter lab

Python scripts can be run via:

uv run python script.py

Advanced usage: This section describes a simple way to use uv that looks familiar to pip users. If you are interested in more advanced usage, please see this document for more explicit instructions on managing virtual environments in uv. If you are a macOS or Linux user and prefer the native uv commands, please refer to this tutorial. I also recommend checking the official uv documentation for additional information.

JupyterLab tips

If you are viewing the notebook code in JupyterLab rather than VSCode, note that JupyterLab (in its default setting) has had scrolling bugs in recent versions. My recommendation is to go to Settings -> Settings Editor and change the "Windowing mode" to "none" (as illustrated below), which seems to address the issue.

Jupyter Glitch 1