Hi Trevor - Nice to see you here. :) For data parallelism, we have a simple wrapper that covers as many GPUs as you want in one box. For more than one box, we

Hi Trevor - Nice to see you here. :) For data parallelism, we have a simple wrapper that covers as many GPUs as you want in one box. For more than one box, we use Spark, and the integration is explained here: http://deeplearning4j.org/spark http://deeplearning4j.org/spark The description for multi-GPUs is through this link: http://deeplearning4j.org/gpu http://deeplearning4j.org/gpu under the subhead "Multi-GPU data parallelism". The code is here: https://github.com/deeplearning4j/deeplearning4j/blob/77b836cd7daba5fbd4c7a77d341d489cf6e9a220/deeplearning4j-core/src/main/java/org/deeplearning4j/parallelism/ParallelWrapper.java https://github.com/deeplearning4j/deeplearning4j/blob/77b836... We're almost done testing on DGX's now, like the one you have at OpenAI, and once we work a bit more with RDMA-enabled hardware, we'll go Sparkless for that. That's how we're hoping to get academic HW acceleration into production environments. In September, we plan to come out with a Scala API inspired by Keras/Torch, which will also share a neural net config file with Keras and allow model imports into DL4J.