Predicting And Training In Different Threads Keras Tensorflow

I am using Keras and Tensorflow to make a kind-of online learning, where I receive new data periodically and I retrain my models with this new data. I can have several models store

Solution 1:

Easiest solution is to have two separate keras models - the first runs in inference mode, and the second runs in training mode. Every time the inference model gets a new dataset to predict on, it first checks to see if it has the most "up to date" .h5 file, if not then it loads it in first then runs the prediction. This way you can avoid locks and such.

It's hard to give advice specific to your case because what you want is likely not the same as what I need

• This is my opinion after having done something similar with Tensorflow Multiprocessing