Best Linux Distro For Machine Learning (2024)

  • Last updated August 19, 2021
  • In

A Linux Distro is an open-source Linux operating system packaged with components like installation programmes, management tools and additional softwares.

  • by Debolina Biswas

Best Linux Distro For Machine Learning (1)

Best Linux Distro For Machine Learning (2)
Best Linux Distro For Machine Learning (3)

Over the last couple of years, operating system Linux has emerged as a significant player in the software space. What makes Linux so popular is its open-source software licensing model. It is free to use. This means users can download Linux from the internet and redistribute it under GNU licensing. Additionally, it offers advantages over other operating systems in terms of security, flexibility and scalability. A Linux distribution, often referred to as Linux distro, is an open-source Linux operating system packaged with components including installation programmes, management tools and additional softwares.

Distributions based on Linux kernel are user-friendly and usually easier to deploy than the traditional open-source versions. At present, there are hundreds of Linux distros available, targeting specific users and systems. Some of these are ready to use, while others are packaged as source code that has to be compiled during installation.

Today, we list a couple of the best Linux distros for machine learning:

Ubuntu

The open-source operating system on Linux, Ubuntu, was developed by Canonical and first released in 2004. Built on Debian’s architecture and infrastructure, Ubuntu comes in handy for beginners. It is meant for enterprise servers, desktops, cloud and IoT.

Ubuntu releases every six months and with long-term support (LTS) every two years. Its default installation includes the softwares – LibreOffice, Firefox, Thunderbird, and Transmission, besides games including Sudoku and Chess. Ubuntu is a popular choice of an operating system for cloud computing, with support from OpenStack.

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Owing to its ease of use, it is a popular Linux distribution choice for data professionals.

To know about Ubuntu’s community, click here.

Arch Linux

Developed by Levente Polyak, Arch Linux is focused on simplicity, modernity, pragmatism, versatility and user-centrality. It was initially released in 2002 and has since then become a go-to choice for developers. Arch Linux’s community believes in keeping it simple. Additionally, its DIY (Do It Yourself) approach enables one to handle everything — from installing to managing. That is, the user decides the components and services they wish to install.

Interestingly, Arch Linux has a rolling release distribution feature, which means it allows new kernel and application versions to be rolled out as soon as they are released. If you are not familiar with Arch Linux or want to know more about the features and its community, check Arch Wiki.

Fedora

Sponsored by Red Hat (an IBM subsidiary), the Fedora operating system contains software distributed under free and open-source licenses. Its primary focus is to provide the latest version of the software. In addition, fedora creates a platform for hardware, clouds, and containers and enables developers to build tailored solutions for their users.

At present, Fedora offers five different versions — Workstation for personal computers, Server for servers, CoreOS for cloud computing, Silverblue for desktop specialisation, and IoT for IoT devices. Know more about its features here.

Linux Mint

Community-driven Linux distro Linux Mint is based on Ubuntu. Free and open-source, Linux Mint was created to produce a modern, comfortable, powerful yet easy to use operating system. Developed by Clement Lefebvre, Linux Mint was first released in 2006. The community-driven Linux distro provides about 30,000 packages and requires minimum maintenance.

Its latest version — Linux Mint 20.2, was released in July this year and will be supported until 2025. It is beginner-friendly, and its software manager is lighter and quicker. In addition, Linux Mint is available for a variety of desktop environments to select from — Cinnamon desktop, MATE and Xfce. To get hold of the download link, click here.

CentOS

CentOS is a community-driven free open source ecosystem for Linux. It is derived from Red Hat Enterprise Linux (RHEL) sources and was initially launched in 2004.

Since it is derived from the Red Hat Linux distro, whenever Red Hat publishes security updates, CentOS turns those updates around and presents them to the community within 24-hours. CentOS thus offers the same stability, interoperability, security and consistency as Red Hat.

Its latest versions, CentOS 8 and CentOS 8-Stream, offer virtualisation, security details, installation and image creation, infrastructure services, and networking. In 2019, Red Hat announced that CentOS Stream would replace CentOS. Know more about the features and developments here.

Choosing the right Linux Distro can be tricky and mostly depends on why one is using it. While Ubuntu offers ease-of-use, Arch Linux is more suitable for users looking for an advanced-level operating system, and CentOS offers Red Hat’s features without its trademark. If you are starting with Linux distros, you can select one from this list, or if you are looking for stability, these are the 10 most stable Linux Distros. To build your own Linux distros, these tools can be helpful.

Debolina Biswas

After diving deep into the Indian startup ecosystem, Debolina is now a Technology Journalist. When not writing, she is found reading or playing with paint brushes and palette knives. She can be reached at debolina.biswas@analyticsindiamag.com

Best Linux Distro For Machine Learning (8)
Best Linux Distro For Machine Learning (9)
Best Linux Distro For Machine Learning (10)

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Best Linux Distro For Machine Learning (2024)

FAQs

Best Linux Distro For Machine Learning? ›

1. Ubuntu Desktop. We all know about Ubuntu, and I think if you are a developer or machine learning engineer you are using Ubuntu on Windows 11 through WSL. Ubuntu is the most popular Linux distribution out there due to its user-friendly interface, extensive documentation, and large community support.

