Different Routers with Dependency Conflicts

[brandonbiggs]

Hi all!

I have a question about scaling my API. The applications I'm trying to serve have very different needs. One app at /example_1 may need Tensorflow 1.4 but /example_2 needs Tensorflow 2.1. Unfortunately backwards compatibility won't work, especially as I add /example{3..n}. I am currently running into a big dependency issue (age old Python problem :D). So I have been exploring how to run each router in a separate "process" so they can access their own dependencies that don't conflict with each other. I'd like to run each out of a Docker container for ease of managing dependencies, but if I need to just use conda/pyenv that would work too.

Example of what I'm envisioning but may not be correct:

running in background:

$ python3 example_1.py

Also running in background

$ python3 example_2.py

Starting fastapi -

app = FastAPI()

from routers import example_1
app.include_router(example_1.router) # Is connecting to the example_1.py running in background

from routers import example_2
app.include_router(example_2.router) # Is connecting to the example_2.py running in background

I preferably don't want to run each router as it's own fastapi instance as I want to have all the docs, base urls, etc all out of the same place, but if I have to run each as their own fastapi instance I can. I don't know how to "scale" like this. I've tried googling/searching through the documentation, but I haven't been able to find anything helpful. I may also be looking in the wrong place, so any guidance would be greatly appreciated. Thanks.

[brandonbiggs]

Answered in Issue #5721