Docker Quickstart#

Argilla is composed of a Python Server with Elasticsearch as the database layer, and a Python Client to create and manage datasets.

To get started you just need to run the docker image with following command:

  docker run -d --network argilla-net --name quickstart -p 6900:6900 argilla/argilla-quickstart:latest

Apple Silicon Issues

Apple Silicon M1/M2 users might have issues with this deployment. To resolve this, use the --platform linux/arm64 parameter. If this doesn’t work, deploying separate images or via docker-compose will work.

This will run the latest quickstart docker image with 2 users argilla and team. The password for these users is 1234 . You can also configure these environment variables as per you needs.

Environment Variables#

  • ARGILLA_API_KEY: Argilla provides a Python library to interact with the app (read, write, and update data, log model predictions, etc.). If you don’t set this variable, the library and your app will use the default API key. If you want to secure your app for reading and writing data, we recommend you to set up this variable. The API key you choose can be any string of your choice and you can check an online generator if you like.

  • ARGILLA_PASSWORD: This sets a custom password for login into the app with the argilla username. The default password is 1234. By setting up a custom password you can use your own password to login into the app.

  • TEAM_API_KEY: This sets the root user’s API key. The API key you choose can be any string of your choice and you can check an online generator if you like.

  • TEAM_PASSWORD: This sets a custom password for login into the app with the argilla username. The default password is 1234. By setting up a custom password you can use your own password to login into the app.

  • LOAD_DATASETS: This variables will allow you to load sample datasets. The default value will be full. The supported values for this variable is as follows:

    1. single: Load single datasets for TextClassification task.

    2. full: Load all the sample datasets for NLP tasks (TokenClassification, TextClassification, Text2Text)

    3. none: No datasets being loaded.