QBO Kubernetes Engine (QKE) is a game-changer for anyone involved in ML and AI development. The use of Docker-in-Docker technology for deploying Kubernetes components is a brilliant move. It simplifies the complexities traditionally associated with virtual machines and ensures direct access to hardware resources, which is crucial for performance-intensive tasks. What particularly stands out is how QKE maintains the agility of cloud environments while delivering optimal performance, a balance that's often hard to achieve. It's clear that QBO is pushing the boundaries of what's possible in cloud-native environments for ML and AI workloads.
It does. Windows WSL2 + Nvidia Operator + Kubeflow Docs are here https://docs.qbo.io/#/ai_and_ml?id=nvidia-gpu-operator. Please note that support for WSL2 is new in Nvidia GPU Device Plugin and the PR is under testing before Nvidia releases it. Looks like RC comming next v0.15.0-rc.1 that should contain the PR.
QBO Kubernetes Engine (QKE) offers unparalleled performance for any ML and AI workloads, bypassing the constraints of traditional virtual machines. By deploying Kubernetes components using Docker-in-Docker technology, it grants direct access to hardware resources. This approach delivers the agility of the cloud while maintaining optimal performance.
In this blog post, we walk you through the setup process for Nvidia GPU Operator and Kubeflow in Docker in Docker (DinD) using QKE.