reinistihovs

#!/bin/bash

#To exclude a domain, please add to its name "nobackup"
#First shutdown the guest, then use this command: virsh domrename oldname newname.

DATE=`date +%Y-%m-%d.%H:%M:%S`
LOG=/var/log/kvm-backup.$DATE.LOG
BACKUPROOT=/backup

Vindaar

General remarks

I personally just implement the features when I need them, as I go (as far as I’m able anyways). I think with a few people pushing we could get most of the important functionality of numpy and scipy built on top of arraymancer in a reasonable time.

In principle using Python where needed (but still accelerating it with Nim!) is pretty straight forward nowadays thanks to the advances in nimpy. I’ll show an example below. One major thing for this is still missing in nimpy, which makes it slightly more complicated than needed.

hartzell

#!/bin/sh
set -e

# ask the spack executable the location of root of its tree
export SPACK_ROOT=$(spack location -r)

# having an app tree set up messes up the build.  Clear the field.
if [[ $(type -t module) == "function" ]]; then
    module purge
fi

VerosK

#!/bin/bash
# create custom bootable iso for CentOS 7 with kickstart

if [ $# -lt 2 ]
then
    echo "Usage1: $0 path2iso path2kickstart"
    exit 1
else
    if [ ! -f $1 ]
    then

stefansundin

# WARNING: Use this at your own risk. It will probably break your other packages and cause other havoc.
# These days you should just upgrade to Ubuntu 18.04.

$ ssh -V
OpenSSH_7.2p2 Ubuntu-4ubuntu2.1, OpenSSL 1.0.2g  1 Mar 2016

wget https://launchpadlibrarian.net/277739251/openssh-client_7.3p1-1_amd64.deb
wget https://launchpadlibrarian.net/298453050/libgssapi-krb5-2_1.14.3+dfsg-2ubuntu1_amd64.deb
wget https://launchpadlibrarian.net/298453058/libkrb5-3_1.14.3+dfsg-2ubuntu1_amd64.deb
wget https://launchpadlibrarian.net/298453060/libkrb5support0_1.14.3+dfsg-2ubuntu1_amd64.deb

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

mcfdn

Using multiple GitHub deploy keys on a single server with a single user

Within GitHub it is possible to set up two types of SSH key - account level SSH keys and and repository level SSH keys. These repository level SSH keys are known in GitHub as deploy keys.

Deploy keys are useful for deploying code because they do not rely on an individual user account, which is susceptible to change, to “store” the server keys.

There is, however, an ‘issue’ with using deploy keys; each key across all repositories on GitHub must be unique. No one key can be used more than once. This becomes a problem when deploying to repositories to the same server with the same user. If you create two keys, the SSH client will not know which key to use when connecting to GitHub.

One solution is to use an SSH config file to define which key to use in which situation. This isn’t as easy as it seems.. you might try something like this:

danielsotopino

# Hello

#include <ESP8266WiFi.h>

const char* ssid = ""; const char* password = ""; const int sensorPin = A0;

WiFiServer server(80);

maxivak

Setup Ubuntu 16.04 to be used with Vagrant and Virtualbox

Prepare Vagrant box with Ubuntu 16.04

We will use official box "ubuntu/xenial64" and modify it to work with Vagrant.

• Vagrantfile

drmalex07

README

Services declared as oneshot are expected to take some action and exit immediatelly (thus, they are not really services, no running processes remain). A common pattern for these type of service is to be defined by a setup and a teardown action.

Let's create a example foo service that when started creates a file, and when stopped it deletes it.

Define setup/teardown actions

Create executable file /opt/foo/setup-foo.sh: