Aana Application Template

Aana SDK is a powerful framework for building multimodal applications. It facilitates the large-scale deployment of machine learning models, including those for vision, audio, and language, and supports Retrieval-Augmented Generation (RAG) systems. This enables the development of advanced applications such as search engines, recommendation systems, and data insights platforms.

This repository contains a template that you can use to start building your own Aana application. It will help you get started with the Aana SDK and provide you with a basic structure for your application and its dependencies.

How to use this template

1. Click on Use this template.
2. Give your repository a name and click on "Create repository". The name of the repository will also be the name of your application and the Python package.
3. Wait for the first workflow to finish. This will rename the package to match the repository name.
4. Clone the repository to your local machine and start building your application.
5. Change the LICENSE file to match your project's license. The default license is the Apache License 2.0.

Getting started

The project template uses Poetry for dependency management. To install the project, run the following commands:

poetry install

See Tutorial for more information on how to build your application.

Project structure

aana_app_project/
├── config/                   | various configs, including settings, deployments and endpoints
│   ├── endpoints.py          | list of endpoints to deploy
│   ├── deployments.py        | list of deployments (models) to deploy
│   └── settings.py           | app settings
├── core/                     | core models and functionality
│   ├── models/               | data models
│   └── prompts/              | prompt templates for LLMs
├── deployments/              | custom deployments
├── endpoints/                | endpoint classes for the app
├── exceptions/               | custom exception classes
├── utils/                    | various utility functionality
└── app.py                    | main application file

Installation

To install the project, follow these steps:

1. Clone the repository.

2. Install the package with Poetry.

The project is managed with Poetry. See the Poetry installation instructions on how to install it on your system.

It will install the package and all dependencies in a virtual environment.

poetry install

3. Install additional libraries.

For optimal performance, you should also install PyTorch version >=2.1 appropriate for your system. You can continue directly to the next step, but it will install a default version that may not make optimal use of your system's resources, for example, a GPU or even some SIMD operations. Therefore we recommend choosing your PyTorch package carefully and installing it manually.

Some models use Flash Attention. Install Flash Attention library for better performance. See flash attention installation instructions for more details and supported GPUs.

4. Activate the Poetry environment.

To activate the Poetry environment, run the following command:

poetry shell

Alternatively, you can run commands in the Poetry environment by prefixing them with poetry run. For example:

poetry run aana deploy aana_app_project.app:aana_app

5. Run the app.

aana deploy aana_app_project.app:aana_app

Usage

To use the project, follow these steps:

1. Run the app as described in the installation section.

aana deploy aana_app_project.app:aana_app

Once the application is running, you will see the message Deployed successfully. in the logs. It will also show the URL for the API documentation.

⚠️ Warning

If the application is using GPU, make sure that the GPU is available and the application can access it.

The applications will detect the available GPU automatically but you need to make sure that CUDA_VISIBLE_DEVICES is set correctly.

Sometimes CUDA_VISIBLE_DEVICES is set to an empty string and the application will not be able to detect the GPU. Use unset CUDA_VISIBLE_DEVICES to unset the variable.

You can also set the CUDA_VISIBLE_DEVICES environment variable to the GPU index you want to use: export CUDA_VISIBLE_DEVICES=0.

2. Send a POST request to the app.

For example, if your application has /summary endpoint that accepts videos, you can send a POST request like this:

curl -X POST http://127.0.0.1:8000/summary -Fbody='{"video":{"url":"https://www.youtube.com/watch?v=VhJFyyukAzA"}}'

Running with Docker

We provide a docker-compose configuration to run the application in a Docker container.

Requirements:

• Docker Engine >= 26.1.0
• Docker Compose >= 1.29.2
• NVIDIA Driver >= 525.60.13

To run the application, simply run the following command:

docker-compose up

The application will be accessible at http://localhost:8000 on the host server.

⚠️ Warning

If your applications requires GPU to run, you need to specify which GPU to use.

The applications will detect the available GPU automatically but you need to make sure that CUDA_VISIBLE_DEVICES is set correctly.

Sometimes CUDA_VISIBLE_DEVICES is set to an empty string and the application will not be able to detect the GPU. Use unset CUDA_VISIBLE_DEVICES to unset the variable.

You can also set the CUDA_VISIBLE_DEVICES environment variable to the GPU index you want to use: CUDA_VISIBLE_DEVICES=0 docker-compose up.

💡Tip

Some models use Flash Attention for better performance. You can set the build argument INSTALL_FLASH_ATTENTION to true to install Flash Attention.

INSTALL_FLASH_ATTENTION=true docker-compose build

After building the image, you can use docker-compose up command to run the application.

You can also set the INSTALL_FLASH_ATTENTION environment variable to true in the docker-compose.yaml file.