- Get started
- Label Studio features
- Billing & Usage
- Release notes
Install and Upgrade
- Install and upgrade Label Studio
- Install Label Studio Enterprise on-premises using Docker
- Deploy Label Studio Enterprise on Kubernetes
- Database setup
- Set up persistent storage
- Start Label Studio
Security and Privacy
- Secure Label Studio
- Set up user accounts
- Manage access
- Set up authentication
- Import data
- Import pre-annotations
- Cloud storage setup
Labeling and Projects
- Project setup
- Manage data
- Set up your labeling interface
- Label and annotate data
- Review annotations
- Annotation statistics
- Custom agreement metric
- Export annotations
Machine Learning Setup
- Machine learning setup
- Write your own ML backend
- ML Examples and Tutorials
- Active learning loop
- Troubleshoot machine learning
- Webhook Setup
- Webhooks Event Reference
- Custom Webhooks
- Python SDK Tutorial
- Backend API
- Frontend library
- Frontend reference
- Update scripts and API calls
Install and upgrade Label Studio
Install Label Studio on premises or in the cloud. Choose the installation method that works best for your environment:
- Install with pip
- Install with Docker
- Install on Ubuntu
- Install from source
- Install with Anaconda
- Upgrade Label Studio
You can install Label Studio on a Linux, Windows, or MacOSX machine running Python 3.6 or later.
Label Studio expects port 8080 to be open by default. To use a different port, specify it when starting Label Studio. See Start Label Studio.
Allocate disk space according to the amount of data you plan to label. As a benchmark, 1 million labeling tasks take up approximately 2.3GB on disk when using the SQLite database. 50GB of disk space is recommended for production instances.
Use a minimum of 8GB RAM, but 16GB RAM is recommended. for example, t3.large or t3.xlarge on Amazon AWS.
For more on using Label Studio at scale and labeling performance, see Start Label Studio.
PostgreSQL version 11.5 or SQLite version 3.35 or higher.
Label Studio is tested with the latest version of Google Chrome and is expected to work in the latest versions of:
- Google Chrome
- Apple Safari
- Mozilla Firefox
If using other web browsers, or older versions of supported web browsers, unexpected behavior could occur.
Install Label Studio in a clean Python environment. We highly recommend using a virtual environment (venv or conda) to reduce the likelihood of package conflicts or missing packages.
To install Label Studio with pip and a virtual environment, you need Python version 3.6 or later. Run the following:
python3 -m venv env source env/bin/activate python -m pip install label-studio
To install Label Studio with pip, you need Python version 3.6 or later. Run the following:
pip install label-studio
After you install Label Studio, start the server with the following command:
Label Studio is also available as a Docker container. Make sure you have Docker installed on your machine.
To install and start Label Studio at http://localhost:8080, storing all labeling data in
./my_project directory, run the following:
docker run -it -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio:latest
Or for Windows, you have to modify the volumes paths set by
You can override the default Docker install by appending new arguments:
docker run -it -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG
If you want to build a local image, run:
docker build -t heartexlabs/label-studio:latest .
Use Docker Compose to serve Label Studio at
http://localhost:8080. You must use Docker Compose version 1.25.0 or higher.
Start Label Studio:
docker-compose up -d
This starts Label Studio with a PostgreSQL database backend. You can also use a PostgreSQL database without Docker Compose. See Set up database storage.
To install Label Studio on Ubuntu and run it in a virtual environment, run the following command:
python3 -m venv env source env/bin/activate sudo apt install python3.9-dev python -m pip install label-studio
If you want to use nightly builds or extend the functionality, consider downloading the source code using Git and running Label Studio locally:
git clone https://github.com/heartexlabs/label-studio.git cd label-studio # Install all package dependencies pip install -e . # Run database migrations python label_studio/manage.py migrate # Start the server in development mode at http://localhost:8080 python label_studio/manage.py runserver
conda create --name label-studio conda activate label-studio pip install label-studio
You might see errors when installing Label Studio. Follow these steps to resolve them.
Many bugs might be fixed in patch releases or maintenance releases. Make sure you’re running the latest version of Label Studio by upgrading your installation before you start Label Studio.
If you see errors about missing packages, install those packages and try to install Label Studio again. Make sure that you run Label Studio in a clean Python environment, such as a virtual environment.
For Windows users the default installation might fail to build the
lxml package. Consider manually installing it from the unofficial Windows binaries. If you are running Windows 64-bit with Python 3.8 or later, run
pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl to install it.
If you see any other errors during installation, try to rerun the installation.
pip install --ignore-installed label-studio
To upgrade to the latest version of Label Studio, reinstall or upgrade using pip.
pip install --upgrade label-studio
Migration scripts run when you upgrade to version 1.0.0 from version 0.9.1 or earlier.
To make sure an existing project gets migrated, when you start Label Studio, run the following command:
label-studio start path/to/old/project
The most important change to be aware of is changes to rename “completions” to “annotations”. See the updated JSON format for completed tasks.
If you customized the Label Studio Frontend, see the Frontend reference guide for required updates to maintain compatibility with version 1.0.0.