• http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
  • AWS에 CUDA 6.5 설치

환경변수로 CUDA device 설정

# CUDA 사용금지
os.environ['CUDA_VISIBLE_DEVICES'] = ""
# 또는 
os.environ['CUDA_VISIBLE_DEVICES'] = "-1"
 
# 첫 번째 디바이스만 보임
os.environ['CUDA_VISIBLE_DEVICES'] = "0"

• https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/
• https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/

nvidia 디바이스 정보 보여주는 도구 windows에서는 “C:\Program Files\NVIDIA Corporation\NVSMI”에 있음

nvidia-smi -l  # 주기적으로 디바이스 정보를 출력
nvidia-smi -lms T  # T ms 마다 디바이스 정보를 출력
nvidia-smi dmon  # 실시간으로 gpu, 메모리 사용, 온도 출력

드라이버 삭제

nvidia-installer --uninstall

최신 버전 드라이버 설치

nvidia-installer --update

• http://www.linuxquestions.org/questions/slackware-14/cannot-reinstall-nvidia-driver-module-being-loaded-wtf-296828/

sudo nvidia-xconfig
sudo nvidia-xconfig --cool-bits=4

# command line에서 조절
## 읽기
nvidia-settings -q [gpu:0]/GPUCoreTemp -c :0.0
nvidia-settings -q [fan:0]/GPUCurrentFanSpeed -c :0.0
## 쓰기
nvidia-settings -a [gpu:0]/GPUFanControlState=1 -a [fan:0]/GPUTargetFanSpeed=75 -c :0.0

## 멀티 GPU
nvidia-settings -a "[gpu:0]/GPUFanControlState=1" -a "[fan:0]/GPUCurrentFanSpeed=n" \
              -a "[gpu:1]/GPUFanControlState=1" -a  [fan:1]/GPUCurrentFanSpeed=n" &

• http://askubuntu.com/questions/42494/how-can-i-change-the-nvidia-gpu-fan-speed
• https://bbs.archlinux.org/viewtopic.php?id=196716
• http://blog.cryptohaze.com/2011/02/scripts-to-control-nvidia-fan-speeds.html
• https://github.com/MisterPup/Nvidia-Dynamic-Fan-Control
• https://wiki.archlinux.org/index.php/NVIDIA/Tips_and_tricks

• http://tleyden.github.io/blog/2014/10/25/docker-on-aws-gpu-ubuntu-14-dot-04-slash-cuda-6-dot-5/

• https://github.com/NVIDIA/nvidia-docker

• https://sites.google.com/site/akohlmey/random-hacks/nvidia-gpu-coolness
• https://github.com/boris-dimitrov/set_gpu_fans_public
• https://gist.github.com/clarkwinkelmann/13af7a328fb20b9a7d78
• http://thomascomputerrepair.com/nvidia-settings-kde.php

• https://medium.com/mlreview/using-your-idle-deep-learning-hardware-for-mining-c1b9887491fa

# sudo apt mesa-utils
export DISPLAY=:1 & Xvfb :1 -screen 0 1024x768x16 & glxinfo

$ xvfb-run -s "-screen 0 1400x900x24" 명령어

xvfb-run -s “-screen 0 1400x900x24” glxinfo 가 정상적으로 작동하지 않을 때,

https://davidsanwald.github.io/2016/11/13/building-tensorflow-with-gpu-support.html

IMPORTANT: Be sure to use the –no-opengl-libs option 부분 참조

$ chmod +x cuda_8.0.44_linux-run
$ sudo sh cuda_8.0.44_linux-run --extract=~/Downloads/
$ sudo sh cuda_8.0.44_linux-run --override --no-opengl-libs

https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07#install-dependencies

• https://towardsdatascience.com/python-performance-and-gpus-1be860ffd58d?ncid=so-twi-n2-96487&linkId=100000006881312

• cuDF: GPU DataFrames
  • https://github.com/rapidsai/cudf
• cuML: scikit-learn과 유사한 ML 라이브러리
  • https://github.com/rapidsai/cuml
• cuGraph: 그래프 라이브러리
  • https://github.com/rapidsai/cugraph
• Rapids: https://rapids.ai/start.html