If not, you could add the country tag to get more precise results, e.g. if the domains are close to your location. Reflector –verbose -l 20 -n 20 –sort rate –save /etc/pacman.d/mirrorlistĬheck the output if it makes sense, e.g. Therefore, we are going to install a small tool that finds and saves the fastest servers called “reflector” If not done automatically, it could happen that your servers are not optimized yet. Software is downloaded from so-called “mirrors”, which are servers containing all the Arch libraries. Once you are done with the Arch installation (phew!), let us first change some settings such that our system works more stable. I was thinking about creating a ready to install Image (iso or img), if enough people are interested leave a comment below or message me! Installing the Deep Learning (TensorFlow, CUDA, CUDNN, Anaconda) setup on a fresh Arch Linux installation All in all it should work the same though. If Arch is too complex for now, you could try out Manjaro, which is a user-friendly version of Arch, even though I can not guarantee that all packages will work the same, as they are slightly different. I will split the how-to in two parts, the first one being “How to I install Arch Linux” and the second one being “How to install the Deep Learning workstation packages”.įor the general “How to install Arch Linux”, head over to this article. Easier to switch between TensorFlow versions.Faster, like packages will install super fast, deep learning is supercharged, ….Also, the system works quite stable, as I am using the LTS (long term support) kernels of Linux, and usually updates to the famous AUR (user-made packages in Arch) are coming out a month ahead of the Debian (Ubuntu) packages.Īll in all, I can only recommend setting up an Arch Linux Deep Learning station as it is: I can have both TensorFlow 1.15 and 2.0 working together, switching the versions with Anaconda environments. When I have been using Arch in the past weeks, RAM usage usually halved compared to Ubuntu, and installing Machine Learning packages is a breeze. In their own words, Ubuntu is built to “work out of the box and make the installation process as easy as possible for new users”, whilst the motto of Arch Linux is “customize everything”.īeing way closer to the hardware Arch is insanely faster compared to Ubuntu (and miles ahead of Windows), for the cost of more Terminal usage. Working with different programs it would be nice to have a way of switching between the two most used TensorFlow versions of 1.15 and 2.0 like you can do with Google Colab in a single command, but installing a different TensorFlow version usually messed up my system again.Īdditionally, Arch has always been on my To-Do list, as it is the most “barebone” Linux distro you can get, meaning you are working way closer on the hardware compared to “higher abstractions” like Ubuntu. I’m not sure about you, but once I had a working Tensorflow 1.15 or 2.0 environment, I usually did not touch it anymore being scared to mess up this holy configuration. One of the issues I had with Ubuntu and the Tensorflow/CUDA though, has been that handling the different drivers and versions of CUDA, cudnn, TensorFlow, and so on has been quite a struggle. Most of you might be using Ubuntu for their workstations, and that is fine for the more inexperienced users. How To: Ditching Ubuntu in favor of Arch Linux for a Deep Learning Workstation Why should I ditch Ubuntu?
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