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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
A hybrid front-end efficiently switches between Gluon eager imperative mode and symbolic mode, offering both adaptability and speed. The framework supports scalable distributed training and enhances performance optimization for both research and real-world applications through its dual parameter server and Horovod integration. It features deep compatibility with Python and extends support to languages such as Scala, Julia, Clojure, Java, C++, R, and Perl. A rich ecosystem of tools and libraries bolsters MXNet, facilitating a variety of use-cases, including computer vision, natural language processing, time series analysis, and much more. Apache MXNet is currently in the incubation phase at The Apache Software Foundation (ASF), backed by the Apache Incubator. This incubation stage is mandatory for all newly accepted projects until they receive further evaluation to ensure that their infrastructure, communication practices, and decision-making processes align with those of other successful ASF initiatives. By engaging with the MXNet scientific community, individuals can actively contribute, gain knowledge, and find solutions to their inquiries. This collaborative environment fosters innovation and growth, making it an exciting time to be involved with MXNet.
API Access
Has API
API Access
Has API
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon EC2 P4 Instances
Amazon SageMaker Debugger
Azure Pipelines
Cameralyze
Flower
GPUonCLOUD
GitHub
GitLab
Integrations
AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon EC2 P4 Instances
Amazon SageMaker Debugger
Azure Pipelines
Cameralyze
Flower
GPUonCLOUD
GitHub
GitLab
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
The Apache Software Foundation
Founded
1999
Country
United States
Website
mxnet.apache.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