Which OS is best for machine learning? ›

One of the most commonly used operating systems for machine learning is Linux. The open source nature of Linux environments lends itself well to the complex installation and configuration processes required by many machine learning applications.

Which Linux distro is best for data science? ›

Ubuntu Desktop

Ubuntu stands out as a top pick for Linux beginners, offering a user-friendly experience and a vast array of data science tools and libraries readily available in its repositories.

Is Linux good for AI ML? ›

It's the best choice due to its stability, flexibility, and open-source nature. In addition, users can customise Linux to fit their needs. It has numerous libraries and tools, making it ideal for developing machine learning and artificial intelligence.

Which Linux distro is based on AI? ›

Ubuntu is one of the best Linux distros you could use for the development of AI tools. It's a stable distribution that provides an extensive software repository. In addition, it has a user-friendly interface, which makes it easy to find and navigate the desired tools.

Which OS is better for Python programming? ›

Linux is a fast, efficient, and lightweight OS. Source codes and scripts run faster in Linux, that's why it is the best operating system for programmers.

What is the best OS for TensorFlow? ›

TensorFlow is tested and supported on the following 64-bit systems:
  • Python 3.8–3.11.
  • Ubuntu 16.04 or later.
  • Windows 7 or later (with C++ redistributable)
Mar 24, 2023

Which Linux is best for TensorFlow? ›

Ubuntu, a Debian-based Linux operating system, offers a perfect platform for machine learning tasks. Known for its ease of use, robustness, and extensive community support, Ubuntu pairs seamlessly with TensorFlow, providing a reliable environment for ML projects.

What is the most professional Linux distro? ›

The best Linux distros for developers of 2024 in full:
  1. Manjaro. Best Arch-based distro for developers. ...
  2. Ubuntu. Great all-rounder: a popular distro for developers that does it all. ...
  3. Puppy Linux. Best Linux distro for developers using older machines. ...
  4. Solus. ...
  5. Sabayon Linux. ...
  6. Debian. ...
  7. CentOS Stream. ...
  8. Fedora Workstation.
Dec 22, 2023

Which processor is best for AI ML? ›

What CPU is best for machine learning & AI? The two recommended CPU platforms are Intel Xeon W and AMD Threadripper Pro. This is because both of these offer excellent reliability, can supply the needed PCI-Express lanes for multiple video cards (GPUs), and offer excellent memory performance in CPU space.

Which Linux kernel is best for deep learning? ›

I would say Ubuntu. Most of the framework (Tensorflow, Cntk, etc) are tested to work with Ubuntu (most of the cases, the old LTE versión Ubuntu 16.04). This doesn't mean that you cannot use any other distro, but the easiest way to get started with deep learning in Linux is Ubuntu.

How much RAM is needed for AI and ML? ›

For smooth handling of most RAM tasks, as a rule of thumb, it's good to get at least twice as much memory as the VRAM in the laptop's GPU. So, if you have 8 GB of VRAM, you'll want at least 16 GB of RAM. But the more, the better. Also check if the laptop has RAM slots that will allow you to upgrade.

What Linux does NASA use? ›

All NAS compute systems are running the Red Hat Enterprise Linux-based Tri-Lab Operating System Stack (TOSS).

Which Linux distro is used by Google? ›

gLinux is a Debian Testing-based Linux distribution used at Google as a workstation operating system. The Google gLinux team builds the system from source code, introducing their own changes. gLinux replaced the previously used Ubuntu-based distribution, Goobuntu.

What system does OpenAI use? ›

Since then, OpenAI systems have run on an Azure-based supercomputing platform from Microsoft.

Is Windows or Mac better for AI? ›

AI requires a lot of RAM, specifically VRAM, and that doesn't change just because you use macOS. However, the advantage that Macs have over Windows PCs is that they can better manage and allocate memory usage. For example, a Windows PC with 32GB RAM and only 4GB VRAM can't do much if an AI process requires more VRAM.

What operating system is used for AI? ›

Linux: Linux-based operating systems, such as Ubuntu, Fedora, and CentOS, are widely used in AI development. Linux offers a robust and flexible environment for AI researchers and developers, providing access to various AI libraries, tools, and frameworks like TensorFlow, PyTorch, and scikit-learn.

Is Mac good for AI and ML? ›

Software Support: Most ML/DL frameworks, libraries, and tools have excellent support for both macOS and Windows. However, some libraries may have better documentation or community support for one platform over the other.

What is OS in machine learning? ›

Machine learning and operating systems (OS) have an indirect relationship. Operating systems provide the foundation for running software applications on a device, including machine learning applications. Machine learning algorithms, in turn, can be used to improve the performance and functionality of operating systems.

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