Data science has become an indispensable field driving decision making across industries. With the exponential growth in data volume and data sources, the tools and techniques leveraged by data scientists are also rapidly evolving. Having an optimal software environment to support rapidly iterating on modeling pipelines is critical.
Many data scientists and developers have flocked to Linux as their operating system of choice for its combination of open source ecosystems, excellent performance, customizable environments, and lightweight footprint. However, with dozens of popular distributions, deciding the right Linux environment can be daunting.
In this comprehensive guide, we will cover the key criteria for evaluating a Linux distro for data science workloads and provide an in-depth comparison of the most popular options:
Key Criteria for Data Science Linux Distros
When searching for the right Linux distro for data science, some key factors to evaluate include:
Software Ecosystem & Support: Having prebuilt packages, dependency management, and support for key data science Python libraries like NumPy, Pandas, Scikit-Learn, TensorFlow etc. is essential. Easy access to visualization tools like Matplotlib and domain-specific libraries is also beneficial.
Performance & Hardware Support: Data science workloads like model training can push hardware to its limits. Distros that offer optimized math libraries, CPU optimization and robust GPU support are key, especially for deep learning.
Stability & Reliability: While bleeding edge updates are tempting, stability and reliability are crucial when iterating on research. Environments that balance newer packages with robust testing suit data science.
Productivity & Workflow: Notebooks, IDEs, containers and other tools that facilitate the end-to-end machine learning workflow improve daily work. Tight integration with version control, data/model pipelines and workflow orchestration are major bonuses.
With these criteria defined, let‘s deep dive into leading data science Linux distros.
Ubuntu
The standard bearer that kicked off Linux‘s popularity among data scientists and developers, Ubuntu remains a robust option. Canonical, the company behind Ubuntu, invests heavily in the ecosystem, from upstream open source technologies to partnerships with hardware vendors.
Software & Hardware Support
With Debian as its base, Ubuntu offers one of the richest software ecosystems. Thousands of scientific Python libraries and visualization tools are tested and optimized for Ubuntu‘s LTS releases.
Ubuntu also shines for GPU support. The investment NVIDIA has poured into CUDA toolkit optimization combined with Canonical‘s certification program pay major dividends for accelerating deep learning on Ubuntu workstations.
Performance & Reliability
While not always on the bleeding edge, Ubuntu offers solid performance for data science tasks. Math libraries like MKL and OpenBLAS are well-optimized. The shift to continuously delivered rolling releases provides frequent updates without sacrificing stability.
Productivity & Workflow
Canonical has invested in numerous innovations and tools tailored to the data science audience on Ubuntu, including:
- Kubeflow – a dedicated machine learning toolkit streamlining model deployment
- Ubuntu Pro – a premium support offering specifically for data science and AI
- Anbox Cloud – containers allowing enterprise applications to run seamlessly
With a vast library of tutorials, one of the largest developer and data science user bases and an unparalleled enterprise ecosystem, data scientists rank Ubuntu as one of the most productive Linux distros for daily workflows.
Fedora
Developed as a testbed for innovation later incorporated into commercial distros, Fedora offers excellent support for newer open source data science libraries. The Red Hat sponsored community project balances this innovation with a robust CoreOS foundation promising stability.
Software & Hardware Support
As the upstream source for commercial distros like RHEL and CentOS Stream, Fedora ships bleeding edge updates. Developers can access the newest data science libraries and GPU support through OpenCL and CUDA directly in Fedora before they land in downstream distros.
The Software Collections Library (SCL) allows next-generation versions of Python, R and other tools to be installed alongside stable baseline package versions. This facilitates continuity for existing code while evaluating newer languages and frameworks.
Performance & Reliability
By focusing on free and open source software (FOSS), Fedora provides excellent software optimization out of the box. However, the short 13 month lifecycle between major upgrades can force users onto the so-called "Fedora upgrade treadmill".
Heavy patching is often required for long-term stability when sticking to a single LTS version. For this reason, many data science teams use Fedora as a fast-moving development environment rather than production workhorse.
Productivity & Workflow
For small teams needing access to the latest libraries and techniques on modest hardware budgets, Fedora delivers. Integration with OpenShift for container-based workflows is another bonus, as is access to newer notebooks like JupyterLab.
However, with only informal product specialization or support for the data science audience, the out of the box workflow remains less refined than commercial counterparts. Larger teams may struggle with tailoring and hardening Fedora at scale.
OpenSUSE
Backed by SUSE Linux Enterprise, OpenSUSE strikes a nice balance between bleeding edge innovation and stability. The Open Build Service for packaging and automated testing allows OpenSUSE to ship newer upstream packages without compromising reliability.
Software & Hardware Support
While the primary focus is delivering newer open source technologies rather than a tailored data science-specific experience, OpenSUSE does check many boxes:
- Python, R, Julia and thousands of libraries available
- Kubernetes support out of the box
- Tight integration with SUSE Linux Enterprise (SLE)
The relationship with SUSE pays dividends in extensive certification for enterprise GPUs and hardware, even if automated configuration and optimization is lacking.
Performance & Reliability
Boasting itself as "the world‘s most usable Linux distribution", OpenSUSE places emphasis on user experience consistency rather than peak performance. However, OpenSUSE is no slouch thanks to many of the low-level optimizations shared with SUSE‘s commercial enterprise Linux variant.
OpenQA, OpenSUSE‘s automated testing framework, facilitates reliability by executing thousands of test cases against SUSE‘s Linux, KDE Plasma desktop and associated modules. Rigorous integration testing leads to a solid experience with the newest upstream packages and kernels.
