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Apache SINGA: History, Applications & Products

Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.

Language: English [EN]
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Apache SINGA topic overview

The analysis highlights History, Applications and Products as prominent areas in the source structure around Apache SINGA.

Related topics
26
Source areas
6
Connected nodes
32
Extracted relationships
66
Concept neighborhoods
11
Bridge connections
32

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Applications · 8 topics
Apache SINGA and Social Good · 5 topics
Software Stack · 5 topics
History · 3 topics
Overview · 3 topics
SINGA-Auto · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Developer
Apache Software Foundation
License
Apache License 2.0
Operating system
Linux, macOS, Windows
Release
October 8, 2015; 10 years ago (2015-10-08)
Repository
github.com/apache/singa
Stable release
5.1.0 / April 2, 2026; 4 months ago (2026-04-02)

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Software Stack

SINGA-Auto

Applications

Apache SINGA and Social Good

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Apache SINGA connects Entity context

The extracted context around Apache SINGA shows recurring relationship patterns in the source. For example, Apache SINGA → Carnegie Technologies, CBRE, Citigroup, JurongHealth Hospital, National University, National University Hospital, NetEase, Noblis, Shentilium Technologies, Singapore, Singapore General Hospital, Tan Tock Seng Hospital, YZBigData Another extracted example is Apache SINGA → After, Australian Food, General Hospital, Health Promotion Board, JHFoodLg, JurongHealth Campus, JurongHealth Food Log, Nutrient Database, The, The Ng Teng Fong. Use these groups to spot repeated connection types before inspecting the individual relationships.

Apache SINGA

Top relations

has application · 13
Apache SINGA → Carnegie Technologies, CBRE, Citigroup, JurongHealth Hospital, National University, National University Hospital, NetEase, Noblis, Shentilium Technologies, Singapore, Singapore General Hospital, Tan Tock Seng Hospital, YZBigData
related to Apache SINGA and Social Good · 10
Apache SINGA → After, Australian Food, General Hospital, Health Promotion Board, JHFoodLg, JurongHealth Campus, JurongHealth Food Log, Nutrient Database, The, The Ng Teng Fong
related to SINGA-Auto · 9
Apache SINGA → As, EMR, For, In, It, Rafiki, SINGA-Auto, Users, VLDB2018
related to SINGA-Easy · 7
Apache SINGA → ACM Multimedia, ACM SIGMOD, Grad-CAM, It, LIME, SINGA-Easy, To
related to In-Database Model Selection · 4
Apache SINGA → PostgreSQL, Starting, The, This
Developer · 1
Apache SINGA → Apache Software Foundation
License · 1
Apache SINGA → Apache License 2.0
Operating system · 1
Apache SINGA → Linux, macOS, Windows
Release · 1
Apache SINGA → October 8, 2015; 10 years ago (2015-10-08)
Repository · 1
Apache SINGA → github.com/apache/singa

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

apache singa learning model data training machine provides distributed software models inference users deep selection service analytics singa-auto mlcask applications

Apache SINGA relationships Subject–Predicate–Object triples

TTTA extracted 66 structured relationships around Apache SINGA. Examples in this analysis include Apache SINGA → Developer → Apache Software Foundation and Apache SINGA → License → Apache License 2.0. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Apache SINGADeveloperApache Software Foundation1.00infobox
Apache SINGALicenseApache License 2.01.00infobox
Apache SINGAOperating systemLinux, macOS, Windows1.00infobox
Apache SINGAReleaseOctober 8, 2015; 10 years ago (2015-10-08)1.00infobox
Apache SINGARepositorygithub.com/apache/singa1.00infobox
Apache SINGAStable release5.1.0 / April 2, 2026; 4 months ago (2026-04-02)1.00infobox
Apache SINGAWebsitesinga.apache.org1.00infobox
Apache SINGAWritten inC++, Python1.00infobox
Apache SINGAis aApache top-level project for developing an open source machine learning library0.90text
structured datainstance ofwhich is a set of built-in machine-learning models for popular tasks0.80text
NetEaseinstance ofApplicationsApache SINGA is in use at organizations0.80text
Carnegie Technologiesinstance ofApplicationsApache SINGA is in use at organizations0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Apache SINGA bring nearby vocabulary together. In this analysis, examples include Singa, Learning and Application. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Apache SINGA
    • Singa
    • Learning
    • Application
    • Project
    • Software
    • Deep
    • Inference
    • Machine
    • Applications
    • Database
    • Development
    • In-database
  • apache singa
    • Singa
    • Application
    • Learning
    • Project
    • Inference
    • Software
    • Deep
    • Machine
    • Database
    • Singa-easy
    • Support
    • General
  • apache
    • Singa
    • Learning
    • Application
    • Project
    • Software
    • Deep
    • Inference
    • Machine
    • Applications
    • Database
    • Development
    • In-database
  • machine learning
    • Learning
    • Machine
    • Deep
    • Models
    • Management
    • Data
    • Singa
    • Distributed
    • Service
    • Inference
    • Training
    • Accuracy
  • apache singa and social good
    • Singa
    • Application
    • Learning
    • Project
    • Inference
    • Software
    • Deep
    • Machine
    • Database
    • Singa-easy
    • Support
    • General
  • applications
    • Software
    • Scalable
    • Database
    • Development
    • Hardware
    • In-database
    • Project
    • Singa-easy
    • Support
    • System
    • Singa
    • Analytics
  • singa-auto
    • System
    • Accuracy
    • Database
    • In-database
    • Singa-easy
    • Support
    • Tuning
    • Mlcask
    • Software
    • Selection
    • Service
    • Models
  • singapore general hospital
    • Models
    • Application
    • Collaborative
    • Management
    • Singa
    • Tuning
    • Learning
    • Service
    • Machine
    • Provides
    • Training
    • Model

Connections between topic areas Semantic bridges

For Apache SINGA, one of the stronger structural bridges in this analysis connects Apache SINGA with Applications. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Apache SINGA — Applications · splits 24 ⟂ 9
Apache SINGA — Software Stack · splits 27 ⟂ 6
Apache SINGA — Apache SINGA and Social Good · splits 27 ⟂ 6
Apache SINGA — Overview · splits 29 ⟂ 4
Apache SINGA — History · splits 29 ⟂ 4
Apache SINGA — SINGA-Auto · splits 30 ⟂ 3

Map overview Semantic statistics

Apache SINGA

Nodes33
Edges32
Triples66
Avg. degree1.94
Density0.060606
Components1

Source & methodology

TTTA analyzes the structure around Apache SINGA to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Apache SINGA · EN edition · Analysis: TopicsToTalkAbout

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