NVIDIA GPU Cloud (NGC): Difference between revisions
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== Overview == | == Overview == | ||
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* 왜 필요한가? - HPC 및 서버 환경에서 필수 개념 | * 왜 필요한가? - HPC 및 서버 환경에서 필수 개념 | ||
* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | * 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | ||
== Purpose == | == Purpose == | ||
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* Scope: NVIDIA GPU Cloud (NGC)의 개념, 사용법, 설정 | * Scope: NVIDIA GPU Cloud (NGC)의 개념, 사용법, 설정 | ||
* Non-goals: 다른 주제로의 확장 | * Non-goals: 다른 주제로의 확장 | ||
== Key Concepts == | == Key Concepts == | ||
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| [[Linux]], [[Server]] | | [[Linux]], [[Server]] | ||
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== Detailed Explanation == | == Detailed Explanation == | ||
* [[NVIDIA GPUs]] | * [[NVIDIA GPUs]] | ||
In brief - A simple but powerful interface provide by NVIDIA for the desired AI framework such as TensorFlow, Caffe2, Theano, MXNet, Microsoft Cognitive Toolkit, PyTorch, etc. automatically based on open source [[Docker]] technology. | In brief - A simple but powerful interface provide by NVIDIA for the desired AI framework such as TensorFlow, Caffe2, Theano, MXNet, Microsoft Cognitive Toolkit, [[PyTorch]], etc. automatically based on open source [[Docker]] technology. | ||
[[file:Ngc.png | NVIDIA GPU Cloud | center]] | [[file:Ngc.png | NVIDIA GPU Cloud | center]] | ||
NGC is something like a NVIDIA Deep Learning Portal site provided by NVIDIA to reduce hardware and software installation, configuration, [[optimization]], deployment, version reconciliation and systems administrative work. '''to help data scientists to focus their time and effort on machine learning and algorithm''' | NGC is something like a NVIDIA Deep Learning Portal site provided by NVIDIA to reduce hardware and software installation, configuration, [[optimization]], deployment, version reconciliation and systems administrative work. '''to help data scientists to focus their time and effort on machine learning and algorithm''' | ||
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* <nowiki>https://ngc.nvidia.com/CATALOG/LANDING</nowiki> | * <nowiki>https://ngc.nvidia.com/CATALOG/LANDING</nowiki> | ||
[[category : article]] | [[category : article]] | ||
== Best Practices == | == Best Practices == | ||
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* 공식 문서 참고 | * 공식 문서 참고 | ||
* 테스트 환경에서 먼저 검증 | * 테스트 환경에서 먼저 검증 | ||
== References == | == References == | ||
* [https://wiki.hpcmate.com NVIDIA GPU Cloud (NGC)] | * [https://wiki.hpcmate.com NVIDIA GPU Cloud (NGC)] | ||
== Related Pages == | == Related Pages == | ||
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* [[Hardware]] | * [[Hardware]] | ||
* [[Network]] | * [[Network]] | ||
[[Category:GPU]] | [[Category:GPU]] | ||
Latest revision as of 13:25, 2 October 2026
Overview
NVIDIA GPU Cloud (NGC)에 대한 기술 문서입니다.
Summary
- 무엇인가? - NVIDIA GPU Cloud (NGC)
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: NVIDIA GPU Cloud (NGC)에 대한 기술 정보 제공
- Scope: NVIDIA GPU Cloud (NGC)의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| NVIDIA GPU Cloud (NGC) | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
In brief - A simple but powerful interface provide by NVIDIA for the desired AI framework such as TensorFlow, Caffe2, Theano, MXNet, Microsoft Cognitive Toolkit, PyTorch, etc. automatically based on open source Docker technology.
NGC is something like a NVIDIA Deep Learning Portal site provided by NVIDIA to reduce hardware and software installation, configuration, optimization, deployment, version reconciliation and systems administrative work. to help data scientists to focus their time and effort on machine learning and algorithm NGC by NVIDIA provides the latest tested versions of AI software stack and development frameworks and then will deploy these software containers on user’s hardware infrastructure by leveraging Open source Docker. Basically NVIDIA manages a cloud registry and repository of the latest 3 versions of tested applications, optimized libraries and frameworks, which are continually evolving through the open source community
- https://ngc.nvidia.com/CATALOG/LANDING
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related
→ CUDA → GPU → NVIDIA GPU → H100 → NVLink → AMD GPUs → A100 → MIG