Why Ubuntu is the best for Deep Learning Framework?

Ubuntu for deep learning

Why use Ubuntu for deep learning? This is the question this article tries to answer. After reading this article you will not have any doubt regarding which platform you should use for your deep learning experiments.

I was also quite happy with my windows 10 and Colab/Jupyter notebook combination for all of my Artificial Intelligence (AI)/Machine Learning(ML)/Deep Learning(DL) programming. Until I decided to start some serious work with deep learning neural network models.

Is it really important?

“[M]achines of this character can behave in a very complicated manner when the number of units is large”

Alan Turing (1948), “Intelligent Machines”, page 6

Soon I started my first model building, the limitation of my present working environment came into my notice. In some forums like Quora, Reddit etc. I was reading some threads on deep learning. And suddenly someone there in his reply mentioned that Ubuntu is a better choice for serious application in deep learning.

Suddenly it struck me that probably it is not a wise choice to continue with Windows for advanced application of deep learning and AI. But it was just a hunch that time. And I needed strong logical points before I made my mind to switch an OS which the only platform I have ever used.

So, I started scouring through internet. Read almost all blogs, threads in discussion forum to make sure if switching the platform really worth my time. Because getting aquiented with a completely new OS takes time and time is money for me.

If anyone of you also in the learning phase and serious about deep learning, this article will help him/her to make an informed decision to select which platform he/she should use. Because it is always a waste of valuable time to switch your working environment at a later stage of learning and lots of rework too.

I have already done the heavy work for you and presenting a vivid description of the topic so that you get all your questions answered at one place.

So let’s start with an introduction with Ubuntu. Although this is not an article on Ubuntu. You can find so many good articles on Ubuntu. But still before knowing its special features concerning deep learning, here is a very brief idea.

What is Ubuntu?

Ubuntu is one of the most popular forms of Linux distribution. It is developed by Mark Shuttleworth of Canonical lab. It is also the most famous open source technology that means all features and applications it offers are completely free. And it is an undeniable fact that being free makes an application mile ahead in popularity automatically.

“What commercialism has brought into Linux has been the incentive to make a good distribution that is easy to use and that has all the packaging issues worked out.”

Linus Torvalds, Principal developer of the Linux kernel

Being open-source, Ubuntu offers its a new update almost twice in a year while Long Term Support(LTS) releases after every two years with updated security patches. It has three main categories for distribution which are core, desktop and Server.

The core version is mainly for those working on IoT devices and robotics. The desktop version is for common users doing day to day office tasks and also programming applications. The server version is obviously for client-server architecture and generally meant for industry uses.

Why Ubuntu is preferred for deep learning?

The Ubuntu version I installed recently is version 20.04 and it is the latest version on this distro. It is a much-improved version than its predecessor. Especially the additional supports it provides for AI, ML and DL programmer is just stupendous.

The MicroK8s feature

“Given its smaller footprint, MicroK8s is ideal for IoT devices- you can even use it on a Raspberry Pi device!”

Kubernets.io, Technical blogs
MicroK8s integration in Ubuntu
MicroK8s integration in Ubuntu

The user interface has improved a lot. The installation process has become very easy (it was always smooth though). The Ubuntu 20.04 version now comes with support for ZFS (a file system with high availability and data integrity) and an integrated module called Microk8s. So, the AI, DL developers now don’t have to install it separately.

Microk8s enables the AI application module to get set up and deployed blazing fast. It comes preloaded with all necessary dependencies like automatic update and security patches. Quite obvious that with this version of Ubuntu you will now need to spend much lesser time to configure the environment.


It is another deep learning edge of Ubuntu 20.04 and comes as an add on to Microk8s. Kubeflow was developed by Google in collaboration with Canonical, especially for Machine Learning applications. It provides inbuilt GPU acceleration for deep learning R&D.

What is Kubeflow?

Kubeflow deployed with Kubernetes and do away with the barrier to create production-ready stacks. It provides developers with enhanced AI, ML capabilities with edge computing feature. The researchers and developers involved in cutting edge research activities get a secured production environment with strict confinement in complete isolation.

Kubeflow architecture
Source: Kubeflow blog by Thea Lamkin

The security provided by Kubeflow and Kubernetes integration is unparallel. Many AI/ML/DL development add ons like Jaeger, Istio, CoreDNS, Prometheus, Knative etc come integrated with it and can be deployed with a single command.

