Optimize TensorFlow: Difference between revisions
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== | {{Status | ||
|status=Draft | |||
|owner=Knowledge Agent | |||
|last_update=2026-07-16 | |||
|review=Pending | |||
}} | |||
{{TOC}} | |||
== 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 == | |||
{| class="wikitable" | |||
! 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. | 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? | 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 [[CPU features|optimization flags]] in it or may not perfectly set for your machine, for example, GCC compiler optimization flgags like | Because pre-built pip packages do not enable all machine capable [[CPU features|optimization flags]] in it or may not perfectly set for your machine, for example, GCC compiler optimization flgags like | ||
<code>gcc -O<number></code> (O the letter, not the number). | <code>gcc -O<number></code> (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. | * -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. | * -O1: Basic optimization level. | ||
| Line 14: | Line 52: | ||
* -O3: Highest optimization possible. Also vectorizes loops, can use all AVX registers. | * -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. | * -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 <code>-O3 -march=native</code>. 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. | If you are building TensorFlow on the same machine that will be running it you can just use <code>-O3 -march=native</code>. 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: | # This is the output on my machine: | ||
$ gcc -march=native -E -v - </dev/null 2>&1 | grep cc1 | $ gcc -march=native -E -v - </dev/null 2>&1 | grep cc1 | ||
| Line 27: | Line 63: | ||
-mno-prefetchwt1 -mclflushopt -mxsavec -mxsaves -mno-avx512dq -mno-avx512bw | -mno-prefetchwt1 -mclflushopt -mxsavec -mxsaves -mno-avx512dq -mno-avx512bw | ||
-mno-avx512vl -mno-avx512ifma -mno-avx512vbmi -mno-avx5124fmaps | -mno-avx512vl -mno-avx512ifma -mno-avx512vbmi -mno-avx5124fmaps | ||
== Best Practices == | |||
* 최신 버전 사용 권장 | |||
* 공식 문서 참고 | |||
* 테스트 환경에서 먼저 검증 | |||
== References == | == References == | ||
* [https://wiki.hpcmate.com Optimize TensorFlow] | |||
== Related Pages == | |||
* [[Linux]] | |||
* [[Server]] | |||
* [[Hardware]] | |||
* [[Network]] | |||
[[Category:Server]] | |||
== Knowledge Graph == | |||
Related | |||
→ [[Linux]] | |||
→ [[Server]] | |||
→ [[Hardware]] | |||
→ [[Network]] | |||
[[Category:Reference]] | |||
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