fondant 0.1.1

Fondant - Sweet data-centric foundation model fine-tuning

Sweet data-centric foundation model fine-tuning
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Fondant helps you create high quality datasets to fine-tune foundation models such as:

• 🎨 Stable Diffusion
• 📄 GPT-like Large Language Models (LLMs)
• 🔎 CLIP
• ✂️ Segment Anything (SAM)
• ➕ And many more

🪤 Why Fondant?

Foundation models simplify inference by solving multiple tasks across modalities with a simple prompt-based interface. But what they've gained in the front, they've lost in the back. These models require enormous amounts of data, moving complexity towards data preparation, and leaving few parties able to train their own models.

We believe that innovation is a group effort, requiring collaboration. While the community has been building and sharing models, everyone is still building their data preparation from scratch. Fondant is the platform where we meet to build and share data preparation workflows.

Fondant offers a framework to build composable data preparation pipelines, with reusable components, optimized to handle massive datasets. Stop building from scratch, and start reusing components to:

• Extend your data with public datasets
• Generate new modalities using captioning, segmentation, translation, image generation, ...
• Distill knowledge from existing foundation models
• Filter out low quality data
• Deduplicate data

And create high quality datasets to fine-tune your own foundation models.

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🪄 Example pipelines

Curious to see what Fondant can do? Have a look at our example pipelines:

Fine-tuning ControlNet

Our example pipeline to generate data for ControlNet fine-tuning allows you to create models that you can control using inpainting, segmentation, and regeneration. All you need to get started is a set of prompts describing the type of images to generate.

For instance, using our ControlNet model fine-tuned on interior design images, allows you to generate the room of your dreams:

Input image	Output image

Want to try out the resulting model yourself, head over to our Hugging Face space!

Fine-tuning Stable Diffusion (:construction:)

Using our example pipeline to fine-tune Stable Diffusion allows you to create models that generate better images within a specific domain. All you need to get started is a small seed dataset of example images.

Eg. generating logos:

Stable Diffusion 1.5	Fine-tuned Stable Diffusion 1.5

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🧩 Reusable components

Fondant comes with a library of reusable components, which can jumpstart your pipeline.

Component	Description
load_from_hf_hub	Load a dataset from the Hugging Face Hub
image_embedding	Create embeddings for images using a model from the HF Hub
embedding_based_laion_retrieval	Retrieve images-text pairs from LAION using embedding similarity
prompt_based_laion_retrieval	Retrieve images-text pairs from LAION using prompt similarity
download_images	Download images from urls
image_resolution_filtering	Filter images based on their resolution
caption images	Generate captions for images using a model from the HF Hub
segment_images	Generate segmentation maps for images using a model from the HF Hub

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⚒️ Installation

Fondant can be installed using pip:

pip install fondant

For the latest development version, you might want to install from source instead:

pip install git+https://github.com/ml6team/fondant.git

🧱 Deploying Fondant

Fondant is flexible and works by leveraging kubeflow pipelines on any kubernetes cluster. All fondant needs is an url pointing to the kubeflow pipeline host and a Object Storage provider (S3, GCS, etc) to store data produced in the pipeline between steps.

We have compiled some references and created some scripts to get you started with setting up the required infrastructure.

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👨‍💻 Usage

Pipeline

Fondant allows you to easily define data pipelines comprised of both reusable and custom components. The following pipeline for instance uses the reusable load_from_hf_hub component to load a dataset from the Hugging Face Hub and process it using a custom component:

from fondant.pipeline import ComponentOp, Pipeline, Client


def build_pipeline():
    pipeline = Pipeline(pipeline_name="example pipeline", base_path="fs://bucket")

    load_from_hub_op = ComponentOp.from_registry(
        name="load_from_hf_hub",
        arguments={"dataset_name": "lambdalabs/pokemon-blip-captions"},
    )
    pipeline.add_op(load_from_hub_op)

    custom_op = ComponentOp(
        component_spec_path="components/custom_component/fondant_component.yaml",
        arguments={
            "min_width": 600,
            "min_height": 600,
        },
    )
    pipeline.add_op(custom_op, dependencies=load_from_hub_op)

    return pipeline
    

if __name__ == "__main__":
    client = Client(host="https://kfp-host.com/")
    pipeline = build_pipeline()
    client.compile_and_run(pipeline=pipeline)

Component

To create a custom component, you first need to describe its contract as a yaml specification. It defines the data consumed and produced by the component and any arguments it takes.

name: Custom component
description: This is a custom component
image: custom_component:latest

consumes:
  images:
    fields:
      data:
        type: binary

produces:
  captions:
    fields:
      data:
        type: utf8

args:
  argument1:
    description: An argument passed to the component at runtime
    type: str
  argument2:
    description: Another argument passed to the component at runtime
    type: str

Once you have your component specification, all you need to do is implement a single .transform method and Fondant will do the rest. You will get the data defined in your specification as a Dask dataframe, which is evaluated lazily.

from fondant.component import TransformComponent

class ExampleComponent(TransformComponent):

    def transform(self, dataframe, *, argument1, argument2):
        """Implement your custom logic in this single method
        
        Args:
            dataframe: A Dask dataframe containing the data
            argumentX: An argument passed to the component
        """

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🚧 Current state and roadmap

Fondant is currently in the alpha stage, offering a minimal viable interface. While you should expect to run into rough edges, the foundations are ready and Fondant should already be able to speed up your data preparation work.

The following topics are on our roadmap

• Local pipeline execution
• Non-linear pipeline DAGs
• LLM-focused example pipelines and reusable components
• Static validation, caching, and partial execution of pipelines
• Data lineage and experiment tracking
• Distributed execution, both on and off cluster
• Support other dataframe libraries such as HF Datasets, Polars, Spark
• Move reusable components into a decentralized component registry
• Create datasets of copy-right free data for fine-tuning
• Create reusable components for bias detection and mitigation

The roadmap and priority are defined based on community feedback. To provide input, you can join our discord or submit an idea in our Github Discussions.

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

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👭 Contributing

We welcome contributions of different kinds:

Issues	If you encounter any issue or bug, please submit them as a Github issue. You can also submit a pull request directly to fix any clear bugs.
Suggestions and feedback	If you have any suggestions or feedback, please reach out via our Discord server or Github Discussions!
Framework code contributions	If you want to help with the development of the Fondant framework, have a look at the issues marked with the good first issue label. If you want to add additional functionality, please submit an issue for it first.
Reusable components	Extending our library of reusable components is a great way to contribute. If you built a component which would be useful for other users, please submit a PR adding them to the components/ directory.
Example pipelines	If you built a pipeline with Fondant which can serve as an example to other users, please submit a PR adding them to the examples/ directory.

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
pre-commit install

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