Morpheus ML Template - Quickstart Guide

Welcome to Morpheus ML Template, a quick start machine learning framework specializing in image and text generation. This guide will help you quickly set up and run the project.

---

Prerequisites

Before starting, ensure you have the following prerequisites:

1. Docker and Docker Compose: The project is containerized for ease of deployment. Ensure you have Docker and Docker Compose installed on your system. Visit Docker's official website for installation instructions.

2. GPU Availability: This project requires a GPU for optimal performance, particularly for tasks like image generation with Stable Diffusion models. Verify that your system has a compatible GPU.

3. Google Firebase Account: This template uses Firebase for auth and analytics. Please create a Google Firebase account, set up a web application under Firebase, and generate a Python service account. Make sure to enable authentication. Visit Firebase Console to get started. You can also follow the Morpheus Firebase Integration Guide

4. AWS S3 credentials: This template uses AWS S3 for image storage. Please create an AWS account and generate an access key and secret key. Visit AWS Console to get started.

---

Configuration

Environment Variables: After setting up Firebase and AWS credentials, replace the necessary values in

the server, client, and worker configurations with your environment variables.

cp -p frontend/.env.dist frontend/.env.local
# change the values in frontend/.env.local

cp -p server/secrets.env.dist server/secrets.env
# change the values in server/secrets.env

cp -p worker/secrets.env.dist worker/secrets.env
# change the values in worker/secrets.env

Running the Project with Docker

Follow these steps to deploy Morpheus ML with Docker and Docker compose:

Build Docker Compose Services:

Navigate to the root directory of the project and build the Docker services using:

docker-compose build

Database Migrations:

Run the following command to apply database migrations using Alembic:

docker compose run --rm server  alembic upgrade head

Start the Server:

Launch the project using:

docker-compose up

Upon successful launch, the following services will be accessible:

• Client: Running on localhost:3000.
• Server: Accessible at localhost:8001.
• Server docs: Available at localhost:8001/docs.
• Ray Worker: Running on localhost:8000.
• Ray Docs: Accessible at localhost:8000/docs.
• Ray Dashboard: Available at localhost:8265.
• PGAdmin: Running on localhost:8002.

---

Development

Running the backend tests

• To run all the tests: docker compose run --rm api pytest.
• To run a specific test: docker compose run --rm api pytest tests/«test_module».py.
• To run a specific test function: docker compose run --rm api pytest tests/«test_module».py::«test_function».

Running the migrations

To use morpheus-data image to run the migrations, you need to create a secrets.env file in the morpheus-server directory. For more information, you can read the morpheus-data README.

• To generate the migrations file:

docker compose run --rm server alembic revision --autogenerate -m "Initial migration"

• To apply the migrations or update the head:

docker compose run --rm server alembic upgrade head

PG admin

The template includes a pg admin service where you can manage your database. If you want to add new models or make adjustments or changes to your database, you can connect to the pg admin dashboard at localhost:8002. Once in the browser, authenticate with your credentials, default credentials for PGAdmin are:

[email protected]
PGADMIN_DEFAULT_PASSWORD=adminmorpheus

Once you have logged into the server, make sure to create a connection with the database server using the database credentials. You can check your server/secrets.env file to add the data. The default values are

POSTGRES_USER=morpheus
POSTGRES_DB=morpheus
POSTGRES_PASSWORD=password
POSTGRES_HOST=postgres
POSTGRES_PORT=5432

After you have created the connection, you can access the database and make changes to it.

---

Features

Morpheus ML Template offers the following main features:

Image Generation

Utilizes Stable Diffusion models for state-of-the-art image generation.

Text Generation

Leverages Large Language Models (LLM) for advanced text generation capabilities.

Storytelling

A unique feature combining both image and text generation to create compelling narratives.

Authentication:

Supports email-password and Google login.

User Profile

A user profile page with an update form.

Newsletters

Form for users to subscribe to newsletters.

Analytics

Google Analytics integration

For detailed documentation, feature descriptions, and advanced configurations, please refer to the Morpheus Wiki.

---

We hope you enjoy working with Morpheus ML Template. For any issues or contributions, please refer to our GitHub repository or contact the development team.