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ND4J is a tensor & N-dimensional array scientific computing library built for the JVM, and is part of the Deeplearning4j suite of software. It is meant to be used in production environments, which means routines are designed to run fast with minimum RAM requirements.

A usability gap has separated Java, Scala and Clojure programmers from the most powerful tools in data analysis, like NumPy or Matlab. Libraries like Breeze don’t support n-dimensional arrays, or tensors, which are necessary for deep learning and other tasks. Libraries like Colt and Parallel Colt use or have dependencies with GPL in the license, making them unsuitable for commercial use. ND4J and ND4S are used by national laboratories such as Nasa JPL for tasks such as climatic modeling, which require computationally intensive simulations.

ND4J brings the intuitive scientific computing tools of the Python community to the JVM in an open source, distributed and GPU-enabled library. In structure, it is similar to SLF4J. ND4J gives engineers in production environments an easy way to port their algorithms and interface with other libraries in the Java and Scala ecosystems.

Main features

  • Versatile n-dimensional array object
  • Multiplatform functionality including GPUs
  • Linear algebra and signal processing functions
  • Supports GPUs via CUDA
  • Integrates with Hadoop and Spark
  • ND4S’s API mimics the semantics of Numpy

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