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sudo apt-get update & sudo apt-get -y upgrade & sudo apt-get -y install build-essential gcc g++ make binutils & sudo apt-get -y install software-properties-common git & sudo apt-get install build-essential cmake git pkg-config 2. Make sure to connect your machine to the internet during the installation to get the latest updates and drivers.Īfter the installation, login and update and install necessary packages, e.g. I recommend option #2, to preserve access to Windows (e.g. Warning: I have never tested this option (you are on your own here!). Use WSL2 (Windows Subsystem for Linux) this is discussed here.Follow the i nstruction here (recommended option). Dual-booting Ubuntu alongside an existing Windows OS.Ubuntu Desktop 20.04 ( download here) is an ideal choice for that, as a lot of functionally works out-of-the-box, allowing us to save on the set-up time as compared to the other Linux distributions. Therefore, we will set-up a Linux OS for that purpose. Windows OS is no good - in my opinion and that of others - for doing any ML development or networking work. Ease-of-life tools: mounting remote directories with SSHFS, setting some commonly-used commands as bash aliases.Creating a Python environment for ML (TensorFlow, JupyterLab).
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Taking care of NVIDIA drivers and libraries (CUDA, cuDNN).Setting remote access ( ssh, WOL, DNS configuration, port-forwarding).Installing and setting Ubuntu 20.04 (stand-alone/dual-boot/WLS2).Moreover, you will be able to remotely switch-on the machine from anywhere and mount its filesystem directly on your laptop. Additionally, I will suggest a few procedures that will make your remote workflow more straightforward and more secure, based on my experience working with the DAQ systems during my PhD.īy the end of the article, you will be able to launch a remote JupyterLab session running on the GPU-host machine, from your laptop. I followed a similar guide from 2017 and found that a lot of steps have changed since then - for the better - in 2020. The goal of this article is to summarise the steps in setting up a machine for personal ML projects. The benefit of using GPUs for your ML workflow has been discussed previously. Milkshake is optional for the GPU set-up. Photo by Caspar Camille Rubin on Unsplash.