Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a robust infrastructure for scaling and managing models in production environments, leveraging Kubernetes' capabilities for orchestration and resource management.
When deploying a model using Seldon Core, you might encounter an error indicating that the resource quota has been exceeded. This typically manifests as a failure to deploy the model, with logs or error messages pointing to resource constraints.
"Error: resource quota exceeded""Failed to schedule pod: Insufficient resources"The "Resource quota exceeded" error occurs when the deployment requests more resources than are available within the namespace's quota. Kubernetes namespaces can have resource quotas set to limit the amount of CPU, memory, and other resources that can be consumed by the pods within them.
Resource quotas are defined in Kubernetes to ensure fair resource distribution and prevent any single deployment from monopolizing resources. They are specified in YAML files and applied to namespaces.
To resolve this issue, you can either adjust the resource requests and limits of your deployment or increase the namespace's resource quota.
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "1Gi"
cpu: "1"
kubectl apply -f your-deployment.yaml kubectl get resourcequota --namespace=your-namespace apiVersion: v1
kind: ResourceQuota
metadata:
name: example-quota
spec:
hard:
requests.cpu: "2"
requests.memory: "4Gi"
limits.cpu: "4"
limits.memory: "8Gi"
kubectl apply -f your-quota.yaml For more information on managing resources in Kubernetes, consider the following resources:
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
