Label Studio Docker

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Playground

Playground

Documentation

Docker

docker run -it --name label-studio -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio

You can find all the generated assets, including SQLite3 database storage label_studio.sqlite3 and uploaded files, in the ./mydata directory.

http://localhost:8080/

Docker Compose

  • Label Studio

  • Nginx - proxy web server used to load various static data, including uploaded audio, images, etc.

  • PostgreSQL - production-ready database that replaces less performant SQLite3.

To start using the app from http://localhost run this command:

docker-compose up

Install locally with pip

# Requires Python >=3.7 <=3.9
pip install label-studio
# Start the server at http://localhost:8080
label-studio

Install locally with Anaconda

conda create --name label-studio
conda activate label-studio
pip install label-studio

Import Data

Cloud storage setup

Amazon S3

  1. Open Label Studio in your web browser.

  2. For a specific project, open Settings > Cloud Storage.

  3. Click Add Source Storage.

  4. In the dialog box that appears, select Amazon S3 as the storage type.

  5. In the Storage Title field, type a name for the storage to appear in the Label Studio UI.

  6. Specify the name of the S3 bucket, and if relevant, the bucket prefix to specify an internal folder or container.

  7. Adjust the remaining parameters:

    • Bucket Name, S3 Endpoint, Access Key ID, Secret Access Key

  8. Click Add Storage.

Screenshots

Gif of Label Studio annotating different types of data

https://labelstud.io/images/ls-modules-scheme.png

https://labelstud.io/_astro/images-tab.64279c16_ZaBSvC.avif

https://labelstud.io/_astro/video-tab.0ad16d1f_ZIpzuy.avif

Label Studio ML backend

Configs and boilerplates for Label Studio’s Machine Learning backend

How it works

  1. Get your model code

  2. Wrap it with the Label Studio SDK

  3. Create a running server script

  4. Launch the script

  5. Connect Label Studio to ML backend on the UI

Quickstart with an example ML backend

git clone https://github.com/heartexlabs/label-studio-ml-backend
cd label-studio-ml-backend/label_studio_ml/examples/simple_text_classifier
docker-compose up

http://localhost:9090/

Start your custom ML backend with Label Studio

  1. Setup environment

    cd label-studio-ml-backend
# Install label-studio-ml and its dependencies
pip install -U -e .
# Install the dependencies for the example or your custom ML backend
pip install -r label_studio_ml/examples/requirements.txt

  2. Initialize an ML backend based on an example script:

    label-studio-ml init my_ml_backend --script label_studio_ml/examples/simple_text_classifier/simple_text_classifier.py

  3. Start ML backend server

    label-studio-ml start my_ml_backend

  4. Start Label Studio and connect it to the running ML backend on the project settings page.

References