[agibsonccc]

Hi Mark, I want to correct one thing. My company is behind dl4j. We are also the second largest contributor to keras. We have near perfect model import (with keras contributors maintaining it). Dl4j also has TF import and a native graph runner: https://github.com/deeplearning4j/dl4j-examples/tree/master/...

Java itself tends to be fairly verbose I know, but not only are there wrappers but for inference if you want to use python for basics and then us for inference. We enable several ways to do that. Folks tend to oversensationlize these "us vs them" framework "fights".

Rather than do that, why not just use what's good for each use case? Python for training, java for deployment?

Beyond that, you could also consider building your own wrapper maybe? In clojure, there's jutsu.ai for example: https://github.com/hswick/jutsu.ai

[mark_l_watson]

Thanks for the info! Sorry if I sounded like I was knocking DL4J: I have used it in examples for two books I have written.

I use Keras at work so that is why I prefer it - I am very used to Keras, and to a lesser degree TensorFlow.

Just tonight I was experimenting with jutsu.ai, and your advice of training on Keras and running on DL4J is good advice. I hacked up a little code to import Keras weights into Racket, but I can only process dense layers, and not RNN or complex architectures.

[agibsonccc]

Thanks! Usually dl4j is targeted at data engineers, not python folks. We actually have the end to end system working.