This guide describes the simplest way to start using ML backend with Label Studio.

Running with Docker (Recommended)

1. Start Machine Learning backend on http://localhost:9090 with prebuilt image:

docker-compose up

2. Validate that backend is running

$ curl http://localhost:9090/
{"status":"UP"}

3. Connect to the backend from Label Studio running on the same host: go to your project Settings -> Machine Learning -> Add Model and specify http://localhost:9090 as a URL.

Building from source (Advanced)

To build the ML backend from source, you have to clone the repository and build the Docker image:

docker-compose build

Running without Docker (Advanced)

To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:

python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements.txt

Then you can start the ML backend:

label-studio-ml start ./dir_with_your_model

Configuration

Parameters can be set in docker-compose.yml before running the container.

The following common parameters are available:

• BASIC_AUTH_USER - specify the basic auth user for the model server
• BASIC_AUTH_PASS - specify the basic auth password for the model server
• LOG_LEVEL - set the log level for the model server
• WORKERS - specify the number of workers for the model server
• THREADS - specify the number of threads for the model server

Customization

The ML backend can be customized by adding your own models and logic inside the ./dir_with_your_model directory.