Start Label Studio

After you install Label Studio, start the server to start using it.

label-studio start

By default, Label Studio starts with an SQLite database to store labeling tasks and annotations. You can specify different sources and target storage for labeling tasks and annotations using Label Studio UI or the API. See Database storage for more.

Command line arguments for starting Label Studio

You can specify a machine learning backend and other options using the command line interface. Run label-studio --help to see all available options, or refer to the following tables.

Some available commands for Label Studio provide information or start the Label Studio server:

Command	Description
label-studio	Start the Label Studio server.
label-studio -h label-studio --help	Display available command line arguments.
label-studio init <project_name> <optional_arguments>	Initialize a specific project in Label Studio.
label-studio start <project_name> --init <optional_arguments>	Start the Label Studio server and initialize a specific project.
label-studio reset_password	Reset the password for a specific Label Studio username. See Create user accounts for Label Studio.
label-studio shell	Get access to a shell for Label Studio to manipulate data directly. See documentation for the Django shell-plus command.
label-studio version	Show the version of Label Studio and then terminates.
label-studio user --username <email>	Show the user info with token.

The following command line arguments are optional and must be specified with label-studio start <argument> <value> or as an environment variable when you set up the environment to host Label Studio:

Command line argument	Environment variable	Default	Description
-b, --no-browser	N/A	False	Do not automatically open a web browser when starting Label Studio.
-db --database	LABEL_STUDIO_DATABASE	label_studio.sqlite3	Specify the database file path for storing labeling tasks and annotations. See Database storage.
--data-dir	LABEL_STUDIO_BASE_DATA_DIR	OS-specific	Directory to use to store all application-related data.
-d --debug	N/A	False	Enable debug mode for troubleshooting Label Studio.
-c --config	CONFIG_PATH	default_config.json	Deprecated, do not use. Specify the path to the server configuration for Label Studio.
-l --label-config	LABEL_STUDIO_LABEL_CONFIG	None	Path to the label configuration file for a specific Label Studio project. See Set up your labeling project.
--ml-backends	N/A	None	Command line argument is deprecated. Specify the URLs for one or more machine learning backends. See Set up machine learning with your labeling process.
--sampling	N/A	sequential	Specify one of sequential or uniform to define the order for labeling tasks. See Set up task sampling for your project on this page.
--log-level	N/A	ERROR	One of DEBUG, INFO, WARNING, or ERROR. Use to specify the logging level for the Label Studio server.
-p --port	LABEL_STUDIO_PORT	8080	Specify the web server port for Label Studio. Defaults to 8080. See Run Label Studio on localhost with a different port on this page.
--host	LABEL_STUDIO_HOST	''	Specify the hostname to use to generate links for imported labeling tasks or static loading requirements. Leave empty to make all paths relative to the root domain. For example, specify "https://77.42.77.42:1234" or "http://ls.example.com/subdomain/". See Run Label Studio with an external domain name on this page.
--cert	LABEL_STUDIO_CERT_FILE	None	Deprecated, do not use. Certificate file to use to access Label Studio over HTTPS. Must be in PEM format. See Run Label Studio with HTTPS on this page.
--key	LABEL_STUDIO_KEY_FILE	None	Deprecated, do not use. Private key file for HTTPS connection. Must be in PEM format. See Run Label Studio with HTTPS on this page.
--initial-project-description	LABEL_STUDIO_PROJECT_DESC	''	Specify a project description for a Label Studio project. See Set up your labeling project.
--password	LABEL_STUDIO_PASSWORD	None	Password to use for the default user. See Set up user accounts.
--username	LABEL_STUDIO_USERNAME	default_user@localhost	Username to use for the default user. See Set up user accounts.
--user-token	LABEL_STUDIO_USER_TOKEN	Automatically generated.	Authentication token for a user to use for the API. Must be set with a username, otherwise automatically generated. See Set up user accounts.
--agree-fix-sqlite	N/A	False	Automatically agree to let Label Studio fix SQLite issues when using Python 3.6–3.8 on Windows operating systems.
N/A	LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED	False	Allow Label Studio to access local file directories to import storage. See Run Label Studio on Docker and use local storage.
N/A	LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT	/	Specify the root directory for Label Studio to use when accessing local file directories. See Run Label Studio on Docker and use local storage.

Set environment variables

How you set environment variables depends on the operating system and the environment in which you deploy Label Studio.

In *nix operating systems, you can set environment variables from the command line or with an environment setup file. For example:

export LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true

On Windows, you can use the following syntax:

set LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true

To check if you set an environment variable successfully, run the following on *nix operating systems:

echo $LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED

Or the following on Windows operating systems:

echo %LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED%

Run Label Studio on localhost with a different port

By default, Label Studio runs on port 8080. If that port is already in use or if you want to specify a different port, start Label Studio with the following command:

label-studio start --port <port>

For example, start Label Studio on port 9001:

label-studio start --port 9001

Or, set the following environment variable:

LABEL_STUDIO_PORT = 9001

Run Label Studio on Docker with a different port

To run Label Studio on Docker with a port other than the default of 8080, use the port argument when starting Label Studio on Docker. For example, to start Label Studio in a Docker container accessible with port 9001, run the following:

docker run -it -p 9001:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio

Or, if you’re using Docker Compose, update the docker-compose.yml file that you’re using to expose a different port for the NGINX server used to proxy the connection to Label Studio. For example, this portion of the docker-compose.yml file exposes port 9001 instead of port 80 for proxying Label Studio:

...
nginx:
    image: nginx:latest
    ports:
      - 9001:80
    depends_on:
      - app
...

