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*.
This section guides you through the Python setup and package installation procedure using `uv`.
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.
## 1. Install Python (if not installed)
First, check if you have a modern version of Python installed (I recommend 3.10 or newer) by executing the following code in the terminal:
```bash
python --version
```
If it returns 3.10 or newer, no further action is required.
> [!NOTE]
> 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/).
Obtain and execute the installer from the official website: https://www.python.org/downloads/.
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.
## 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.
**1. Install uv**
```bash
pip install uv
```
**2. Create the virtual environment**
```bash
uv venv --python=python3.10
```
**3. Activate the virtual environment**
```bash
source .venv/bin/activate
```
> [!NOTE]
> If you are using Windows, you may have to replace the command above by `source .venv/Scripts/activate`
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.
After activating your virtual environment, you can install Python packages using `uv`. For example:
```bash
uv pip install packaging
```
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 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.
**Start working with the code**
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 `uv`.
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.
