Launch Label Studio in Hugging Face Spaces

Overview

Label Studio can be deployed on Hugging Face Spaces, a community platform for hosting and sharing machine learning applications. Launch a personal demonstration instance of Label Studio with just one click, and begin collaborating on data annotation projects.

The Label Studio space includes all the features of Label Studio, including a machine learning API that can be connected to a wide variety of models. Automate data annotation with machine learning predictions, and fine-tune models through active learning.

With some additional configuration, a Hugging Face Spaces instance of Label Studio can securely attach to cloud storage to store project data—including annotations and user settings—in a remotely attached database.

Data can also be persisted directly to Hugging Face Spaces, giving you a single location for hosting the open source edition of Label Studio and project information.

Benefits

• Fast Installation: Hosting Label Studio on your own infrastructure is unnecessary. The application can be replicated and launched in your Hugging Face account with just one click.
• Easy Collaboration: Share an instance of the Label Studio space with colleagues to collaborate on annotation projects.
• Adaptable: Label Studio spaces can be adapted to meet the exact needs of your project through its integrations with external databases, cloud storage, and machine learning models.

How Label Studio Connects with Hugging Face

Label Studio integrates with Hugging Face in three main ways: you can host the Label Studio app directly in a Hugging Face Space, use Hugging Face models as ML backends for pre-labeling or training, or import datasets from Hugging Face into your labeling projects. Running Label Studio in a Space gives you a quick, hosted environment for annotation, while connecting a Hugging Face model lets you generate predictions or fine-tune interactively inside the labeling UI. For a complete walkthrough, see Introduction to Label Studio in Hugging Face Spaces and Hugging Face ML backend examples.