I have been a long-time user of [Conda](https://anaconda.org/anaconda/conda) and [pip](https://pypi.org/project/pip/), but recently, the [uv](https://github.com/astral-sh/uv) package has gained significant traction as it provides a faster and more efficient way to install packages and resolve dependencies.
I recommend starting with *Option 1: Using uv* as it is the more modern approach in 2025. If you encounter problems with *Option 1*, consider *Option 2: Using Conda*.
In this tutorial, I am using a computer running macOS, but this workflow is similar for Linux machines and may work for other operating systems as well.
This section guides you through the Python setup and package installation procedure using `uv` via its `uv pip` interface. The `uv pip` interface may feel more familiar to most Python users who have used pip before than the native `uv` commands.
> There are alternative ways to install Python and use `uv`. For example, you can install Python directly via `uv` and use `uv add` instead of `uv pip install` for even faster package management.
> If you prefer the native `uv` commands, refer to the [./native-uv.md tutorial](./native-uv.md). I also recommend checking the official [`uv` documentation](https://docs.astral.sh/uv/).
> While `uv add` offers additional speed advantages, I think that `uv pip` is slightly more user-friendly, making it a good starting point for beginners. However, if you're new to Python package management, the native `uv` interface is also a great opportunity to learn it from the start. It's also how I use `uv` now, but I realize it the barrier to entry is a bit higher if you are coming from `pip` and `conda`.
If you haven't manually installed Python on your system before, I highly recommend doing so. This helps prevent potential conflicts with your operating system's built-in Python installation, which could lead to issues.
However, even if you have installed Python on your system before, check if you have a modern version of Python installed (I recommend 3.10 or newer) by executing the following code in the terminal:
> I recommend installing a Python version that is at least 2 versions older than the most recent release to ensure PyTorch compatibility. For example, if the most recent version is Python 3.13, I recommend installing version 3.10 or 3.11.
Otherwise, if Python is not installed or is an older version, you can install it for your operating system as described below.
Download and run the installer from the official website: [https://www.python.org/downloads/](https://www.python.org/downloads/).
## 2. Create a virtual environment
I highly recommend installing Python packages in a separate virtual environment to avoid modifying system-wide packages that your OS may depend on. To create a virtual environment in the current folder, follow the three steps below.
Note that you need to activate the virtual environment each time you start a new terminal session. For example, if you restart your terminal or computer and want to continue working on the project the next day, simply run `source .venv/bin/activate` in the project folder to reactivate your virtual environment.
To install all required packages from a `requirements.txt` file (such as the one located at the top level of this GitHub repository) run the following command, assuming the file is in the same directory as your terminal session:
```bash
uv pip install -U -r requirements.txt
```
Alternatively, install the latest dependencies directly from the repository:
> If you have problems with the following commands above due to certain dependencies (for example, if you are using Windows), you can always fall back to using regular pip:
If you encounter any issues with specific packages, try reinstalling them using:
```bash
uv pip install packagename
```
(Here, `packagename` is a placeholder name that needs to be replaced with the package name you are having problems with.)
If problems persist, consider [opening a discussion](https://github.com/rasbt/LLMs-from-scratch/discussions) on GitHub or working through the *Option 2: Using Conda* section below.
Once everything is set up, you can start working with the code files. For instance, launch [JupyterLab](https://jupyterlab.readthedocs.io/en/latest/) by running:
This section guides you through the Python setup and package installation procedure using [`conda`](https://www.google.com/search?client=safari&rls=en&q=conda&ie=UTF-8&oe=UTF-8) via [miniforge](https://github.com/conda-forge/miniforge).
In this tutorial, I am using a computer running macOS, but this workflow is similar for Linux machines and may work for other operating systems as well.
> Many scientific computing libraries do not immediately support the newest version of Python. Therefore, when installing PyTorch, it's advisable to use a version of Python that is one or two releases older. For instance, if the latest version of Python is 3.13, using Python 3.10 or 3.11 is recommended.
If you want to style your terminal similar to mine so that you can see which virtual environment is active, check out the [Oh My Zsh](https://github.com/ohmyzsh/ohmyzsh) project.
To install new Python libraries, you can now use the `conda` package installer. For example, you can install [JupyterLab](https://jupyter.org/install) and [watermark](https://github.com/rasbt/watermark) as follows:
However, since PyTorch is a comprehensive library featuring CPU- and GPU-compatible codes, the installation may require additional settings and explanation (see the *A.1.3 Installing PyTorch in the book for more information*).
It's also highly recommended to consult the installation guide menu on the official PyTorch website at [https://pytorch.org](https://pytorch.org).
## 5. Installing Python packages and libraries used in this book
Please refer to the [Installing Python packages and libraries used in this book](../02_installing-python-libraries/README.md) document for instructions on how to install the required libraries.
