A repo for implementing a simple message queue based server architecture to asynchronously handle resource-intensive tasks(e.g., ML inference). For detailed information about RabbitMQ, please check this article: Handling resource-intensive tasks with work queues in RabbitMQ.

The operation process is as follows:

1. Subscribe a request message from the API Server (request queue)
   • In this example, the API Server is implemented in node.js
2. StarGAN v2 Inference: Generate eight images and upload them to AWS S3
3. Publish a result message to the subscriber on the API Server (result queue)

• Linux is recommended for performance and compatibility reasons.
• One or more high-end NVIDIA GPUs, NVIDIA drivers, CUDA toolkit and cuDNN.
• 64-bit Python 3.6 installation. I strongly recommend Anaconda3.
• Install the packages from requirements.txt.
• Docker users: use the provided Dockerfile to build an image with the required library dependencies.