How to download and setup Pytorch, CUDA 9.0, cuDNN 7.0, Anaconda2 with or without sudo rights

gistfile1.txt

###  How to download and setup Pytorch, CUDA 9.0, cuDNN 7.0, Anaconda2 with or without sudo rights

# Tested on Ubuntu 16.04, GPU support, pytorch 0.4.1, cuda 9.0, cuDNN 7.0, Anaconda2 version 5.2.0. If you want other versions small changes must be made.
# Note: you can install everything with or without sudo rights ( if you do not have sudo rights we will install everything locally )

### Download and install CUDA 9.0 Toolkit 
# Note: NVIDIA drivers can be also installed during the process (if you have the rights)
# Check that the version of your NVIDIA drivers is compatible with CUDA 9.0. If not you should install the drivers or change CUDA version.
# The default installation path is "/usr/local/cuda". You can install here ONLY if you have the rights to do that, otherwise you must install them somewhere else. You can create a folder where you want and then you MUST say to the installation process the path to the folder you created. Example: I create a folder in ~/nvidia_libraries/cuda-9.0/ so when the process will ask where to install the toolkit just say: "/home/nvidia_libraries/cuda-9.0/".
# When asked if you want to create a symbolic link say YES if you have installed them in /usr/local/cuda, NO otherwise

cd
cd Downloads
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda_9.0.176_384.81_linux-run
# Note: if you have the NVIDIA drivers already installed it will ask to install them again or update them. If you don't want to do it select NO. Then select YES to install the NVIDIA toolkit. Then select NO when asked to install NVIDIA samples.)

# If you can use sudo:
sudo sh cuda_9.0.176_384.81_linux.run

# If you cannot use sudo:
cd
cd ~/
mkdir nvidia_libraries
mkdir nvidia_libraries/cuda-9.0

chmod +x cuda_9.0.176_384.81_linux.run
./cuda_9.0.176_384.81_linux.run

### Download and install cuDNN 7.0 ( required to run CUDA 9.0 )
# Note: you have to create a profile on the NVIDIA website and then navigate to the download page of cuDNN (https://developer.nvidia.com/rdp/cudnn-archive)

- Download cuDNN 7.0 for linux by clicking on the link

# Assuming that the file is in ~/Downloads

cd
cd Downloads
tar -xzvf cudnn-9.0-linux-x64-v7.tgz

# If you have installed CUDA 9.0 in /usr/local/cuda
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h

# If you have installed CUDA 9.0 somewhere else and you do not have sudo rights ( I have it in /home/nvidia_libraries/cuda-9.0/ for example )
cp cuda/include/cudnn.h /home/nvidia_libraries/cuda-9.0/include
cp cuda/lib64/libcudnn* /home/nvidia_libraries/cuda-9.0/lib64
chmod a+r /home/nvidia_libraries/cuda-9.0/include/cudnn.h

### Set enviroment variables for CUDA libraries

nano ~/.bashrc
# Copy and paste the following lines at the bottom ( assuming that your CUDA libraries are in "/home/nvidia_libraries/cuda-9.0/" )
export LD_LIBRARY_PATH="/home/nvidia_libraries/cuda-9.0/lib64:$LD_LIBRARY_PATH"
export PATH="/home/nvidia_libraries/cuda-9.0/bin:$PATH"

### Download and install Anaconda ( Anaconda2, version 5.2 here )

cd 
cd Downloads
wget https://repo.anaconda.com/archive/Anaconda2-5.2.0-Linux-x86_64.sh
bash ~/Downloads/Anaconda2-5.2.0-Linux-x86_64.sh
# Say no to install Microsoft stuff
# Close the terminal and open it again in order to make anaconda work

### Create conda environment ( here I call the environment "pytorch")
# You can define the version of python to use. I set 3.6 here

conda create --name pytorch python=3.6
source activate pytorch

### Install pytorch ( latest version, now 0.4.1)

conda install pytorch torchvision -c pytorch

### Done! 
# Close every terminal in order to make everything effective