Production-ready data processing made easy and shareable
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🪤 Why Fondant?

Fondant is a data framework that enables collaborative dataset building. It is designed for developing and crafting datasets together, sharing reusable operations and complete data processing trees.

Fondant enables you to initialize datasets, apply various operations on them, and load datasets from other users. It assists in executing operations on managed services, sharing operations with others, and keeping track of your dataset versions. Fondant makes this all possible without moving the source data.

💨 Getting Started

Fondant allows you to easily define workflows comprised of both reusable and custom components. The following example uses the reusable load_from_hf_hub component to load a dataset from the Hugging Face Hub and process it using a custom component that will resize the images resulting in a new dataset.

import pyarrow as pa

from fondant.dataset import Dataset

# initialize a dataset by loading data from the Hugging Face Hub
raw_data = Dataset.create(
    "load_from_hf_hub",
    arguments={
        "dataset_name": "fondant-ai/fondant-cc-25m",
        "n_rows_to_load": 100,
    },
    produces={
        "alt_text": pa.string(),
        "image_url": pa.string(),
        "license_location": pa.string(),
        "license_type": pa.string(),
        "webpage_url": pa.string(),
        "surt_url": pa.string(),
        "top_level_domain": pa.string(),
    },
)
# add an operation to download the images from the urls
images = raw_data.apply(
    "download_images",
    arguments={
        "input_partition_rows": 100,
        "resize_mode": "no",
    },
)
# add an operation to resize the images
dataset = images.apply(
    "resize_images",
    arguments={
        "resize_width": 128,
        "resize_height": 128,
    },
)

Custom use cases require the creation of custom components. Check out our step by step guide to learn more about how to build custom pipelines and components.

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Running your pipeline

Once you have a pipeline you can easily run (and compile) it by using the built-in CLI:

fondant run local pipeline.py

To see all available runners and arguments you can check the fondant CLI help pages

fondant --help

Or for a specific subcommand:

fondant <subcommand> --help

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🪄 How Fondant works

• Dataset: The building blocks, a dataset is a collection of columns. Fondant operates uniquely via datasets. We start with a dataset, we augment it into a new dataset and we end with a dataset. Fondant optimizes the data transfer by storing and loading columns as needed. While also processing based on the available partitions. The aim is to make these datasets sharable and allow users to create their own datasets based on others.
• Operation: A transformation to be applied on a dataset resulting in a new dataset. The operation will load needed columns and produce new/altered columns. A transformation can be anything from loading, filtering, adding a column, writing etc. Fondant also makes operations sharable so you can easily use an operation in your workflow.
• Shareable trees: Datasets are a result of applying operations on other datasets. The full lineage is baked in. This allows for sharing not just the end product but the full history, users can also easily continue based on a dataset or branch off of an existing graph.

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🧩 Key Features

Here's what Fondant brings to the table:

• 🔧 Plug ‘n’ play composable data processing workflows
• 🧩 Library containing off-the-shelf reusable components
• 🐼 A simple Pandas based dataframe interface for creating custom components
• 📊 Built-in lineage, caching, and data explorer
• 🚀 Production-ready, scalable deployment
• ☁️ Integration with runners across different clouds (Vertex on Google Cloud, Sagemaker on AWS, Kubeflow on any k8s cluster)

👉 Check our Component Hub for an overview of all available components

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🪄 Example pipelines

We have created several ready-made example pipelines for you to use as a starting point for exploring Fondant. They are hosted as separate repositories containing a notebook tutorial so you can easily clone them and get started:

📖 RAG tuning pipeline
End-to-end Fondant pipelines to index and evaluate RAG (Retrieval-Augmented Generation) systems.

🛋️ ControlNet Interior Design Pipeline
An end-to-end Fondant pipeline to collect and process data for the fine-tuning of a ControlNet model, focusing on images related to interior design.

🖼️ Filter creative common license images
An end-to-end Fondant pipeline that starts from our Fondant-CC-25M creative commons image dataset and filters and downloads the desired images.

⚒️ Installation

First, run the basic Fondant installation:

pip install fondant

Fondant also includes extra dependencies for specific runners, storage integrations and publishing components to registries. The dependencies for the local runner (docker) is included by default.

For more detailed installation options, check the installation pageon our documentation.

👭 Contributing

We welcome contributions of different kinds:

For a detailed view on the roadmap and day to day development, you can check our github project board.

You can also check out our architecture page to familiarize yourself with the Fondant architecture and repository structure.

Environment setup

We use poetry and pre-commit to enable a smooth developer flow. Run the following commands to set up your development environment:

pip install poetry
poetry install --all-extras
pre-commit install

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