This blog post details setting up a bare-metal Kubernetes cluster on NixOS with Nvidia GPU support, focusing on simplicity and declarative configuration. It leverages NixOS's package management for consistent deployments across nodes and uses the toolkit's modularity to manage complex dependencies like CUDA drivers and container toolkits. The author emphasizes using separate NixOS modules for different cluster components—Kubernetes, GPU drivers, and container runtimes—allowing for easier maintenance and upgrades. The post guides readers through configuring the systemd unit for the Nvidia container toolkit, setting up the necessary kernel modules, and ensuring proper access for Kubernetes to the GPUs. Finally, it demonstrates deploying a GPU-enabled pod as a verification step.
This blog post by Fang Pen Lin details the process of setting up a Kubernetes cluster on bare metal NixOS machines, with a specific focus on enabling GPU support provided by Nvidia cards. The author emphasizes a declarative and reproducible approach using NixOS's configuration language and the nixpkgs package repository.
The core challenge lies in coordinating the necessary drivers, libraries, and daemons across both the host NixOS system and the containerized workloads within Kubernetes. The post meticulously outlines the steps involved, beginning with configuring the NixOS hosts. This includes installing the Nvidia driver, the CUDA toolkit, and related dependencies directly into the system's profile, ensuring they're available at boot. Critically, this avoids conflicts that might arise from installing these components within the Kubernetes cluster itself.
A key component of this setup is the use of the Nvidia Container Toolkit. This toolkit facilitates the sharing of the host's GPU resources with containers, enabling Kubernetes pods to leverage the GPU for accelerated computing tasks. The blog post explains the installation and configuration of this toolkit on the NixOS hosts, highlighting the importance of proper device access and permissions.
For orchestrating container deployments, the author opts for deploying a Kubernetes cluster using kubectl and a standard YAML manifest. This approach uses pre-built container images designed for CUDA development, ensuring compatibility and ease of deployment. To ensure the containers have access to the necessary GPU resources, the manifest includes specific configurations, including requesting GPU resources and mounting the necessary device paths. This setup allows users to define the required GPU resources directly in their pod specifications, ensuring proper allocation and usage.
The author then elaborates on using a privileged DaemonSet to deploy the Nvidia device plugin. This plugin is crucial for communicating available GPU resources to the Kubernetes scheduler, enabling intelligent scheduling of GPU-dependent workloads. The post details the configuration of this DaemonSet, including security considerations related to running a privileged pod. It explains that this approach allows the Kubernetes scheduler to be aware of the GPUs present on each node and schedule pods requesting GPU resources accordingly.
Finally, the blog post emphasizes the declarative and reproducible nature of the NixOS configuration. By defining the entire system configuration, including the Kubernetes cluster and GPU setup, in Nix code, the author ensures consistent deployments across different machines and facilitates easy reproducibility. This allows for easier maintenance, updates, and troubleshooting, as the entire system configuration can be easily replicated. The author highlights the benefits of this approach for managing complex infrastructure and minimizing configuration drift.
Summary of Comments ( 6 )
https://news.ycombinator.com/item?id=43234666
Hacker News users discussed various aspects of running Nvidia GPUs on a bare-metal NixOS Kubernetes cluster. Some questioned the necessity of NixOS for this setup, suggesting that its complexity might outweigh its benefits, especially for smaller clusters. Others countered that NixOS provides crucial advantages for reproducible deployments and managing driver dependencies, particularly valuable in research and multi-node GPU environments. Commenters also explored alternatives like using Ansible for provisioning and debated the performance impact of virtualization. A few users shared their personal experiences, highlighting both successes and challenges with similar setups, including issues with specific GPU models and kernel versions. Several commenters expressed interest in the author's approach to network configuration and storage management, but the author didn't elaborate on these aspects in the original post.
The Hacker News post titled "Nvidia GPU on bare metal NixOS Kubernetes cluster explained" (https://news.ycombinator.com/item?id=43234666) has a moderate number of comments, generating a discussion around the complexities and nuances of using NixOS with Kubernetes and GPUs.
Several commenters focus on the challenges and trade-offs of this specific setup. One commenter highlights the complexity of managing drivers, particularly the Nvidia driver, within NixOS and Kubernetes, questioning the overall maintainability and whether the benefits outweigh the added complexity. This sentiment is echoed by another commenter who mentions the difficulty of keeping drivers updated and synchronized across the cluster, suggesting that the approach might be more trouble than it's worth for smaller setups.
Another discussion thread centers around the choice of NixOS itself. One user questions the wisdom of using NixOS for Kubernetes, arguing that its immutability can conflict with Kubernetes' dynamic nature and that other, more established solutions might be more suitable. This sparks a counter-argument where a proponent of NixOS explains that its declarative configuration and reproducibility can be valuable assets for managing complex infrastructure, especially when dealing with things like GPU drivers and kernel modules. They emphasize that while there's a learning curve, the long-term benefits in terms of reliability and maintainability can be substantial.
The topic of hardware support and specific GPU models also arises. One commenter inquires about compatibility with consumer-grade GPUs, expressing interest in utilizing gaming GPUs for tasks like machine learning. Another comment thread delves into the specifics of PCI passthrough and the complexities of ensuring proper resource allocation and isolation within a Kubernetes environment.
Finally, there are some comments appreciating the author's effort in documenting their process. They acknowledge the value of sharing such specialized knowledge and the insights it provides into managing complex infrastructure setups involving NixOS, Kubernetes, and GPUs. One commenter specifically expresses gratitude for the detailed explanation of the networking setup, which they found particularly helpful.
In summary, the comments section reflects a mixture of skepticism and appreciation. While some users question the practicality and complexity of the approach, others recognize the potential benefits and value the author's contribution to sharing their experience and knowledge in navigating this complex technological landscape. The discussion highlights the ongoing challenges and trade-offs involved in integrating technologies like NixOS, Kubernetes, and GPUs for high-performance computing and machine learning workloads.