glukicov

printf "\nSourcing ML PY env: ~/gpu_ml/bin/activate\n"

# activate the gpu_ml environment
source ~/gpu_ml/bin/activate
cd ~/gpu_ml/

# open Jupyter lab on specific port 
alias jlab="jupyter lab --port=8899"

alias lr="ls -ltrh"

glukicov

# use ML as $ML in Linux (can use ip address instead of the DNS hostname) 
export ML=mlgpu.ddns.net

# just like below ;) for the magic packer command to wake-up a remote machine
alias wake_gml="wakeonlan -i $ML -p 22 b3:21:88:6k:17:24"

# open ssh tunnel from ML to the laptop in port 8899
alias sg="ssh -L 8899:localhost:8899 $ML"

# 'cd_ML' to quickly cd into the mounted directory 

glukicov

#!/bin/bash
#-----------------------------------------------------------
remotedir="USERNAME@HOST:/PATH/TO/MOUNT"  # TODO Add directory on the remote machine
mountdir="/Users/user/Mount/Directory"   # TODO Add local directory 
echo "Mounting (${mountdir})..."

if [ -d "${mountdir}" ]; then
  #Unmount if it's already mounted
  if mount | grep "on $mountdir" > /dev/null; then
    umount ${mountdir}  

glukicov

glukicov

printf "\nSourcing ML PY env: ~/gpu_ml/bin/activate\n"

# activate the gpu_ml environment
source ~/gpu_ml/bin/activate
cd ~/gpu_ml/

# open Jupyter lab on specific port 
alias jlab="jupyter lab --port=8899"

alias lr="ls -ltrh"

glukicov

# use ML as $ML in Linux (can use ip address instead of the DNS hostname) 
export ML=mlgpu.ddns.net

# just like below ;) for the magic packer command to wake-up a remote machine
alias wake_gml="wakeonlan -i $ML -p 22 b3:21:88:6k:17:24"

# open ssh tunnel from ML to the laptop in port 8899
alias sg="ssh -L 8899:localhost:8899 $ML"

# 'cd_ML' to quickly cd into the mounted directory 

glukicov

import matplotlib.pyplot as plt
plt.style.use('ml_style.mplstyle')

glukicov

pip install virtualenv

glukicov

mkdir ~/my_project &&
cd ~/my_project &&
virtualenv my_project_env

glukicov

source ~/my_project/bin/activate