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ML-Telemetry

This project serves as a platform for telemetry data analysis using machine learning. The application is containerized with Docker, allowing for easy deployment and hot-reloading during development.

Table of Contents


Getting Started

To begin working with the app, clone this repository and ensure you have Docker installed.

Download Elysia's Telemetry Data

Download historical telemetry data for Elysia from Google Drive:

Save the data files locally in the training_data folder to ensure they are accessible when running the app.

Running Locally with Docker Compose

To run the app in a Docker container:

docker-compose up --build

This will build and start the containerized application, exposing it on http://localhost:8000.

Starting the Server for Hot Reloading

Alternatively, to run the app with hot reloading support, use:

python -m uvicorn app:app --reload --host 0.0.0.0 --port 8000

This is particularly useful during development, as it reloads the application automatically when changes are detected.


Accessing the Application

Once the app is running, you can access it by navigating to:


Docker Hub Repository

You can pull the application image directly from Docker Hub if you prefer not to build locally:

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