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What is Label Studio?
Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.
Have a custom dataset? You can customize Label Studio to fit your needs. Read an introductory blog post to learn more.
Try out Label Studio
Install Label Studio locally or deploy it in a cloud instance. Or sign up for a free trial of our Starter Cloud edition! You can learn more about what each edition offers here.
Install locally with Docker
Official Label Studio docker image is here and it can be downloaded with docker pull.
Run Label Studio in a Docker container and access it at http://localhost:8080.
docker pull heartexlabs/label-studio:latest
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest
You can find all the generated assets, including SQLite3 database storage label_studio.sqlite3 and uploaded files, in the ./mydata directory.
Override default Docker install
You can override the default launch command by appending the new arguments:
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG
Build a local image with Docker
If you want to build a local image, run:
docker build -t heartexlabs/label-studio:latest .
Run with Docker Compose
Docker Compose script provides production-ready stack consisting of the following components:
- 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
Run with Docker Compose + MinIO
You can also run it with an additional MinIO server for local S3 storage. This is particularly useful when you want to
test the behavior with S3 storage on your local system. To start Label Studio in this way, you need to run the following command:
# Add sudo on Linux if you are not a member of the docker group
docker compose -f docker-compose.yml -f docker-compose.minio.yml up -d
If you do not have a static IP address, you must create an entry in your hosts file so that both Label Studio and your
browser can access the MinIO server. For more detailed instructions, please refer to our guide on storing data.
Install locally with pip
# Requires Python >=3.10
pip install label-studio
# Start the server at http://localhost:8080
label-studio
Install locally with poetry
### install poetry
pip install poetry
### set poetry environment
poetry new my-label-studio
cd my-label-studio
poetry add label-studio
### activate poetry environment
poetry shell
### Start the server at http://localhost:8080
label-studio
Install locally with Anaconda
conda create --name label-studio
conda activate label-studio
conda install psycopg2
pip install label-studio
Install for local development
You can run the latest Label Studio version locally without installing the package from pypi.
# Install all package dependencies
pip install poetry
poetry install
# Run database migrations
python label_studio/manage.py migrate
python label_studio/manage.py collectstatic
# Start the server in development mode at http://localhost:8080
python label_studio/manage.py runserver
Deploy in a cloud instance
You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform:
Apply frontend changes
For information about updating the frontend, see label-studio/web/README.md.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
