# Notebooks Tutorial

Welcome to the Notebooks Tutorial Library of Data Juicer Hub! [This branch](https://github.com/datajuicer/data-juicer-hub/tree/notebook) contains a series of Jupyter notebooks to help you get started with Data Juicer quickly.

## 🚀 Quick Start

In addition to the methods below, you can also try [JupyterLab Playground with Tutorials](http://8.138.149.181/).

### Method 1: Github Codespace

1. **Launch Codespace**
   - Click the `Code` button in this repository and select the `Codespaces` tab
   - Click `Create codespace on notebook` (the `+` icon) to start the environment
   - Wait a moment and you'll see the VSCode Web interface

2. **Select and Run Notebooks**
   - Find the `notebooks` folder in the left file directory
   - Click on the notebook file you're interested in
   - Click the kernel selector in the top right corner and choose the **`data-juicer-hub`** environment (located in the `.venv` directory)
   - Start running!

### Method 2: Google Colab

Click the links below to run the tutorials online in Google Colab:

| Chapter | Title | Colab Link |
|---------|-------|-----------|
| 01 | Getting Started | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/01_Getting_Started.ipynb) |
| 02 | Building Recipes | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/02_Building_Recipes.ipynb) |
| 03 | Data Formats and Loading | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/03_Data_Formats_and_Loading.ipynb) |
| 04 | DJ Dataset API | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/04_DJ_Dataset_API.ipynb) |
| 05 | Operators Usage | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/05_Operators_Usage.ipynb) |
| 06 | Analysis and Visualization | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/06_Analysis_and_Visualization.ipynb) |
| 07 | Distributed Processing with Ray | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/07_Distributed_Processing_with_Ray.ipynb) |
| 08 | Preprocessing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/08_Preprocessing.ipynb) |
| 09 | Multimodal Data Processing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/09_Multimodal_Data_Processing.ipynb) |
| 10 | Advanced Dataset Configuration | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/datajuicer/data-juicer-hub/blob/notebook/notebooks/10_Advanced_Dataset_Configuration.ipynb) |

# 📝 Tutorial Content Overview

- [**01_Getting_Started**](./notebooks/01_Getting_Started.ipynb) - Data Juicer core concepts and quick start guide
- [**02_Building_Recipes**](./notebooks/02_Building_Recipes.ipynb) - Design and construction of data processing recipes
- [**03_Data_Formats_and_Loading**](./notebooks/03_Data_Formats_and_Loading.ipynb) - Comprehensive guide to data formats and loading methods
- [**04_DJ_Dataset_API**](./notebooks/04_DJ_Dataset_API.ipynb) - Complete Dataset API guide
- [**05_Operators_Usage**](./notebooks/05_Operators_Usage.ipynb) - Detailed explanation of data processing operators (both YAML and Python modes)
- [**06_Analysis_and_Visualization**](./notebooks/06_Analysis_and_Visualization.ipynb) - Data analysis and visualization tools
- [**07_Distributed_Processing_with_Ray**](./notebooks/07_Distributed_Processing_with_Ray.ipynb) - Ray distributed processing framework integration

- [**08_Preprocessing**](./notebooks/08_Preprocessing.ipynb) - Data preprocessing scripts
- [**09_Multimodal_Data_Processing**](./notebooks/09_Multimodal_Data_Processing.ipynb) - Multimodal data processing capabilities
- [**10_Advanced_Dataset_Configuration**](./notebooks/10_Advanced_Dataset_Configuration.ipynb) - Advanced Dataset configuration options

## 💡 Recommended Learning Paths

**Quick Start**
> 01 → 02 → 05 → 06 → 07
> 
> For: Learners who want to quickly understand and use Data Juicer's core capabilities

**Data Integration**
> 03 → 08, 09, 10
> 
> For: Developers who already understand Data Juicer's core capabilities and need to integrate their own data into processing pipelines

**Programming and Customization**
> 01 → 03 → 04 → 05 → 07
> 
> For: Engineers who want to flexibly customize data processing pipelines with code

## 📚 More Resources

- [Data Juicer Official Documentation](https://datajuicer.github.io/data-juicer/en/main/)
- [Data Juicer Hub](https://github.com/datajuicer/data-juicer-hub)

Happy Learning! 🎉