Deeplearning4j vs. Keras Comparison


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About 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.	About Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API.
Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Not Supported iPhone Supported iPad Supported Android Supported Chromebook Not Supported
Audience Researchers, developers and professionals requiring an open-source, distributed, deep learning library for the JVM	Audience Developers interested in an deep learning API solution to minimize the number of user actions required for common use cases
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Not Supported Free Trial Not Supported	Pricing No information available. Free Version Not Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 5.0 / 5 Read all reviews
	Pros & Cons from Real Users Pros I mostly code in Python, so using Keras for my deep learning needs wasn't too hard to get used to, given the abundance of documentation and ease of writing modular code with its API. Cons Keras only has high level APIs, unlike Tensorflow, which has both high and low level support.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information Deeplearning4j Founded: 2019 Japan deeplearning4j.org	Company Information Keras United States keras.io
Alternatives MXNet The Apache Software Foundation	Alternatives TensorFlow
Fabric for Deep Learning (FfDL) IBM	PyTorch
Apache Mahout Apache Software Foundation	AutoKeras
MLlib Apache Software Foundation	AWS Deep Learning AMIs Amazon
Apache Spark Apache Software Foundation View All	Deeplearning4j View All
Categories Deep Learning Supported Neural Network Supported	Categories Deep Learning Supported Neural Network Supported
	Deep Learning Features Convolutional Neural Networks Supported Document Classification Not Supported Image Segmentation Not Supported ML Algorithm Library Not Supported Model Training Not Supported Neural Network Modeling Not Supported Self-Learning Not Supported Visualization Supported
Integrations Comet Not Supported Comet LLM Not Supported Databricks Not Supported Dataiku Not Supported Flower Not Supported Graphcore Not Supported Hadoop Supported Horovod Not Supported Jovian Not Supported MLflow Not Supported ModelOp Not Supported OpenVINO Not Supported PaliGemma 2 Not Supported RazorThink Not Supported RunCode Not Supported StreamFlux Not Supported Superwise Not Supported Thunder Compute Not Supported Vectice Not Supported Zorro Not Supported Show More Integrations View All 2 Integrations	Integrations Comet Supported Comet LLM Supported Databricks Supported Dataiku Supported Flower Supported Graphcore Supported Hadoop Not Supported Horovod Supported Jovian Supported MLflow Supported ModelOp Supported OpenVINO Supported PaliGemma 2 Supported RazorThink Supported RunCode Supported StreamFlux Supported Superwise Supported Thunder Compute Supported Vectice Supported Zorro Supported Show More Integrations View All 51 Integrations
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