I keep breaking and having to reinstall my Ubuntu setup, so I figured I'd make a little document that helps me do that faster. From a completely fresh install. ## First things first. - Download and install Chrome. - `sudo apt update` - `sudo apt upgrade` - `sudo apt install curl` - `sudo apt install vim` - `sudo apt install git` - `git config --global user.name "John Doe"` - `git config --global user.email johndoe@example.com` - `sudo apt install openssh-server` - `sudo vim /etc/ssh/ssh_config #and change port` - `sudo service ssh restart` - `ssh-keygen -o -f ~/.ssh/id_rsa` - `cat ~/.ssh/id_rsa.pub` - Paste key into https://gitlab.com/profile/keys or wherever your git host is. ## Nvidia driver. Do this first, if nothing else, so you can stop looking at a 640x480 screen. Install nvidia, cuda, and cudnn based on [this](https://hackernoon.com/up-and-running-with-ubuntu-nvidia-cuda-cudnn-tensorflow-and-pytorch-a54ec2ec907d). - `sudo add-apt-repository ppa:graphics-drivers/ppa` - `sudo apt install nvidia-driver-410` - `sudo apt-mark hold nvidia-driver-410` - `sudo reboot` ## Cuda 10 - `wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64` - `sudo dpkg -i cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64` - `sudo apt-key add /var/cuda-repo-10-0-local-10.0.130-410.48/7fa2af80.pub` - `sudo apt-get update` - `sudo apt-get install cuda` - `sudo apt-mark hold cuda` - `echo "export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}" >> ~/.bashrc` - `source ~/.bashrc` ## cuDNN - Go to https://developer.nvidia.com/rdp/cudnn-download and create an account if you don't already have one. It's free. - Click "Download cuDNN v7.6.0 (May 20, 2019), for CUDA 10.0" - Download all 3 files with 18.04 in the name. Specifically the runtime library, developer library, and code samples. - `cd Downloads` - `sudo dpkg -i libcudnn7_7.6.0.64-1+cuda10.0_amd64.deb` - `sudo dpkg -i libcudnn7-dev_7.6.0.64-1+cuda10.0_amd64.deb ` - `sudo dpkg -i libcudnn7-doc_7.6.0.64-1+cuda10.0_amd64.deb ` - `sudo apt-mark hold libcudnn7 libcudnn7-dev libcudnn7-doc` ## Conda Install conda based on [this](https://www.digitalocean.com/community/tutorials/how-to-install-the-anaconda-python-distribution-on-ubuntu-16-04). - `cd /tmp` - `curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh` - `sha256sum Anaconda3-2019.03-Linux-x86_64.sh` - `bash Anaconda3-2019.03-Linux-x86_64.sh` - `source ~/.bashrc` ## Tensorflow - `conda create --name tensorflow_p37 python=3.7` - `echo "alias p37='conda activate tensorflow_p37'" >> ~/.bashrc` - `source ~/.bashrc` - `p37` - `pip install matplotlib` - `pip install numpy` - `pip install pillow` - `pip install pathlib` - `pip install shutil` - `pip install ipython` - `pip install jupyter` - `pip install six` - `pip install tensorboard` - `pip install datetime` - `pip install neptune-client` Then if you are running on a GPU... - `pip install tensorflow-gpu` And if a CPU... - `pip install tensorflow` ## Apps - Slack - Visual Studio Code - TeamViewer Work in progress.

Ubuntu 18.04 Machine Learning Setup

I keep breaking and having to reinstall my Ubuntu setup, so I figured I'd make a little document that helps me do that faster.

From a completely fresh install.

Do this first, if nothing else, so you can stop looking at a 640x480 screen.
Install nvidia, cuda, and cudnn based on this.

wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64
sudo dpkg -i cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64
sudo apt-key add /var/cuda-repo-10-0-local-10.0.130-410.48/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
sudo apt-mark hold cuda
echo "export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}" >> ~/.bashrc
source ~/.bashrc

Go to https://developer.nvidia.com/rdp/cudnn-download and create an account if you don't already have one. It's free.
Click "Download cuDNN v7.6.0 (May 20, 2019), for CUDA 10.0"
Download all 3 files with 18.04 in the name. Specifically the runtime library, developer library, and code samples.
cd Downloads
sudo dpkg -i libcudnn7_7.6.0.64-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.0.64-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.6.0.64-1+cuda10.0_amd64.deb
sudo apt-mark hold libcudnn7 libcudnn7-dev libcudnn7-doc

Install conda based on this.

Then if you are running on a GPU...

And if a CPU...

Work in progress.