A lot of our Docker images allow the user to make predictions in two ways: using simple file-polling or via a Redis backend. File-polling is great for testing, but unsuitable for a production system due to wear-and-tear on SSDs.
Initially, I developed a really simple library for sending and receiving data via Redis, called simple-redis-helper:
With this library you get some command-line tools for broadcasting, listening, etc. Sufficient for someone who is comfortable with the command-line (or especially when logged in remotely via terminal), but not so great for your clients.
Now, there is the brilliant gradio library that was specifically developed for such scenarios: to create easy to use and great looking interfaces for your machine learning models.
The last couple of days, I have put together a new library that is tailored to our Docker images called gifr:
With the first release, the following types of models are supported:
object detection/instance segmentation