msis

1. ssh to the remote instance (based on Ubuntu Deeplearning AMI) with port* redirection:

ssh -L 8888:127.0.0.1:8888 ubuntu@IP

2. activate the environment (here we're using tensorflow_p36):

source activate tensorflow_p36

3. launch jupyter:

msis

1. File > Open ...
2. Launch new terminal instance: New > Terminal
3. tarball the folder:

tar czvhf coursera.tar.gz *

3. a. split it (usually this file might be too large for your instance to allow download)

split -b 500M -d coursera.tar.gz coursera.

msis

When running snap inside lxc container, snap install might fail with the following output for :

$ systemctl status snap-core-2898.mount

● snap-core-2898.mount - Mount unit for core
   Loaded: loaded (/etc/systemd/system/snap-core-2898.mount; enabled; vendor preset: enabled)
   Active: failed (Result: exit-code) since Tue 2017-09-26 15:36:39 UTC; 26s ago
    Where: /snap/core/2898
     What: /var/lib/snapd/snaps/core_2898.snap

msis

#!/bin/bash
# Mohamed Saad Ibn Seddik

set -e # exit with nonzero exit code if anything fails

# go to the out directory
cd ${DEPLOY_DIR} || exit 1

# initiate new git repo inside the new deploy folder
git init

msis

# when using the new C++11 for loops:
# for (auto i : range) {}
target_compile_features(myapp PRIVATE cxx_range_for)

msis

# To be copied in /etc/ld.so.con.d/
# Manually installed Qt5
/home/big/Qt/5.4/gcc_64/lib
# Do not forget to run ldconfig after to update the cache!

msis

#!/bin/bah
mount -o loop -t iso9660 file.iso /mnt/test

msis

#!/bin/bash
export CMAKE_PREFIX_PATH=$CMAKE_PREFIX_PATH:$HOME/Qt/5.4/gcc_64

msis

#!/bin/bash

#this script looks for a window named Mozilla Firefox and then starts procedure to select all mails in the current folder and archive them

for i in {1..30}
do
  xdotool search --name "Mozilla Firefox" key s+a
  xdotool search --name "Mozilla Firefox" key e
  sleep 5
done

msis

## Add freshly installed libraries
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib