/ Machine Learning

An in-memory machine learning library that can be summed up

An in-memory machine learning library that can be summed up

doddle-model

doddle-model is an in-memory machine learning library that can be summed up with three main characteristics:

How does it compare to existing solutions?

doddle-model takes the position of scikit-learn in Scala and as a consequence, it's much more lightweight than e.g. Spark ML. Fitted models can be deployed anywhere, from simple applications to concurrent, distributed systems built with Akka, Apache Beam or a framework of your choice. Training of estimators happens in-memory, which is advantageous unless you are dealing with enormous datasets that absolutely cannot fit into RAM.

Installation

The project is published for Scala versions 2.11, 2.12 and 2.13. Add the dependency to your SBT project definition:

libraryDependencies  ++= Seq(
  "io.github.picnicml" %% "doddle-model" % "<latest_version>",
  // add optionally to utilize native libraries for a significant performance boost
  "org.scalanlp" %% "breeze-natives" % "1.0"
)

Note that the latest version is displayed in the Latest Release badge above and that the v prefix should be removed from the SBT definition.

Getting Started

For a complete list of code examples see doddle-model-examples.

Performance

Performance of implementations is described here. Also, take a peek at what's written in that document if you encounter java.lang.OutOfMemoryError: Java heap space.

GitHub