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TensorMap

🌟 Overview

TensorMap is a web application that allows users to create machine learning algorithms visually. TensorMap supports reverse engineering of the visual layout to a TensorFlow implementation in preferred languages. The goal of the project is to let beginners play with machine learning algorithms in TensorFlow without requiring extensive background knowledge about the library. For more details about the project, read our project wiki.

🚀 Key Features

  • Drag-and-drop interface for neural network design

  • Auto-generation of TensorFlow code (Python/JavaScript)

  • Model visualization and version control

  • Export capabilities for trained models

  • Collaborative workspace support

Getting Started

Follow these steps to set up and run TensorMap using Docker.

Prerequisites

System Architecture

This repository has the following structure:

TensorMap/
├── tensormap-server/  # Backend services
├── tensormap-client/  # Frontend interface
├── docs/              # Documentation
└── scripts/           # Deployment

Running TensorMap with Docker

  1. Clone the Repository:

    git clone https://github.com/c2siorg/tensormap.git
cd TensorMap

  2. Set Up Environment:

    • Ensure Docker and Docker Compose are installed and running on your machine.

  3. Build and Run the Application:

    Use Docker Compose to build and start the TensorMap services (database, server, and client):

    docker-compose up --build

    This will:

    • Start a PostgreSQL database.

    • Build and run the TensorMap server (Flask backend).

    • Build and run the TensorMap client (React frontend).

  4. Access the Application:

    • Frontend (Client): Open your browser and go to http://localhost:5173.

    • Backend (Server): The Flask API will be available at http://localhost:5000.

  5. Stop the Application:

    To stop the running services, press Ctrl+C in the terminal or run:

    docker-compose down

Docker Compose Configuration

The docker-compose.yml file defines the following services:

  • Database: PostgreSQL database for storing application data.

  • Server: Flask backend for TensorMap.

  • Client: React frontend for TensorMap.

You can modify the docker-compose.yml file to customize the setup (e.g., change ports or environment variables).

Development with Docker

If you're developing TensorMap, you can use Docker to streamline your workflow:

  • Rebuild and Restart the Client:

    docker-compose up --build client

  • View Logs:

    docker-compose logs client

  • Access the Container Shell:

    docker exec -it <client-container-id> /bin/sh

Development Workflow

Branching Strategy

git checkout -b feat/new-layer-type   # Feature development
git checkout -b fix/issue-123         # Bug fixes
git checkout -b docs/readme-update    # Documentation i

Testing

# Run backend tests
cd tensormap-server
pytest

# Run frontend tests
cd tensormap-client
npm test

Contributing

Please read the 'Note to Contributors' in the project wiki for more details and the 'Contributing.md'

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

This updated README.md includes clear instructions for running TensorMap using Docker, making it easier for users to get started. Let me know if you need further adjustments!