Optimize TensorFlow: 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 | ||
== Purpose == | == Purpose == | ||
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* Non-goals: 다른 주제로의 확장 | * Non-goals: 다른 주제로의 확장 | ||
== Key Concepts == | == Key Concepts == | ||
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== Detailed Explanation == | == Detailed Explanation == | ||
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== Best Practices == | == Best Practices == | ||
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* 테스트 환경에서 먼저 검증 | * 테스트 환경에서 먼저 검증 | ||
== References == | == References == | ||
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* [https://wiki.hpcmate.com Optimize TensorFlow] | * [https://wiki.hpcmate.com Optimize TensorFlow] | ||
== Related Pages == | == Related Pages == | ||
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* [[Network]] | * [[Network]] | ||
[[Category:Server]] | [[Category:Server]] | ||
== Knowledge Graph == | |||
Related | |||
→ [[Linux]] | |||
→ [[Server]] | |||
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Latest revision as of 11:30, 17 July 2026
Overview
Optimize TensorFlow에 대한 기술 문서입니다.
Summary
- 무엇인가? - Optimize TensorFlow
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: Optimize TensorFlow에 대한 기술 정보 제공
- Scope: Optimize TensorFlow의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| Optimize TensorFlow | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
Optimize TensorFlow to CPU features by turning on all the computation optimization opportunities provided by the CPU.
Do you wondering how much of a difference those instructions end up making on your machine between genera pip packages and custom optimization on the system?
Because pre-built pip packages do not enable all machine capable optimization flags in it or may not perfectly set for your machine, for example, GCC compiler optimization flgags like
gcc -O<number> (O the letter, not the number).
- -O0: Turns off optimization entirely. Fast compile times, good for debugging. This is the default if you don't specify any which you probably don't want.
- -O1: Basic optimization level.
- -O2: Recommended for most things. SSE / AVX may be used, but not fully.
- -O3: Highest optimization possible. Also vectorizes loops, can use all AVX registers.
- -Os: Small size. Basically enables -O2 options which do not increase size. Can be useful for machines that have limited storage and/or CPUs with small cache sizes.
If you are building TensorFlow on the same machine that will be running it you can just use -O3 -march=native. If you are building on a different machine, you can use the command below to see which flags GCC would set and then pass them when configuring TF.
# This is the output on my machine: $ gcc -march=native -E -v - </dev/null 2>&1 | grep cc1 /usr/libexec/gcc/x86_64-pc-linux-gnu/7.3.0/cc1 -E -quiet -v - -march=znver1 -mmmx -mno-3dnow -msse -msse2 -msse3 -mssse3 -msse4a -mcx16 -msahf -mmovbe -maes -msha -mpclmul -mpopcnt -mabm -mno-lwp -mfma -mno-fma4 -mno-xop -mbmi -mno-sgx -mbmi2 -mno-tbm -mavx -mavx2 -msse4.2 -msse4.1 -mlzcnt -mno-rtm -mno-hle -mrdrnd -mf16c -mfsgsbase -mrdseed -mprfchw -madx -mfxsr -mxsave -mxsaveopt -mno-avx512f -mno-avx512er -mno-avx512cd -mno-avx512pf -mno-prefetchwt1 -mclflushopt -mxsavec -mxsaves -mno-avx512dq -mno-avx512bw -mno-avx512vl -mno-avx512ifma -mno-avx512vbmi -mno-avx5124fmaps
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related