Run Label Studio on Docker with a host and sub-path

To run Label Studio on Docker with a host and sub-path, just pass LABEL_STUDIO_HOST env variable with sub-path to docker/docker-compose:

LABEL_STUDIO_HOST=http://localhost:8080/foo docker-compose up -d

Expose a local Label Studio instance outside using ngrok

Sometimes it’s useful to have the LabelStudio instance you’re running on your local machine to be reachable over the internet.

One way to do that is to use ngrok, a reverse proxy that allows you to expose your instance to the Internet.

1. Install ngrok: Follow the official ngrok guidelines
2. Authenticate the ngrok if this is your first time running it (the token can be obtained from Ngrok dashboard): ngrok config add-authtoken <Your token>
3. Start ngrok and point it at Label Studio: ngrok http --host-header=rewrite 8080
4. Copy the Forwarding URL ngrok displays. e.g.: https://xx-xx-xx-xx.eu.ngrok.io
5. Pass this url to Label Studio upon startup:

# python instance
LABEL_STUDIO_HOST=https://xx-xx-xx-xx.eu.ngrok.io label-studio start

# docker container
docker run -it -e LABEL_STUDIO_HOST=https://xx-xx-xx-xx.eu.ngrok.io -p 8080:8080 -v <yourvolume>:/label-studio/data heartexlabs/label-studio:latest

# docker-compose
LABEL_STUDIO_HOST=https://xx-xx-xx-xx.eu.ngrok.io docker-compose up -d

6. Open the ngrok URL in browser to make sure you see your instance
7. Done

Run Label Studio on Docker and use Local Storage

To run Label Studio on Docker and reference persistent local storage directories, mount those directories as volumes when you start Label Studio and specify any environment variables you need.

The following command starts a Docker container with the latest image of Label Studio with port 8080 and an environment variable that allows Label Studio to access local files. In this example, a local directory ./myfiles is mounted to the /label-studio/files location.

docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data \
--env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true \ 
--env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/files \ 
-v $(pwd)/myfiles:/label-studio/files \
heartexlabs/label-studio:latest label-studio

By specifying the environment variable LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/files, Label Studio only scans this directory for local files. It’s highly recommended to explicitly specify a LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT path to secure the volume access from the Docker container to your local machine.

Place files in the specified source directory (./myfiles in this example) and reference that directory when you set up local storage. For more information about using volume mounts in Docker, see the Docker documentation on volumes.

If you’re using Docker Compose, specify the volumes in the Docker Compose YAML file and add the relevant environment variables to the app container. For more about specifying volumes in Docker Compose, see the volumes section of the Docker Compose file documentation.

Run Label Studio with HTTPS

To run Label Studio with HTTPS and access the web server using HTTPS in the browser, use NGINX or another web server to run HTTPS for Label Studio.

Run Label Studio on the cloud using Heroku

To run Label Studio on the cloud using Heroku, specify an environment variable so that Label Studio loads.

LABEL_STUDIO_HOST

If you want, you can specify a different hostname for Label Studio, but you don’t need to.

To run Label Studio with Heroku and use PostgreSQL as the database storage, specify the PostgreSQL environment variables required as part of the Heroku environment variable DATABASE_URL. For example, to specify a PostgreSQL database hosted on Amazon:

DATABASE_URL = postgres://username:password@hostname.compute.amazonaws.com:5432/dbname

Then you can specify the required environment variables for a PostgreSQL connection as config variables. See Database storage.

Our Heroku manifest uses postgresql addon out of the box.
Please notice that the storage capacity is limited.

Please notice that all uploaded data via import will be lost after a dyno replace since filesystem is ephemeral.
Using S3 storage is recommended.

Run Label Studio with an external domain name

If you want multiple people to collaborate on a project, you might want to run Label Studio with an external domain name.

To do that, use the host parameter when you start Label Studio. These parameters ensure that the correct URLs are created when importing resource files (images, audio, etc) and generating labeling tasks.

There are several possible ways to run Label Studio with an external domain name.

• Replace the host parameter in the file which you specified with --config option. If you don’t use --config then edit label_studio/utils/schema/default_config.json in the Label Studio package directory.
• Specify the parameters when you start Label Studio: label-studio start --host http://your.domain.com/ls-root.
• Specify the parameters as environment variables HOST especially when setting up Docker: HOST=https://your.domain.com:7777.

Or, you can use environment variables:

LABEL_STUDIO_HOST = https://subdomain.example.com:7777

You must specify the protocol for the domain name: http:// or https://

If your external host has a port, specify the port as part of the host name.

Set up task sampling for your project

When you start Label Studio, you can control the order in which tasks are exposed to annotators for a specific project.

For example, to create a project with sequential task ordering for annotators:

label-studio start <project_name> --sampling sequential

The following table lists the available sampling options:

Option	Description
sequential	Default. Tasks are shown to annotators in ascending order by the id field.
uniform	Tasks are sampled with equal probabilities.
prediction-score-min	Tasks with the minimum average prediction score are shown to annotators. To use this option, you must also include predictions data in the task data that you import into Label Studio.

You can also use the API to set up sampling for a specific project. Send a PATCH request to the /api/projects/<project_id> endpoint to set sampling for the specified project. See the API reference for projects.

Individual annotators can also control the order in which they label tasks by adjusting the filtering and ordering of labeling tasks in the Label Studio UI. See Set up your labeling project.