Theun Kohlbeck theunkohlbeck

SSH Slackbot

This is a simple slackbot to post successful SSH logins to a slack channel to help you keep track of server access.

Step 1

Create an incoming webhook for your slack community.

Step 2

Create /etc/ssh/sshslack.sh

This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:

lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)

A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly. In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers. This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.

Howto

Install these programms

sudo apt-get install gnome-raw-thumbnailer ufraw-batch

Try now if everything works, and your thumbnails show up. If not, try the following part.

	#!/bin/bash -ex

	CURL_COMMAND='curl -X POST https://api.github.com/graphql'
	RAW_OUTPUT_FILE="$(tempfile -s ".query.output")"
	MINIMUM_PLUS_1_COUNT=20

	function API_call() {
	if [ ! -v NEXT_PAGE ]; then
	api_output=$($CURL_COMMAND -H "Authorization:bearer $GITHUB_TOKEN" -d @query)
	else

	# Calculate the latest and earliest offsets, and provide the total # of messages in the topic (the difference):

	#!/bin/bash
	if [[ $# -eq 0 ]] ; then
	echo 'provide the name of a topic to get totals against'
	exit 0
	fi
	latest=`bin/kafka-run-class.sh kafka.tools.GetOffsetShell --broker-list localhost:9092 --topic $1 --time -1 --offsets 1 \| awk -F ":" '{sum += $3} END {print sum}'`
	earliest=`bin/kafka-run-class.sh kafka.tools.GetOffsetShell --broker-list localhost:9092 --topic $1 --time -2 --offsets 1 \| awk -F ":" '{sum2 += $3} END {print sum2}'`
	total=`expr $latest - $earliest`

	import os, argparse
	import tensorflow as tf
	from tensorflow.python.framework import graph_util

	dir = os.path.dirname(os.path.realpath(__file__))


	def freeze_graph(model_folder):
	# We retrieve our checkpoint fullpath
	checkpoint = tf.train.get_checkpoint_state(model_folder)

	#!/bin/bash

	function pause(){
	read -p "$*"
	}
	RED='\033[0;31m'
	NC='\033[0m' # NOCOLOR
	YELLOW='\033[0;33m'

	function HexToDotted (){

	Step by step how to pull a private DockerHub hosted image in a Kubernetes YML.

	export DOCKER_REGISTRY_SERVER=https://index.docker.io/v1/
	export DOCKER_USER=Type your dockerhub username, same as when you `docker login`
	export DOCKER_EMAIL=Type your dockerhub email, same as when you `docker login`
	export DOCKER_PASSWORD=Type your dockerhub pw, same as when you `docker login`

	kubectl create secret docker-registry myregistrykey \
	--docker-server=$DOCKER_REGISTRY_SERVER \
	--docker-username=$DOCKER_USER \

	#!/opt/datadog-agent/embedded/bin/python

	import os
	import sys
	import time
	import re
	import subprocess
	import logging

	# To keep this lightweight we assume the following

	:80 {
	root /serve
	}