How To — Ditching Ubuntu in favor of Arch Linux for a Deep Learning Workstation. Most of you might be using Ubuntu for their workstations, and that is fine for the more inexperienced users.
Most of you might be using Ubuntu for their workstations, and that is fine for the more inexperienced users. 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. 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.
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.
Additionally, 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. 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”. Being way closer to the hardware Arch is insanely faster compared to Ubuntu (and miles ahead of Windows), for the cost of more Terminal usage.
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. I can have both TensorFlow 1.15 and 2.0 working together, switching the versions with Anaconda environments. 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.
All in all, I can only recommend setting up an Arch Linux Deep Learning station as it is:
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”.
For the general “How to install Arch Linux”, head over to this article.
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. All in all it should work the same though.
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!
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
Best Free Resources to Learn Programming, Software Engineering, Machine Learning, And More All you need to learn. Do you know that you can take the courses from MIT, Stanford.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.
If Machine Learning is a dish, then linear algebra, programming, analytical skills, statistics, and Algorithms are the primary recipes of Machine Learning.