Productivity & Workflow
Between the Open Build Service packaging automation, OpenQA continuous testing and openSUSE Leap + SUSE Linux Enterprise code sharing, OpenSUSE offers a compelling developer workflow:
- Prototype against new FOSS functionality in OpenSUSE Tumbleweed rolling release
- Fine tune for stability and performance in OpenSUSE Leap
- Streamline management at scale with SUSE Manager when deploying to production with SUSE Linux Enterprise
However, for data scientists that want to hit the ground running, OpenSUSE expects a bit more elbow grease tailoring the rich platform to specialized workloads.
CentOS Stream
Building on decades of enterprise usage as the free clone of Red Hat Enterprise Linux, CentOS Stream focuses on building an innovative Linux pipeline. The rolling updates avoid version lock-in while offering stability from upstream RHEL testing.
Software & Hardware Support
Subscribing to RHEL‘s supported kernel, compilers and libraries ensures excellent out of the box optimization. Certification for data science focused hardware like NVIDIA Clara and DGX Systems combines with Red Hat‘s software partnership ecosystem including providers of proprietary libraries.
However, the focus remains delivering emerging RHEL functionality to enthusiasts rather than optimizing specifically for data science. Expect to self-assemble more elements of the pipeline.
Performance & Reliability
CentOS Stream concentrates RHEL‘s considerable resources and maturity on constant integration testing of bleeding edge functionality destined for downstream inclusion. This leads to more frequent updates than RHEL while benefitting from the scrutiny during QA.
Delta RPMs that deliver only updated packages reduce patching overhead and downtime further improving stability. For users comfortable configuring upstream RHEL environments at scale, CentOS Stream hits a nice reliability sweet spot between raw innovation and productization.
Productivity & Workflow
For large organizations already invested in the Red Hat ecosystem, CentOS Stream offers an on-ramp to integrate emerging functionality with more rigidity and auditing than Fedora. Tight integration with in-house Linux administration skills, OpenShift and Ansible simplify scaling an innovative pipeline.
Mid-sized data science teams may find the networking effect within the community around cutting edge functionality lacking compared to rivals. The out of the box workflow requires more self-assembly.
Beyond the high-level comparisons, evaluating specific machine learning capabilities by distro proves useful:
| Functionality | Ubuntu | Fedora | OpenSUSE | CentOS Stream |
|---|---|---|---|---|
| Native Notebooks | JupyterLab | Jupyter Notebook | Jupyter Notebook | Jupyter Classic |
| Python Support | Extensive | Extensive | Extensive | Extensive |
| R Support | Extensive | Extensive | Extensive | Extensive |
| Julia Support | Available | Available | Lacking | Lacking |
| TensorFlow support | Excellent | Excellent | Available | Available |
| PyTorch Support | Excellent | Excellent | Available | Available |
| Keras Support | Excellent | Excellent | Available | Lacking |
| Kubeflow | Well-Integrated | Lacking | Lacking | OpenShift AI Alternatives |
| GPU/TPU Support | Excellent | Excellent | Excellent | Excellent |
| Integrated Workflow | Excellent | Lacking | Lacking | OpenShift Integration |
Evaluating Linux options based on key framework and tool availability removes much distro preference subjectivity. Target environments like software development, model prototyping, model training, and model serving may prioritize different libary access.
Beyond picking the right distro, properly configuring your environment ensures optimal data science performance:
Filesystems
Most distros now default to ext4, but leveraging newer generation filesystems like ZFS and Btrfs improves reliability and scalability for data lakes:
- ZFS – Excellent integrity checking, snapshots, and RAID integration
- Btrfs – Fast snapshots and incremental send/receive for backups
Just ensure selecting disk formats supporting your ML frameworks (no XFS for Docker/Kubernetes, for example).
GPUs for Deep Learning
Adding NVIDIA GPUs supercharges model training. But complications with drivers and CUDA toolkit compatibility between Linux kernels, distros, and GPU hardware quickly causes headaches.
Using a certified distro and GPU stack circumvents most issues. Also consider containers, Kubernetes device plugins, and virtual GPU allocation when sharing hardware between multiple data scientists.
Network Attached Storage (NAS)
Slow bulk data pipeline movement to intermediate NAS systems kills productivity. Where possible:
- Leverage fast networking (25/100 GbE)
- Use parallel NFS mounts
- Employ SSD read/write caches
Investing in fast parallel data ingest, efficient distributed filesystems like Lustre or GlusterFS, and hierarchical storage delivers dividends.
We‘ve covered a lot of ground when it comes to selecting the right Linux distro for your data science efforts. With core software ecosystem, stability and workflow optimization differences clarified – where should teams start?
For most data science teams, Ubuntu continues to deliver the most refined, productivity-enhancing out of the box experience. The unparalleled access to optimized mathematical libraries, extensive GPU support, huge upstream investments, and access to complementary data science tooling explain its continued popularity.
However, for organizations needing tighter alignment to downstream enterprise Linux vendors or smaller teams wanting simpler access to upstream innovation, Fedora and CentOS Stream both appeal. Likewise, more experimental teams or those already bought into the SUSE ecosystem can benefit from OpenSUSE‘s balance of new and stable.
Of course, one size rarely fits all data science environments given the diversity of applications and infrastructure. Hopefully clarifying the core optimization criteria, differentiation in distro capabilities, and infrastructure best practices helps teams better evaluate the Linux options for supporting smoothly running data science pipelines.