Docker library sharing from host to container: Difference between revisions
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* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | * 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시 | ||
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* Non-goals: 다른 주제로의 확장 | * Non-goals: 다른 주제로의 확장 | ||
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* 테스트 환경에서 먼저 검증 | * 테스트 환경에서 먼저 검증 | ||
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* [https://wiki.hpcmate.com Docker library sharing from host to container] | * [https://wiki.hpcmate.com Docker library sharing from host to container] | ||
== Related Pages == | == Related Pages == | ||
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* [[Network]] | * [[Network]] | ||
[[Category:AI]] | [[Category:AI]] | ||
Latest revision as of 11:28, 17 July 2026
Overview
Docker library sharing from host to container에 대한 기술 문서입니다.
Summary
- 무엇인가? - Docker library sharing from host to container
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: Docker library sharing from host to container에 대한 기술 정보 제공
- Scope: Docker library sharing from host to container의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| Docker library sharing from host to container | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
From Docker and micro service point of view, the best practices of Docker are to package the library into the image, and not rely on a library file being present on the host. however, sometime sharing host library to container would be helpful to reduce redundancy and reduce size of Docker images. One of example would be CUDA tool kit, for example, install various version of CUDA toolkit on host and volume mouting when container start with read only. this method will save significant amount of storage space of docker image size and layers and easy to change version of CUDA for the application inside of container.
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related
→ Kubernetes → Dockerfile → Docker Compose → Docker → Container