MLPerf
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Overview
MLPerf에 대한 기술 문서입니다.
Summary
- 무엇인가? - MLPerf
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: MLPerf에 대한 기술 정보 제공
- Scope: MLPerf의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| MLPerf | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
MLPerf is a consortium of key contributors from the AI/ML (Artificial Intelligence and Machine Learning) community with its 50+ founding Members and Affiliates, including startups, leading companies, academics, and non-profits from around the globe that provides unbiased AI/ML performance evaluations of hardware, software, and services.[1] The latest MLPerf Inference 3.0 trends shows the latest MLPerf result and trends at the time of createing this document.
| Categories | Description | Official Result |
|---|---|---|
| MLPerf Training v2.1 | The seventh instantiation for training and consists of eight different workloads covering a broad diversity of use cases, including vision, language, recommenders, and reinforcement learning | https://mlcommons.org/en/training-normal-21/ |
| MLPerf Inference v3.0 | The seventh instantiation for inference and tested seven different use cases across seven different kinds of neural networks. Three of these use cases were for computer vision, one was for recommender systems, two were for language processing, and one was for medical imaging. | https://mlcommons.org/en/inference-edge-30/ |
| MLPerf HPC v2.0 | The third iteration for HPC and tested three different scientific computing use cases, including climate atmospheric river identification, cosmology parameter prediction, and quantum molecular modeling. | https://mlcommons.org/en/training-hpc-20/ |
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related