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# Using GPU Workload in Welkin | ||
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!!! elastisys "For Elastisys Managed Services Customers" | ||
You can order a new Environment with GPU support by filing a [service ticket](https://elastisys.atlassian.net/servicedesk/). | ||
Make sure to specify the need for GPU Nodes in "Additional information or comments". | ||
If you are unsure, get in touch with your account manager. | ||
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As the demand for AI, machine learning, and data science workloads grows, Kubernetes provides a flexible and scalable platform to manage these applications. | ||
In this guide, we'll focus on how to use GPU in the Welkin platform. | ||
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> [!NOTE] | ||
> Not all infrastructure providers have support for GPU. | ||
> Check with the platform administrator to find out if your environment has support for GPU workload. | ||
## Deployment | ||
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To use GPU resources in your cluster, you need to create a deployment that is using the resource `nvidia.com/gpu`. | ||
Here's an example of how to configure GPU resources for a Pod: | ||
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```yaml | ||
apiVersion: v1 | ||
kind: Pod | ||
metadata: | ||
name: cuda-vectoradd | ||
spec: | ||
restartPolicy: OnFailure | ||
containers: | ||
- name: cuda-vectoradd | ||
image: "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1-ubuntu20.04" | ||
resources: | ||
limits: | ||
nvidia.com/gpu: 1 | ||
``` | ||
> [!NOTE] | ||
> If your cluster is using the cluster autoscaling feature and there's currently not enough resources, the autoscaler will create one for you. | ||
> It might take a couple of minutes for the new node to join the cluster and to install all the pre-requisites. | ||
### Further Reading | ||
- [Kubernetes Schedule GPU Documentation](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) | ||
- [Kubernetes Cluster Autoscaler Documentation](https://kubernetes.io/docs/concepts/cluster-administration/cluster-autoscaling/) | ||
- [Cluster Autoscaler FAQ](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md) |
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