Apache Spark vs. Deeplearning4j vs. JavaScript vs. OCaml Comparison Chart

DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers.

OCaml is a general-purpose, industrial-strength programming language with an emphasis on expressiveness and safety. OCaml’s powerful type system means more bugs are caught at compile time, and large, complex codebases are easier to maintain. This makes it a good language for running critical code. At the same time, sophisticated inference makes the type system unobtrusive, creating a smooth developer experience. One is a bytecode compiler which generates small, portable executables and is very fast. The other is a native code compiler that produces more efficient machine code; its performance matches the highest standards of modern compilers. OCaml has great support for the most popular editors. VS Code is recommended for beginners, and for power users there is deep integration with Vim and Emacs. OCaml has a rich and dynamic community and best-in-class tooling.