aws-virtual-gpu-device-plugin
k8s-device-plugin
| aws-virtual-gpu-device-plugin | k8s-device-plugin | |
|---|---|---|
| 3 | 13 | |
| 132 | 3,411 | |
| - | 1.9% | |
| 0.0 | 9.2 | |
| almost 3 years ago | 5 days ago | |
| Jupyter Notebook | Go | |
| Apache License 2.0 | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
aws-virtual-gpu-device-plugin
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Share a GPU between pods on AWS EKS
This project (available here) uses the k8s device plugin described by this AWS blog post to make GPU-based nodes publish the amount of GPU resource they have available. Instead of the amount of VRAM available or some abstract metric, this plugin advertises the amount of pods/processes that can be connected to the GPU. This is controlled by what is called by NVIDIA as Multi-Process Service (MPS).
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[D] Serverless solutions for GPU inference (if there's such a thing)
AWS has apparently already started using this type of tech as of this year (see lost below). They mention virtual gpus but this particular solution probably won't help OP unfortunately. https://aws.amazon.com/blogs/opensource/virtual-gpu-device-plugin-for-inference-workload-in-kubernetes/
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AWS open source news and updates No.41
The post explores GPU device plugin to address how to set fractional number of GPU resource for each pod by implementing the Kubernetes device plugin and Nvidia MPS. This project has been open sourced on GitHub.
k8s-device-plugin
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Nvidia GPU on bare metal NixOS Kubernetes cluster explained
There's a bug in k8s-device-plugin that stops the plugin from even launching, as I mentioned in the article:
https://github.com/NVIDIA/k8s-device-plugin/issues/1182
And I opened a PR for fixing that here:
https://github.com/NVIDIA/k8s-device-plugin/pull/1183
I am unsure if this bug is only for the NixOS environment because its library paths and other quicks differ from those of major Linux distros.
Another major problem was that the "default_runtime_name" in the Containerd config didn't work as expected. I had to create a RuntimeClass and assign it to the pod to make it pick up the Nvidia runtime.
Other than that, I haven't tried K3S, the one I am running is a full-blown K8S cluster. I guess they should be similar.
While there's no guarantee, if you find any hints showing why your Nvidia plugin won't work here, I might be able to help, as I skip some minor issues I encountered in the articles. If it happens to be the ones I faced, I can share how I solved them.
- A practitioner's guide to testing and running GPU clusters
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Unlocking AI and ML Metal Performance with QBO Kubernetes Engine (QKE) Post
https://github.com/NVIDIA/k8s-device-plugin/issues/332#issue...
- Nos – Open-Source to Maximize GPU Utilization in Kubernetes
- Show HN: Nos – Open-Source to Maximize GPU Utilization in Kubernetes
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Time-Slicing GPUs with Karpenter
K8s-device-plugin
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Understanding Kubernetes Limits and Requests
This framework allows the use of external devices (e.g., NVIDIA GPUs, AMD GPUS, SR-IOV NICs) without modifying core Kubernetes components.
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Nvidia GPU Plugin: Am I really limited to one pod per GPU?
Not talking about MIG. NVIDIA device plugin. https://github.com/NVIDIA/k8s-device-plugin
- Nvidia Kubernetes plugin install option that does not require Helm?
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What is the difference between nvidia device plugin and GPU operator?
GPU Operator Device plugin
What are some alternatives?
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
kubevirt-gpu-device-plugin - NVIDIA k8s device plugin for Kubevirt
aws-eks-share-gpu - How to share the same GPU between pods on AWS EKS
asdf-awscli
terraform-provider-kubernetes - Terraform Kubernetes provider