The programming edge of Ubuntu

When it comes to programming activities, Ubuntu is undoubtedly the leader. Not only for AI, ML or DL programming but any kind of programming task and application development task is best performed when the operating system is Ubuntu.

It has the best libraries, vast examples and tutorials readily available for users. The support for all open source software used with Ubuntu is massive to solve any issue you face quickly. The updates are also regular and irrespective of which version you are using.

The enhanced Graphics Processing Unit

Powerful GPU is an important component for serious ML/DL programming. Ubuntu has an edge here to make any contemporary changes in AI environment. NVIDIA the respectable name in GPU manufacturing industry has put all efforts to make Ubuntu powerful with CUDA to its maximum capacity.

Ubuntu in its latest version 20.04 also gives its user an option to use external graphics cards through thunderbolt adapters. They can add them through dedicated PCI slots too.

So no surprise that all deep learning frameworks like Keras, TensorFlow, OpenCV, PyTorch etc all prefer Ubuntu over all other OS. The world leaders in advanced AI/ML/DL research and development like autonomous car sector, the CERN and LHC, famous brands like Samsung, NVIDIA, Uber etc. all use Ubuntu for their research activities.

Advanced feature and support for Hardware

The support Ubuntu comes with for hardware is also exceptional. Ubuntu provides organization-specific hardware certification which means high compatibility is assured. The hardware has tight integration with BIOS and factory level quality assurance.

To achieve the quality hardware Canonical directly deals with the hardware manufacturers. Canonical develops partnerships with major hardware manufacturers in order to provide an operating system with preloaded and pretested features.

The support team is as usual exceptional anytime ready for any kind of troubleshooting. With all these assurances developers can fully concentrate on their R&D.

Finally the Software

Canonical’s Ubuntu has its own open-source software collection. The software devices all are compatible both at board level as well as component level. All of its versions old or latest contain the same package of software. This feature has several advantages.

The large user base of Linux using different versions of Ubuntu with a seamless experience of switching between them. This becomes possible only because of the same software packages across all the versions. Developers can easily test their applications locally before launching them on the web for global users.

The bunch of open source software makes it possible fast creation of AI models. Creation of software and debugging is fast and easy on IoT hardware before deployment.

The snapcraft tool

Snapcraft, the app store for Linux

It is another major feature of Ubuntu which makes it a clear winner for an ideal programming OS. Snap is a feature for packaging and distribution of containerized applications. The automatic updates in Ubuntu are very safe to install and execute only because of this snap feature.

Snapcraft is a command line tool which creates snaps. This tool makes packaging of applications very easy. The feedback of the users through snapcraft tool has immense importance for the developers. These feedback provides the necessary insights about the software and helps further improvement.

For example, a study made by Canonical revealed that maximum users of Ubuntu never update the software. So, based on this feedback they started to provide automatic updates. Canonical does not need to provide support for older versions. As the complete user base simultaneously moves to the latest version of Ubuntu.

Massive online support base

Being an open-source platform, Ubuntu has a massive online support and documentation repository. Any user anytime can use the service like Slack and Skype to ask their queries. The Ubuntu support group is also very vibrant. Here you can expect a reply from the development team itself.

Even popular question-answer groups like Quora, Reddit etc. also have threads on Ubuntu related queries. I personally got many of my queries already answered there. Even you have some unique problem that has not answered earlier, you can post them in any of these platforms. It is highly likely that within a few hours you will get some really helpful suggestions by either any normal user or the Ubuntu support/development team itself.

Final words

As you finish reading this article you have a clear idea of why you should pick Ubuntu as your machine learning or deep learning programming platform. I have tried my best to put together all the information I got reading many articles online or offline.

I invested a lot of my time researching this topic to be 100% sure before diving deep into the advance learning. It is an important decision no doubt. I had bitter experiences before when I already put a lot of effort into learning a particular application. And then one day due to some limitation I had to backtrack and change that platform or application.

It was quite a rework and wastage of time starting fresh from scratch. And it can be avoided if I had done thorough research in the very beginning. So, I learned my lessons and made no mistakes this time. And hope it will also help you make an informed decision.

So, please let me know if you find the article useful by commenting below. Any queries, doubt, suggestions are welcome. I would try to improve the post further based on your comments.