# Getting Started with Devbox for Data Science

## 1. What is Devbox and Why Use It?

* **Devbox** is a command-line tool that allows you to create **isolated, reproducible, and portable development environments** without relying on Docker or virtual machines.
* It is powered by Nix, but without the complexity of Nix language.
* For a **data scientist**, this means:

  * Easy sharing of dependencies (Python, ML libraries, etc.) in a single `devbox.json` file.
  * Consistency across machines: every teammate works with the same versions.
  * Ability to experiment with tools without polluting your global system.



## 2. Installing Devbox

1. Open your terminal (Linux, macOS, or Windows with WSL2).

2. Run the following command (non-root user):

   ```bash
   curl -fsSL https://get.jetify.com/devbox | bash
   ```

3. If Nix is not installed, Devbox will install it automatically.



## 3. Creating Your First Devbox Environment

1. Initialize Devbox in your project directory:

   ```bash
   devbox init
   ```

   This generates a `devbox.json` file.

2. Search for packages:

   ```bash
   devbox search uv
   ```

3. Add **uv** (a fast Python package installer and environment manager) and data science libraries:

   ```bash
   devbox add uv pandas
   ```

4. Start your environment:

   ```bash
   devbox shell
   ```

5. Create and manage a Python environment with `uv`:

   ```bash
   uv venv .venv
   source .venv/bin/activate
   uv pip install numpy scikit-learn
   ```

6. Exit the environment:

   ```bash
   exit
   ```



## 4. Automating with Scripts and Hooks

You can customize workflows in `devbox.json`:

```json
{
  "packages": [
    "uv",
    "pandas"
  ],
  "shell": {
    "init_hook": [
      "uv pip install -r requirements.txt"
    ],
    "scripts": {
      "run-notebook": "uv run jupyter notebook"
    }
  }
}
```

* **init\_hook**: runs commands when entering `devbox shell`.
* **scripts**: custom commands launched with:

  ```bash
  devbox run run-notebook
  ```

Example: automatically install dependencies or start Jupyter Notebook when entering the environment.



## 5. Managing Global Packages

Some tools are useful across all projects (like `git` or `ripgrep`). Install them globally:

```bash
devbox global add ripgrep git
```

To make them available outside of `devbox shell`, add this to your `.bashrc` or `.zshrc`:

```bash
eval "$(devbox global shellenv --init-hook)"
```



## 6. Best Practices for Data Scientists

| Step | Recommendation                                                                    |
| - |  |
| 1    | Always version-control `devbox.json` and `devbox.lock`.                           |
| 2    | Use `uv` to manage Python environments and package installations.                 |
| 3    | Use **init\_hooks** to automate dependency installation with `uv pip`.            |
| 4    | Use **scripts** for tasks like launching Jupyter or running tests.                |
| 5    | Leverage `devbox global` for tools you need everywhere.                           |
| 6    | Explore **Devbox templates** (like Jupyter setups) to kickstart projects quickly. |



## Summary

Using **Devbox** with **uv** provides a modern, fast, and reproducible way to manage Python environments for data science. This workflow combines the reliability of Devbox with the speed of `uv`, ensuring smooth and consistent setups across projects.