You can serve a model using Docker to deploy a GAN for image generation by referring to the following:
- Prepare the GAN Model and Save It
- Save the trained GAN model and necessary preprocessing code.
- Create a Flask App for Serving the GAN
- Write a Python script (app.py) to load the model and handle requests.
- Write a Dockerfile
- Create a Dockerfile to package the Flask app
- Build and Run the Docker Container
- Build the Docker image and run it locally or deploy it to your preferred cloud platform
Here is the code you can refer to:
![](https://www.edureka.co/community/?qa=blob&qa_blobid=14411640617956406150)
![](https://www.edureka.co/community/?qa=blob&qa_blobid=12137534788530600171)
![](https://www.edureka.co/community/?qa=blob&qa_blobid=6823727256662820378)
![](https://www.edureka.co/community/?qa=blob&qa_blobid=11830150642338653054)
Hence, this setup serves your GAN model via a REST API. Clients can send requests to the /generate endpoint to generate images.