Installation

You can deploy Lumigator either locally or into a distributed environment using Kubernetes HELM Charts. In this guide, we’ll show you how to get started with a local deployment.

Prerequisites

Before you start, make sure you have the following:

  • A working installation of Docker

    • On MAC, Docker Desktop >= 4.3, and docker-compose >= 1.28.

    • On Linux, please also complete the post-installation steps.

  • The system Python; no version manager, such as pyenv or uv, should be active.

Local Deployment

You can run and develop Lumigator locally using docker-compose. This creates three container services networked together to make up all the components of the Lumigator application:

  • localstack: Local storage for datasets that mimics S3-API compatible functionality.

  • backend: Lumigator’s FastAPI REST API.

  • ray: A Ray cluster for submitting several types of jobs.

Note

Lumigator requires an SQL database to hold metadata for datasets and jobs. The local deployment uses SQLite for this purpose.

Despite the fact this is a local setup, it lends itself to more distributed scenarios. For instance, one could provide different AWS_* environment variables to the backend container to connect to any provider’s S3-compatible service, instead of localstack. Similarly, one could provide a different RAY_HEAD_NODE_HOST to move compute to a remote ray cluster, and so on. See here for an example of how to do this.

To deploy Lumigator locally:

  1. Clone the Lumigator repository:

    user@host:~$ git clone git@github.com:mozilla-ai/lumigator.git

  2. Change to the Lumigator directory:

    user@host:~$ cd lumigator

  3. Run the start-lumigator make target:

    user@host:~/lumigator$ make start-lumigator

Verify

To verify that Lumigator is running, open a browser and navigate to http://localhost:8000. You should receive the following JSON response:

{"Hello": "Lumigator!🐊"}

Note

If you need to change the port that the Lumigator service listens on, you can do it in the docker-compose.yaml file.

Next Steps

Now that you have Lumigator running locally, you can start using it to evaluate your language models. Next, we’ll show you how to interact with the Lumigator API to create a new evaluation job.