Flux: A Machine Learning Framework for Julia

There was a HUGE announcement on the Julia blog a few days ago. The convergence of a language for machine learning and marrying it with a compiler just got a bit closer. Julia announced Flux, a machine learning frame work for Julia. 

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Deploying Julia with Domo

This is a short by great video of Julia in production with Domo.


JuliaCon 2018 Julia with Domo

I really like Julia. A lot.





Notes from the video:

  • Java backend, some C++ and some Go
  • Want to be scale analytics
  • Julia iterates quickly
  • Math and Statistics are right there, easy accessible
  • Uses Resources.jl, Events.jl to Service.jl to Mapper.jl
  • All analytics done at the Service.jl layer
  • Wrapped up via Docker (this is pretty cool)
  • Includes all dependencies on docker image, great way to upgrade everything in one spot
  • Deploy into Kubernetes environment OR cloud instance
  • Open sourced config handling, context setting/handling, and process data-sharing

Mashing Up Julia Language with RapidMiner

If you want to execute any Python in RapidMiner, you have to use the Execute Python operator. This operator makes things so simple that people use the hell out o fit. However, it wasn’t so simple in the “old days.” It could still be done but it required more effort, and that’s what I did with the Julia Language. I mashed up the Julia Language with RapidMiner with only a few extra steps.

The way we mashed up other programs and languages in the old days was to use the Execute Program operator. That operator let’s you execute arbitrary programs in RapidMiner within the process. Want to kick of some Java at run time? You could do it. Want to use Python? You could do that (and I did) too!

The best part? You can still use this operator today and that’s what I did with Julia. Mind you, this is tutorial is a simple Proof of Concept, I didn’t do anything fancy, but it works.

What I did was take some a RapidMiner sample data set (Golf) and pass it to a Julia script that writes it out as a CSV file. I save the CSV file to a working directory defined by the Julia script.

Tutorial Processes

A few prerequisites, you’ll need RapidMiner and Julia installed. Make sure your Julia path is correct in your environment variables. I had some trouble in Windows with this but it worked fine after I fixed it.

Below you’ll find the XML for the Rapidminer process and the simple Julia script. I named the script read.jl and called from my Dropbox, you’ll need to repath this on your computer.

The RapidMiner Process


The Julia Language script

Note: You’ll need to “Pkg.add(“Dataframes”)” to Julia first.

Of course the next steps is to write a more defined Julia script, pass the data back INTO RapidMiner, and then continue processing it downstream.