sudo apt-get remove libcudnn7

ssh-keygen -t rsa -f ~/.ssh/[KEY_FILENAME] -C [USERNAME]
cat ~/.ssh/[KEY_FILENAME].pub
Save it to Metadata
ssh -i ~/.ssh/my-ssh-key [USERNAME]@[EXTERNAL_IP_ADDRESS]

The following script helps you install all the dependencies for keras.
We highly recommend that you run the following code, manually, line by line
to avoid any problem.

You need to run the code in the given order to avoid dependency issues.

* Linux Essentials
* Nvidia Driver
* CUDA
* cuDNN
* Tensorflow
* Keras

# essential
sudo apt-get update
sudo apt-get upgrade  
sudo apt-get install build-essential cmake g++ gfortran 
sudo apt-get install git pkg-config python-dev 
sudo apt-get install software-properties-common wget
sudo apt-get autoremove 
sudo rm -rf /var/lib/apt/lists/*

# install driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-375

# reboot your machine
Sudo reboot

# check driver is installed correctly
nvidia-smi

# install cuda
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda_9.0.176_384.81_linux-run
sudo chmod +x cuda_9.0.176_384.81_linux-run
sudo sh cuda_9.0.176_384.81_linux-run
'''
Executing cuda installation
1. scoll to the bottom of the license agreement using d
2. Do you accept the previously read EULA? : accept
3. Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81?: NO (we've installed manually)
4. Install the CUDA 9.0 Toolkit? : yes
5. Enter Toolkit Location: use default(press enter)
6. Do you want to install a symbolic link at /usr/local/cuda?: yes
7. Install the CUDA 9.0 Samples?: no
'''

# change environment for cuda
echo 'export PATH=/usr/local/cuda-9.0/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc 

#verify your cuda is installed correctly
nvcc -V 

# install cuDNN 
# You need to register online and download the file to local machine then upload to the cloud
# https://developer.nvidia.com/cudnn
# download the cuDNN 7.0 for CUDA 9.0 and for Ubuntu 16.04. 
sudo apt install ./(your cuDNN file)

# install python stuff
sudo apt-get update
sudo apt-get install git python-dev python3-dev python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev
sudo apt-get install -y libfreetype6-dev libpng12-dev
python3 -m pip install -U pip
pip3 install -U matplotlib ipython[all] jupyter pandas scikit-image

# install tensorflow
pip3 install --upgrade tensorflow-gpu

# keras
pip3 install keras

# pytorch
pip3 install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp35-cp35m-linux_x86_64.whl 
pip3 install torchvision

# helpful tools 
# tmux: keep session in the background. Keep the session running even the ssh disconnects.
# nohup: similar to tmux. Keep things running. Log the output to nohup.log

wget https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh

bash Anaconda3-5.1.0-Linux-x86_64.sh
# Select yes to all options during the setup process

# install pytorch
pip3 install http://download.pytorch.org/whl/cpu/torch-0.3.1-cp35-cp35m-linux_x86_64.whl 
pip3 install torchvision

tensorboard

Network
default

Priority
1000

Direction
Ingress

Action on match
Allow

Source filters
IP ranges
0.0.0.0/0

Protocols and ports
tcp:6006;udp:6006

# Get the code from tensorflow
git clone https://github.com/tensorflow/tensorflow.git

# Mnist is chosen as demo
cd tensorflow/tensorflow/examples/tutorials/mnist

# run it in the background, output is stored in train.log
nohup python3 mnist_with_summaries.py --max_steps=1000000 > train.log

# close and open another terminal
# run the tensorboard
nohup tensorboard --logdir=/tmp/tensorflow/mnist