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ease
features
design
support

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Description

Apache Yetus comprises a suite of libraries and tools designed to facilitate the contribution and release workflows for software projects. It offers a comprehensive framework for automatically validating new contributions against a range of standards recognized by the community, alongside features for documenting a clearly defined supported interface for downstream projects. Additionally, it equips release managers with tools to create release documentation based on data sourced from community issue trackers and source code repositories. Predominantly, the software is developed using shell and various scripting languages, with the project's name derived from a term linked to the Cymbium genus of gastropods, paying homage to shell code. The Yetus Precommit build, patch, and continuous integration suite empowers projects to formalize their criteria for patch acceptance and assess incoming contributions before they reach the review stage by a committer. Furthermore, the Audience Annotations feature enables developers to utilize Java Annotations to indicate which segments of their Java library are intended for public consumption, enhancing clarity for users. This combination of tools and features makes Yetus an invaluable resource for software development communities looking to streamline their processes.

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

Screenshots View All

Screenshots View All

Integrations

Apache Spark
Azure Pipelines
GitHub
GitLab
Hadoop
Jenkins
Jira
Semaphore
Travis CI
iTop VPN

Integrations

Apache Spark
Azure Pipelines
GitHub
GitLab
Hadoop
Jenkins
Jira
Semaphore
Travis CI
iTop VPN

Pricing Details

No price information available.
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

Apache Software Foundation

Founded

1999

Country

United States

Website

yetus.apache.org

Vendor Details

Company Name

Deeplearning4j

Founded

2019

Country

Japan

Website

deeplearning4j.org

Product Features

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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