nanmi

apt-get install libomp-dev

//omp.cc
#include <iostream>
#include <omp.h>
int main()
{

nanmi

ROS系统安装

安装依赖

$ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ sudo sh -c 'echo "deb http://mirrors.ustc.edu.cn/ros/ubuntu/ $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ sudo apt-key adv --keyserver hkp://ha.pool.sks-keyservers.net:80 --recv-key 0xB01FA116
$ sudo apt-get update
$ sudo apt-get install ros-kinetic-desktop-full

nanmi

//CUDA C++
//1D grid of 1D blocks
__device__ int getGlobalIdx_1D_1D()
{
    return blockIdx.x*blockDim.x + threadIdx.x;
}

//1D grid of 2D blocks
__device__ int getGlobalIdx_1D_2D()
{

nanmi

User Manual

Index

• Config
• Scheduling GPUs

Config

Config QuickStart

1. Config skuTypes

nanmi

docker 19.03已经正式发布了，这次发布对我来说有两大亮点。

• 就是docker不需要root权限来启动和运行了
• 就是支持GPU的增强功能，我们在docker里面想读取nvidia显卡再也不需要额外的安装nvidia-docker了

安装NVIDIA Container Runtime

cat nvidia-container-runtime-script.sh

curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
  sudo apt-key add -

nanmi

pybind11编译和使用

download pybind11 project

$ git clone https://github.com/pybind/pybind11.git

install pytest

$ pip install pytest

nanmi

#启动容器
docker run -it --rm --name mysql -e MYSQL_ROOT_PASSWORD=123456 -p 3306:3306 -d mysql

#进入容器
apt-get update
passwd #设置密码
apt-get install ssh openssh-server vim
#修改/etc/ssh/sshd_config     修改为PermitRootLogin yes和修改为UsePAM no

$ mysql -uroot -p123456 或 (mysql -uroot -p )

nanmi

自定义logo

$ vim /etc/ssh/sshd_config
#文档末尾加入
Banner /etc/ssh/my_banner

创建

nanmi

CentOS 安装nvidia-docker2 2.1设置仓库

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo

2.2更新仓库中的key

$ DIST=$(sed -n 's/releasever=//p' /etc/yum.conf)

nanmi

summary_writer = tf.summary.FileWriter('./logs', sess.graph)

train_summary = tf.Summary(value=[tf.Summary.Value(tag='train_loss', simple_value=train_loss),
                                  tf.Summary.Value(tag='train_accuracy', simple_value=train_acc)
                                  ])

test_acc, test_loss, test_summary = Evaluate(sess) 

summary_writer.add_summary(summary=train_summary, global_step=epoch)
summary_writer.add_summary(summary=test_summary, global_step=epoch)