This project provides a Flask-based web service for generating image embeddings using the OpenCLIP model, leveraging GPU acceleration for efficient processing. It's containerized with Docker, ensuring easy deployment and scalability.
- Flask web application for handling image embedding requests.
- Utilizes OpenCLIP pre-trained models with support for multiple configurations.
- GPU acceleration for high-performance computation of embeddings.
- Docker and Docker Compose integration for easy setup and deployment.
Before you begin, ensure you have met the following requirements:
- Docker and Docker Compose installed on your system.
- NVIDIA Docker for GPU support within Docker containers.
- An NVIDIA GPU with the appropriate drivers installed.
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Clone the repository
git clone https://github.com/harperreed/imbedding.git cd imbedding -
Build and run the Docker container
Using Docker Compose, you can build and run the service with a single command:
docker-compose up --build
This command builds the Docker image and starts the service, making it accessible on port 1708.
To use the service, send a POST request to the /embeddings endpoint with an image file. For example, using curl:
curl -F "image=@path_to_your_image.jpg" http://localhost:1708/embeddingsReplace path_to_your_image.jpg with the actual path to an image file. The service will return the image embeddings as a JSON response.
The service can be configured through environment variables in the docker-compose.yml file. Available configurations include:
MODEL: OpenCLIP model variant (default:ViT-SO400M-14-SigLIP-384).MODEL_NAME: Model name for loading pre-trained weights.MAX_BATCH_SIZE: Maximum batch size for processing images.PORT: Port number the Flask application listens on.DEVICE: Device to run the model on (cuda:0for GPU).DEBUG: Enable/disable Flask debug mode.
Distributed under the MIT License. See LICENSE for more information.