Installing Nvidia Drivers on Ubuntu Server 18.04

Aravind — Sun, 08 Dec 2019 10:40:00 +0000

Installing the Nvidia Proprietary Drivers for your Ubuntu Server 18.04 are necessary for proper GPU accelerated processing and CUDA. The following instructions will show how to install the latest drivers.

1: Add the repository for the Nvidia drivers

sudo add-apt-repository ppa:graphics-drivers/ppa

2. Update to read the packages available

sudo apt update

3. You can also run the following commands to make sure that whenever the kernel is updated dkms rebuilds the Nvidia driver for the new kernel too.

sudo apt install dkms build-essential

4. Install the driver. At the time of writing 430 was the latest. One thing to note is that this will install a GUI for the server. But if you have it set up as a headless server, it will still function the same way and you will be able to connect through ssh.

sudo apt install nvidia-driver-430

5. Reboot your system

sudo reboot

6. Once your system has rebooted, then run the following command to check if everything is working.

nvidia-smi

7. You should get the following output: I have a GTX 1070 and that’s what shows below. Yours may be different.

And that should be it. Your driver is now installed and ready to go.

The post Installing Nvidia Drivers on Ubuntu Server 18.04 appeared first on AI After Work.

Installing Python, PostgreSQL and JupyterLab

Aravind — Sun, 11 Nov 2018 05:30:00 +0000

Installing Python

The easiest way to get not only Python but all the python libraries needed for data science work is to download the Anaconda distribution.
Detailed instructions are available at https://docs.anaconda.com/anaconda/install/linux/. I prefer to use Python version 3 but the instructions show you how to download Python 2 too.

There is also a choice between anaconda and miniconda. Miniconda as the name suggests, is a mini version of Anaconda that only contains the conda package manager and it’s dependencies. If you prefer to have conda plus over 720 open source packages, install Anaconda.

Installing PostgresSQL

You will definitely need a database for your data science projects. At some point working on really large datasets is more efficient within a database. I chose to go with PostgreSQL 12.

Installation instructions can be found here: https://www.postgresql.org/download/linux/ubuntu/ but I’ve also copied the instructions for Ubuntu 18.04 LTS.

Create the file /etc/apt/sources.list.d/pgdg.list and add a line for the repository
```
deb http://apt.postgresql.org/pub/repos/apt/ bionic-pgdg main
```

Import the repository signing key, and update the package lists

wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key addsudo apt update

Finally install the application
```
sudo apt install postgresql-12
```

Changing the location of the database to your SSD

If you remember how I built my Ubuntu Analytics server, I added in both an SSD (Solid state drive) and HDD (Hard Disk Drive) for storage. The idea was that my database and any other application that needed fast data read/write capabilities would sit on the SSD. Since Postgresql would be handlong a lot of data it became an ideal candidate to move to the SSD. There is good news that the it has a parameter called data_directory that just needs the folder path to your SSD
Digitalocean has a great tutorial on just that.

Once that’s done your database should be very fast.

Install JupyterLab

I found a good set of instructions on this website https://agent-jay.github.io/2018/03/jupyterserver/

I did make one change to the instruction. To use jupyterlab at localhost:ipaddress you will have to create an ssh tunnel. That’s what the last set of instructions was doing

ssh -N -f -L 8888:localhost:9999 user@domain.com #Change the specifics as required

Instead I started jupyterlab with the ip address of my server itself so i could reach it directly without using localhost. So if your headless Ubuntu server was at 192.168.1.2 on your LAN then you can use the command as follows:

jupyter lab --no-browser --ip=192.168.1.2 --port=12345

You can now access Jupyterlab at https://192.168.1.2:12345 without opening an ssh tunnel to the server.

Another thing to do if you’re getting a 403 error when you try to open a notebook could be because of folder restrictions. So make sure to change the default notebook directory location in the config file to a location you have write access to.

c.NotebookApp.notebook_dir = '/absolute/path/to/folder'

Updated: Mar 26, 2020