BrandPost: A Guide to Using NDArrays in Java
Within many development languages, there is a popular paradigm of using N-Dimensional arrays. They allow you to write numerical code that would otherwise require many levels of nested loops in only a few simple operations. Because of the ability to parallelize, it often runs even faster than the standard looping as well. This is now standard practice in many fields such as data science, graphics, and deep learning, but can be used in applications far beyond this.
In Python, the standard library for NDArrays is called NumPy. However, there is no equivalent standard library in Java. One offering for Java developers interested in working with NDArrays is AWS’s Deep Java Library (DJL). Although it also contains Deep Learning, the core is a powerful NDArray system that can be used on its own to bring this paradigm into Java. With support for several Deep Learning Frameworks (PyTorch, TensorFlow, MXNet), DJL can allow the NDArray operations to run at a large-scale and across multiple platforms. No matter whether you are running on CPU or GPU, PC or Android, it simply works.
In this tutorial, we will walk through how you can leverage the NDArray from DJL to write your NumPy code in Java and apply NDArray into a real-world application.