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Description
An open-source, comprehensive microservice framework offers high performance right out of the box, ensuring compatibility with widely used ecosystems and supporting multiple programming languages. It guarantees service contracts via OpenAPI and features one-click scaffolding to expedite the development of microservice applications. This solution enables the ecological extension for various programming languages, including Java, Golang, PHP, and NodeJS. Apache ServiceComb serves as a robust open-source microservices framework, comprising several components that can be tailored to diverse scenarios through strategic combinations. This guide is designed to help newcomers swiftly get acquainted with Apache ServiceComb, making it an ideal starting point for beginners. Additionally, the framework allows for a separation between programming and communication models, enabling developers to integrate any desired communication model as needed. Consequently, application developers can prioritize API development while effortlessly adapting their communication strategies during deployment. With this flexibility, the framework enhances productivity and streamlines the microservice application lifecycle.
Description
DL4J leverages state-of-the-art distributed computing frameworks like Apache Spark and Hadoop to enhance the speed of training processes. When utilized with multiple GPUs, its performance matches that of Caffe. Fully open-source under the Apache 2.0 license, the libraries are actively maintained by both the developer community and the Konduit team. Deeplearning4j, which is developed in Java, is compatible with any language that runs on the JVM, including Scala, Clojure, and Kotlin. The core computations are executed using C, C++, and CUDA, while Keras is designated as the Python API. Eclipse Deeplearning4j stands out as the pioneering commercial-grade, open-source, distributed deep-learning library tailored for Java and Scala applications. By integrating with Hadoop and Apache Spark, DL4J effectively introduces artificial intelligence capabilities to business settings, enabling operations on distributed CPUs and GPUs. Training a deep-learning network involves tuning numerous parameters, and we have made efforts to clarify these settings, allowing Deeplearning4j to function as a versatile DIY resource for developers using Java, Scala, Clojure, and Kotlin. With its robust framework, DL4J not only simplifies the deep learning process but also fosters innovation in machine learning across various industries.
API Access
Has API
API Access
Has API
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
ServiceComb
Country
United States
Website
servicecomb.apache.org
Vendor Details
Company Name
Deeplearning4j
Founded
2019
Country
Japan
Website
deeplearning4j.org
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization