Apache Mahout vs. Deeplearning4j vs. Java vs. Riemann 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.

Riemann aggregates events from your servers and applications with a powerful stream processing language. Send an email for every exception in your app. Track the latency distribution of your web app. See the top processes on any host, by memory and CPU. Combine statistics from every Riak node in your cluster and forward them to Graphite. Track user activity from second to second. Riemann provides a low-latency, transient shared state for systems with many moving parts. Riemann streams are just functions that accept an event. Since Riemann's configuration is a Clojure program, its syntax is concise, regular, and extendable. Configuration-as-code minimizes boilerplate and gives you the flexibility to adapt to complex situations. Riemann can tell you as much or as little as you want. Throttle or roll up multiple events into a single message. Get emails about exceptions in your code, provider downtime, or latency spikes. You can also integrate with PagerDuty for SMS or phone alerts.