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| title: README | |
| emoji: 🍫 | |
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| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/ml6team/fondant/main/docs/art/fondant_banner.svg" height="250px"/> | |
| </p> | |
| <p align="center"> | |
| <i>Large-scale data processing made easy and reusable</i> | |
| <br> | |
| <a href="https://fondant.readthedocs.io/en/stable/"><strong>Explore the docs »</strong></a> | |
| <br> | |
| <br> | |
| <a href="https://discord.gg/HnTdWhydGp"><img alt="Discord" src="https://dcbadge.vercel.app/api/server/HnTdWhydGp?style=flat-square"></a> | |
| <a href="https://pypi.org/project/fondant/"><img alt="PyPI version" src="https://img.shields.io/pypi/v/fondant?color=brightgreen&style=flat-square"></a> | |
| <a href="https://fondant.readthedocs.io/en/latest/license/"><img alt="License" src="https://img.shields.io/github/license/ml6team/fondant?style=flat-square&color=brightgreen"></a> | |
| <a href="https://github.com/ml6team/fondant/actions/workflows/pipeline.yaml"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/ml6team/fondant/pipeline.yaml?style=flat-square"></a> | |
| <a href="https://coveralls.io/github/ml6team/fondant?branch=main"><img alt="Coveralls" src="https://img.shields.io/coverallsCoverage/github/ml6team/fondant?style=flat-square"></a> | |
| </p> | |
| --- | |
| 🍫**Fondant is an open-source framework that aims to simplify and speed up large-scale data processing by making | |
| containerized components reusable across pipelines and execution environments and shareable within the community.**\ | |
| It offers: | |
| - 🔧 Plug ‘n’ play composable pipelines for creating datasets for | |
| - AI image generation model fine-tuning (Stable Diffusion, ControlNet) | |
| - Large language model fine-tuning (LLaMA, Falcon) | |
| - Code generation model fine-tuning (StarCoder) | |
| - 🧱 Library of off-the-shelf reusable components for | |
| - Extracting data from public sources such as Common Crawl, LAION, ... | |
| - Filtering on | |
| - Content, e.g. language, visual style, topic, format, aesthetics, etc. | |
| - Context, e.g. copyright license, origin | |
| - Metadata | |
| - Removal of unwanted data such as toxic, NSFW or generated content | |
| - Removal of unwanted data patterns such as societal bias | |
| - Transforming data (resizing, cropping, reformatting, …) | |
| - Tuning the data for model performance (normalization, deduplication, …) | |
| - Enriching data (captioning, metadata generation, synthetics, …) | |
| - Transparency, auditability, compliance | |
| - 📖 🖼️ 🎞️ ♾️ Out of the box multimodal capabilities: text, images, video, etc. | |
| - 🐍 Standardized, Python/Pandas-based way of creating custom components | |
| - 🏭 Production-ready, scalable deployment | |
| - ☁️ Multi-cloud integrations | |
| ## 🪤 Why Fondant? | |
| In the age of Foundation Models, control over your data is key and building pipelines | |
| for large-scale data processing is costly, especially when they require advanced | |
| machine learning-based operations. This need not be the case, however, if processing | |
| components would be reusable and exchangeable and pipelines were easily composable. | |
| Realizing this is the main vision behind Fondant. | |
| <p align="right">(<a href="#chocolate_bar-fondant">back to top</a>)</p> | |