跳到主要內容

nvidia-docker2


https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites



https://www.nvidia.com.tw/object/docker-container-tw.html

Prerequisites

The list of prerequisites for running nvidia-docker 2.0 is described below.
For information on how to install Docker for your Linux distribution, please refer to the Docker documentation.
  1. GNU/Linux x86_64 with kernel version > 3.10
  2. Docker >= 1.12
  3. NVIDIA GPU with Architecture > Fermi (2.1)
  4. NVIDIA drivers ~= 361.93 (untested on older versions)
Your driver version might limit your CUDA capabilities (see CUDA requirements)

Removing nvidia-docker 1.0

Version 1.0 of the nvidia-docker package must be cleanly removed before continuing.
You must stop and remove all containers started with nvidia-docker 1.0.

Ubuntu distributions

docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge nvidia-docker
 

Installing version 2.0

 

sudo apt-get install nvidia-docker2
sudo pkill -SIGHUP dockerd
 

Basic usage

nvidia-docker registers a new container runtime to the Docker daemon.
You must select the nvidia runtime when using docker run:
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
 

 

留言

這個網誌中的熱門文章

A3C in ATARI Pong-V0

ATARI PONG 對戰模式,左邊為遊戲程式,右邊為訓練中的A3C模型。一局以21分決勝負,對手MISS 一球得一分。從LOG可以看出,A3C模型從最初全敗的輸21分,經過2小時左右的TRAINING,已經逆轉至幾乎每局都勝利,偶爾甚至勝出高達13分。 底下為TRAINING A3C MODEL過程的LOG, (base) frank@viper1:~/a3c$ python main.py --env-name "Pong-v0" --num-processes 8 Time 00h 00m 10s, episode reward -21.0, episode length 1026 Time 00h 01m 18s, episode reward -21.0, episode length 1020 Time 00h 02m 26s, episode reward -21.0, episode length 1029 Time 00h 03m 35s, episode reward -21.0, episode length 1023 Time 00h 04m 42s, episode reward -21.0, episode length 1014 Time 00h 05m 50s, episode reward -21.0, episode length 1087 Time 00h 07m 00s, episode reward -21.0, episode length 1359 Time 00h 08m 14s, episode reward -16.0, episode length 1922 Time 00h 09m 30s, episode reward -14.0, episode length 2220 Time 00h 10m 48s, episode reward -15.0, episode length 2431 Time 00h 12m 04s, episode reward -15.0, episode length 2211 Time 00h 13m 23s, episode reward -8.0, episode length 2600 Time 00h 14m 38s, episod...

DDPG in Torcs within Docker Container

Dockerfile: FROM tensorflow/tensorflow:0.10.0-gpu WORKDIR /home/frank/old_gym_torcs ADD . /home/old_gym_torcs  RUN apt update RUN apt install -y vim xautomation torcs RUN apt-get install -y libjpeg-dev cmake swig python-pyglet python3-opengl libboost-all-dev \         libsdl2-2.0.0 libsdl2-dev libglu1-mesa libglu1-mesa-dev libgles2-mesa-dev \         freeglut3 xvfb libav-tools RUN pip install gym RUN pip install keras==1.1.0 ENV PATH="/usr/games:${PATH}" CMD ["/bin/bash"] viper1 $ docker run --runtime=nvidia -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /home/frank/old_gym_torcs:/home/old_gym_torcs -v /var/run/docker.sock:/var/run/docker.sock -v /home/frank/gym_torcs:/home/gym_torcs -v /home/frank/gym:/home/gym -p 3101:3101 --workdir /home/old_gym_torcs -p 8888:8888 ddpgfrk:tf0.10 /bin/bash (grey part is not necessary) After  $ docker commit id ddpg:tf0.10.0-gpu viper1 $ docker r...

Loss function

why cross entroy is more suitable for categorical classcification? ref: https://www.youtube.com/watch?v=Li5sVEXTIJw