Skip to main content

Installation

The platform is composed of different modules

  • UI: the front-end application
  • API: the back-end application
  • Processing: the Spark jobs
  • SDK: the Python SDK

Development & Testing with Docker Compose​

You can easily run the platform locally using Docker and the provided Docker Compose file.

Important: This setup is intended for development and testing only, not for production environments.

Prerequisites​

To run the platform successfully, you'll need to have both Docker and Docker Compose installed on your machine.

Procedure​

Once you've installed Docker and Docker Compose, clone the repository to your local machine:

git clone git@github.com:radicalbit/radicalbit-ai-monitoring.git

This repository provides a Docker Compose file to set up the platform locally alongside a K3s Kubernetes cluster. This allows you to deploy Spark jobs within the cluster.

For streamlined development and testing, you can execute these steps to run the platform locally without the graphical user interface:

docker compose up

If you want to access the platform's user interface (UI):

docker compose --profile ui up

After all containers are up and running, you can access the platform at http://localhost:5173 to start using it.

Accessing the Kubernetes Cluster​

The platform creates a Kubernetes cluster for managing deployments. You can connect and interact with this cluster from your local machine using tools like Lens or kubectl.

In the compose file is present a k9s container that can be used to monitor the K3s cluster.

docker compose up k9s -d && docker attach radicalbit-ai-monitoring-k9s-1
Using the kubeconfig File​

A file named kubeconfig.yaml is automatically generated within the directory ./docker/k3s_data/kubeconfig/ when the platform starts. This file contains sensitive information used to authenticate with the Kubernetes cluster.

Here's how to connect to the cluster:​
  1. Copy the kubeconfig.yaml file to a desired location on your local machine.
  2. Edit the copied file and replace the server address https://k3s:6443 with https://127.0.0.1:6443. This points the kubeconfig file to the local Kubernetes cluster running on your machine.
  3. Use the modified kubeconfig.yaml file with tools like Lens or kubectl to interact with the cluster.

Using Real AWS Credentials​

In order to use a real AWS instead of MinIO is necessary to modify the environment variables of the api container, putting real AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION and S3_BUCKET_NAME and removing S3_ENDPOINT_URL.

Teardown​

To completely clean up the environment we can use docker compose

docker compose --profile ui --profile k9s down -v --remove-orphans

To remove everything including container images:

docker compose --profile ui --profile k9s down -v --remove-orphans --rmi all