Chat with Video App

Chat with Video App is a multimodal chat application that allows users to upload a video and ask questions about the video content based on the visual and audio information. See Chat with Video Demo notebook for more information.

Installation

To install the project, follow these steps:

1. Clone the repository.

2. 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.

3. 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

4. Run the app.

CUDA_VISIBLE_DEVICES="0" aana deploy aana_chat_with_video.app:aana_app

Usage

To use the project, follow these steps:

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

CUDA_VISIBLE_DEVICES="0" aana deploy aana_chat_with_video.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

The applications require 1 large GPUs to run. GPU should have at least 48GB of memory.

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.

See Chat with Video Demo notebook for more information.

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

The applications require 1 GPUs to run.

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.