Azure Databricks vs. Deeplearning4j vs. JavaScript vs. Koyeb 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.

Push code to production, everywhere, in minutes with Koyeb. Accelerate backend apps at the edge with high-performance hardware. Connect your GitHub account to Koyeb, choose a repository to deploy, and leave us the infrastructure. We build, deploy, run, and scale your application with zero configuration. Simply git push, and we build and deploy your app with blazing fast built-in continuous deployment. Develop fearlessly with native versioning of all deployments. Build Docker containers, host them on any registry, and atomically deploy your new version worldwide in a single API call. Invite your team to build together and enjoy a live preview after each push with built-in CI/CD. The Koyeb platform lets you combine the languages, frameworks, and technologies you use. Deploy any application without modifications thanks to native support of popular languages and Docker containers. Koyeb detects and builds apps in Node.js, Python, Go, Ruby, Java, PHP, Scala, Clojure, and more.