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Creating containerized components#

Fondant makes it easy to build dataset collaborative leveraging reusable components. Fondant provides a lot of components out of the box, but you can also define your own containerized components.

Containerized components are useful when you want to share the components within your organization or community. If you don't need your component to be shareable, we recommend starting with a simpler lightweight components instead.

To make sure containerized components are reusable, they should implement a single logical data processing step (like captioning images or removing Personal Identifiable Information [PII] from text). If a component grows too large, consider splitting it into multiple separate components each tackling one logical part.

To implement a containerized component, a couple of files need to be defined:

Fondant component specification#

Each containerized Fondant component is defined by a specification which describes its interface. This specification is represented by a single fondant_component.yaml file. See the component specification page for info on how to write the specification for your component. script#

The component script should be implemented in a script in a folder called src. Refer to the script section for more info on how to implement the script.

Note that the script can be split up into several Python scripts in case it would become prohibitively long. See the prompt based LAION retrieval component as an example: the CLIP client itself is defined in a separate script called clip_client, which is then imported in the script.


The Dockerfile defines how to build the component into a Docker image. An example Dockerfile is defined below.

FROM --platform=linux/amd64 python:3.10-slim

# install requirements
COPY requirements.txt ./
RUN pip3 install --no-cache-dir -r requirements.txt

# Set the working directory to the component folder
WORKDIR /component/src

# Copy over src-files and spec of the component
COPY src/ .

ENTRYPOINT ["fondant", "execute", "main"]


A requirements.txt file lists the Python dependencies of the component. Note that any Fondant component will always have Fondant[component] as the minimum requirement. It's important to also pin the version of each dependency to make sure the component remains working as expected. Below is an example of a component that relies on several Python libraries such as Pillow, PyTorch and Transformers.


Refer to this section to find out how to build and publish your components to use them in your own dataset workflows.