Deeplearning4j Sharing Computational Graph between Threads in Scala

Question

I'm trying to do image classification. I'm using Scala, the Akka actor system, and deeplearning4j. The thing is that I have to detect always on the same spots or crop on the image. I was thinking of creating a new actor for each crop of the image, on each frame. The thing is that, from what I understand, instantiating a new model for each actor creation is not viable, but having an instance of the model, and passing to each actor isn't either. Should I have a pool of instances? I'm a bit stuck with this problem, since it is the first time I'm trying deeplearning4j. Previously, I would use a python REST api, but I think that this solution should perform better.

Thank you in advance.

Answer 1

1. There is no need to instantiate a new actor for each crop of the image. Simply keep a pool of actors that ask a master node to give them more images to classify as soon as they are done with the previous image. I also suggest to check whether it actually buys you anything compared to the ordinary parallel collections (sth. like images.par.map(model.classify) could already do the job; it would take care of a thread pool all by itself).
2. There is no need to instantiate a new model for each actor. The classification does not mutate the model, so you can simply share the same model between all actors. That's like 8 bytes overhead per actor for a reference to the model object, therefore negligible.