Optimising dl4j deployment size

DL4J
1

Hi,

As I discussed in another thread recently, I need to deploy DL4j using a megajar (or fat jar, if you prefer), with all of the DL4j resources exported in it. This file is 1.2GB large.

For my purposes, I simply need to load a multilayernetwork and normalizer from disk, and compare a new value against the network (model.output). By manually entering the file with a software such as WinRar, I am able to delete unneeded files (e.g. android libraries, etc). This reduces it down to 800MB.

Although I would prefer one JAR for all operating systems, I can cut it down to 400MB if I make a JAR for just one operating system. But ideally I would like it to be ~100MB large.

I feel like going in and deleting classes manually with WinRar is a poor solution, would you have a better recommendation for optimising the dl4j size?

2

This will help you reduce the jar size by only including the necessary binaries for the deployment platform.

You probably also want to reduce the other dependencies too, as deeplearning4j-core will pull in a lot of things that aren’t exactly necessary for your use case.

Try starting with just deeplearning4j-nn and then add whatever you are missing one by one to get to the minimal size.