In SimBA-Live, we need to perform accurate classifications as fast as possible. When generating your classifiers in SimBA, we recommend using as few estimators as you can while still getting accurate classifiation, as the number of estimators is positively related to the speed of the classification. An additional way of speeding up your classifier is to remove all the unnecessery bits of the SimBA `.sav` classifier file, and convert it into pure python. We can do this using the [pure-predict](https://github.com/Ibotta/pure-predict) library, accessable through the dropdown menus in SimBA-Live. 

Often-times, we do not have powerful computer near our animals, and the GPU-supported computers required for live classification is located in a different part of the building. In order to perform live classification of behavior in this scenario, we need to live-stream the video that is captured in the animal room, to the computer with the GPU-support. There are many ways to approach this issue, and SimBA-live includes a GUI-wrapper for doing this. 

Step 1. We initate the stream from the animal room computer (i.e., the computer with the attached camera recording the images). When we initate this stream, we need to tell the code where to send the images (i.e., the IP address of the computer with the GPU-support). 
