💧 Visual Data Preparation (VDP) is an open-source tool to seamlessly integrate Vision AI with the modern data stack

💧 Visual Data Preparation (VDP) is an open-source tool to seamlessly integrate Vision AI with the modern data stack

Visual Data Preparation: open-source visual data ETL

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Visual Data Preparation (VDP) is an open-source visual data ETL tool to streamline the end-to-end visual data processing pipeline:

  • Extract unstructured visual data from pre-built data sources such as cloud/on-prem storage, or IoT devices

  • Transform it into analysable structured data by Vision AI models

  • Load the transformed data into warehouses, applications, or other destinations

VDO Concept


Online demos

An online demo VDP instance has been provisioned, in which you can directly play around the basic features in its Console via https://demo.instill.tech and the API (e.g., https://demo.instill.tech/v1alpha/pipelines).

A number of applications that you can possibly use VDP to quickly achieve are showcased below:

  • Object Detection Demo

Want to showcase your ML/DL models? We offer fully-managed VDP on Instill Cloud. Please sign up the form and we will reach out to you.


  • macOS or Linux - VDP works on macOS or Linux, but does not support Windows yet.

  • Docker and Docker Compose - VDP uses Docker Compose (compose file version: 3.9) to run all services at local. Please install Docker and Docker Compose before using VDP.

Quick start

Execute the following commands to start pre-built images with all the dependencies:

$ git clone https://github.com/instill-ai/vdp.git && cd vdp

# Launch all services
$ make all

🚀 That's it! Once all the services are up with health status, the UI is ready to go at http://localhost:3000!

VDO Console

Jump right in


The image of model-backend (~2GB) and Triton Inference Server (~11GB) can take a while to pull, but this should be an one-time effort at the first setup.

Shut down VDP

To shut down all running services:

$ make down

Guidance philosophy

VDP is built with open heart and we expect VDP to be exposed to more MLOps integrations. It is implemented with microservice and API-first design principle. Instead of building all components from scratch, we've decided to adopt sophisticated open-source tools:

We hope VDP can also enrich the open-source communities in a way to bring more practical use cases in unstructured visual data processing.


📔 Documentation

Check out the documentation & tutorials to learn VDP!

📘 API Reference

The gRPC protocols in protobufs provide the single source of truth for the VDP APIs. The genuine protobuf documentation can be found in our Buf Scheme Registry (BSR).

For the OpenAPI documentation, access http://localhost:3001 after make all, or simply run make doc.

Model Hub

We curate a list of ready-to-use models for VDP. These models are from different sources and have been tested by our team. Want to contribute a new model? Please create an issue, we are happy to test and add it to the list 👐 .

Model Task Sources Framework CPU GPU Notes
MobileNet v2 Image classification GitHub, GitHub-DVC ONNX
YOLOv4 Object detection GitHub-DVC ONNX
YOLOv7 Object detection GitHub-DVC ONNX
Detectron2 Keypoint R-CNN R50-FPN Keypoint detection GitHub-DVC PyTorch

Note: The GitHub-DVC source in the table means importing a model into VDP from a GitHub repository that uses DVC to manage large files.

Community support

For general help using VDP, you can use one of these channels:

  • GitHub - bug reports, feature requests, project discussions and contributions

  • Discord - live discussion with the community and our team

  • Newsletter & Twitter - get the latest updates

If you are interested in hosting service of VDP, we've started signing up users to our private alpha. Get early access and we'll contact you when we're ready.


We love contribution to VDP in any forms:

Note Code in the main branch tracks under-development progress towards the next release and may not work as expected. If you are looking for a stable alpha version, please use latest release.


See the LICENSE file for licensing information.

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Sep 2, 2022