alincc

sudo apt update

sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

alincc

#!/bin/sh
OVPNCONF="/root/ovpn/client.ovpn"
RUNNING=$(ps aux | grep -o "openvpn \-\-config" | wc -l)

case "$1" in
  start)
                if [ $RUNNING -eq 0 ]; then
                        echo -n > /var/log/ovpn_autostart.log
                        nohup openvpn --config "$OVPNCONF" > /var/log/ovpn_autostart.log 2>&1 &
                else

alincc

#!/bin/sh
OVPNCONF="/root/ovpn/client.ovpn"
RUNNING=$(ps aux | grep -o "openvpn \-\-config" | wc -l)

case "$1" in
  start)
                if [ $RUNNING -eq 0 ]; then
                        echo -n > /var/log/ovpn_autostart.log
                        nohup openvpn --config "$OVPNCONF" > /var/log/ovpn_autostart.log 2>&1 &
                else

alincc

# Redis Cheatsheet
# All the commands you need to know


redis-server /path/redis.conf  # start redis with the related configuration file
redis-cli                      # opens a redis prompt


# Strings.

alincc

# For use in Chef:
# Adds a command: git-pull-recursive
file "/usr/local/bin/git-pull-recursive" do
  owner "root"
  group "root"
  mode "0755"
  action :create
  content 'find . -type d -name .git -exec sh -c "cd \"{}\"/../ && pwd && git pull" \;'
end

alincc

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

alincc

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

alincc

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

alincc

#!/bin/sh
echo Install all AppStore Apps at first!
# no solution to automate AppStore installs
read -p "Press any key to continue... " -n1 -s
echo  '\n'

echo Install and Set San Francisco as System Font
ruby -e "$(curl -fsSL https://raw.github.com/wellsriley/YosemiteSanFranciscoFont/master/install)"
echo Install Homebrew, Postgres, wget and cask
ruby -e "$(curl -fsSL https://raw.github.com/Homebrew/homebrew/go/install)"

alincc

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048