Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a scalable and flexible solution for serving models, allowing for easy integration with CI/CD pipelines and monitoring tools. Seldon Core supports multiple model frameworks and offers features like canary deployments, A/B testing, and advanced metrics.
When using Seldon Core, you might encounter load balancing issues where requests are not evenly distributed across model server instances. This can lead to some instances being overloaded while others remain underutilized, resulting in degraded performance and increased latency.
Load balancing issues in Seldon Core can arise due to several reasons. The most common root causes include:
Incorrect configuration of load balancing settings can lead to inefficient distribution of requests. Ensure that your load balancer is correctly set up to handle the traffic and distribute it evenly across all available instances.
If there are not enough instances to handle the incoming traffic, some instances may become overloaded. This can happen if the autoscaling policies are not properly configured or if there is a sudden spike in traffic.
To address load balancing issues in Seldon Core, follow these steps:
LoadBalancer
if using a cloud provider's load balancing service.kubectl get pods -n seldon
kubectl scale deployment --replicas= -n seldon
By ensuring proper load balancing configuration and maintaining sufficient instances, you can effectively resolve load balancing issues in Seldon Core. Regular monitoring and adjustment of your deployment settings will help maintain optimal performance and reliability of your model serving infrastructure.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)