Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Deep Learning Software
• Distributed CPUs and GPUs
• Java, Scala and Python APIs
• Adapted for micro-service architecture
• Parallel training via iterative reduce
• Scalable on Hadoop
• GPU support for scaling on AWS
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Skymind is its commercial support arm. Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. DL4J is customizable at scale. Released under the Apache 2.0 license, all derivatives of DL4J belong to their authors. DL4J can import neural net models from most major frameworks via Keras, including TensorFlow, Caffe, Torch and Theano, bridging the gap between the Python ecosystem and the JVM with a cross-team toolkit for data scientists, data engineers and DevOps. Keras is employed as Deeplearning4j's Python API. In a nutshell, Deeplearning4j lets users compose deep neural nets from various shallow nets, each of which form a so-called `layer`. This flexibility lets users combine restricted Boltzmann machines, other autoencoders, convolutional nets or recurrent nets as needed in a distributed, production-grade framework that works with Spark and Hadoop on top of distributed CPUs or GPUs.
There are a lot of parameters to adjust when users are training a deep-learning network. Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure and Kotlin programmers. DL4J takes advantage of the latest distributed computing frameworks including Hadoop and Apache Spark 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 Skymind 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.