Is it possible to switch conda environments while Streamlit is running?

Hi there,

I’m a new user to Streamlit and have been playing around from past couple of weeks. I’m trying to create a application for Word embedding. Part of my models use libraries/code base that is dependent on Tensorflow 1.X and part of them use Tensorflow 2.X and also multiple pytorch dependencies. I have two separate anaconda environments for either of them to avoid dependency issues.

I want to have just one application file which will show/run all models and results irrespective of the environments (or hiding all environment details from user). Is there a simple/dynamic way to switch conda environments while the Streamlit app is running.

Was hoping advanced users might have some good insights on this ?

Thanks!
Mohammed Ayub

Hi @mohammed_ayub, welcome to the Streamlit community!

In general, no, you can’t switch conda environments from the same Python session (this is a Python limitation, not a Streamlit one). Once you load a module one time, trying to reload it again won’t do anything:

So once Tensorflow 1.x is loaded, that’s what you have. Additionally, with conda, the Python interpreter is part of the environment…trying to “switch” the interpreter is essentially stopping the code and starting a different executable, which means Streamlit would no longer be running.

In industry, when people want to serve the results of a model, they wrap it up in their own process as an API, then call the API. You could do this in your case…depending on a drop-down or radio button in streamlit, you could call the different API endpoints for your model results.

Thanks @randyzwitch . That makes sense and right now looks like the best option.
I might convert it to SavedModel format and serve it using Tensorflow Serving API.

Cheers !