Seldon Core Metrics not being collected

Prometheus not scraping the model metrics endpoint.

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

What is

Seldon Core Metrics not being collected

?

Understanding Seldon Core

Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a robust framework for scaling, monitoring, and managing machine learning models in production environments. One of its key features is the ability to integrate with monitoring tools like Prometheus to collect metrics from deployed models.

Identifying the Symptom

When using Seldon Core, you might encounter a situation where metrics are not being collected as expected. This can manifest as missing data in your monitoring dashboards or alerts not being triggered due to the absence of metrics.

Common Observations

Empty or incomplete metrics data in Prometheus.
Grafana dashboards showing no data or gaps in data.
Alerts based on metrics not firing.

Exploring the Issue

The root cause of metrics not being collected is often due to Prometheus not scraping the model metrics endpoint. This can happen if the endpoint is not correctly exposed or if Prometheus is not configured to scrape it.

Why This Happens

Prometheus relies on a configuration file to determine which endpoints to scrape for metrics. If the Seldon Core model's metrics endpoint is not included in this configuration, or if there are network issues preventing access, metrics will not be collected.

Steps to Fix the Issue

To resolve this issue, follow these steps to ensure that Prometheus is correctly configured to scrape the Seldon Core model metrics endpoint.

Step 1: Verify Endpoint Exposure

Ensure that the model's metrics endpoint is exposed. You can check this by accessing the endpoint directly in your browser or using a tool like curl:

curl http:///metrics

If the endpoint is not accessible, you may need to adjust your Kubernetes service or ingress configuration.

Step 2: Update Prometheus Configuration

Edit the Prometheus configuration file to include the model's metrics endpoint. This file is typically named prometheus.yml and is located in the Prometheus server's configuration directory. Add a new scrape job for your Seldon Core model:

scrape_configs: - job_name: 'seldon-model' static_configs: - targets: [':']

Replace <model-service-url> and <port> with the appropriate values for your deployment.

Step 3: Reload Prometheus Configuration

After updating the configuration, reload Prometheus to apply the changes. This can typically be done by sending a SIGHUP signal to the Prometheus process or using the Prometheus web interface to reload the configuration.

Additional Resources

By following these steps, you should be able to resolve the issue of metrics not being collected in Seldon Core. Ensure that your Prometheus configuration is up-to-date and that all endpoints are correctly exposed and accessible.

Attached error:

Seldon Core Metrics not being collected

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Master

Seldon Core

debugging in Minutes

— Grab the Ultimate Cheatsheet

(Perfect for DevOps & SREs)

Most-used commands

Real-world configs/examples

Handy troubleshooting shortcuts

Thankyou for your submission

We have sent the cheatsheet on your email!

Oops! Something went wrong while submitting the form.

Seldon Core

Cheatsheet

(Perfect for DevOps & SREs)

Most-used commands

Thankyou for your submission

We have sent the cheatsheet on your email!

Oops! Something went wrong while submitting the form.

MORE ISSUES

Seldon Core Model server reliability issues

Lack of redundancy or fault tolerance leading to reliability issues.

Seldon Core Model server scalability issues

Inadequate scalability planning or misconfigured scaling settings.

Seldon Core Model server performance issues

Inefficient code or resource bottlenecks affecting performance.

Seldon Core Model server testing issues

Inadequate testing leading to undetected issues.

Seldon Core Inconsistent model behavior due to configuration discrepancies.

Lack of configuration management leading to inconsistencies.

Seldon Core Model server deployment issues

Incorrect deployment procedures or misconfigured deployment settings.

Seldon Core Model server dependency management issues

Poor dependency management leading to conflicts or missing dependencies.

Seldon Core Model server upgrade issues

Incompatibility between new and old versions or incorrect upgrade procedures.

Seldon Core Model server rollback issues

Improper rollback procedures or lack of version control.

Seldon Core Model server backup issues

Inadequate backup procedures or misconfigured backup settings.

Seldon Core Model server monitoring issues

Lack of monitoring tools or misconfigured monitoring settings.

Seldon Core Model server logging issues

Misconfigured logging settings or insufficient log retention.

Seldon Core Model server load balancing issues

Improper load balancing configuration or insufficient instances.

Seldon Core Model server API version mismatch

Incompatibility between client and server API versions.

Seldon Core Model server storage issues

Insufficient storage space or misconfigured storage settings.

Seldon Core Model server security vulnerabilities

Unpatched vulnerabilities or insecure configurations.

Seldon Core Model server dependency version conflicts

Incompatible versions of dependencies causing conflicts.

Seldon Core Model server network issues

Network connectivity problems or misconfigured network settings.

Seldon Core Model server configuration errors

Incorrect or incomplete configuration settings.

Seldon Core Model server resource limits exceeded

Resource-intensive operations exceeding allocated limits.

Seldon Core Explainer not providing explanations

Explainer configuration issues or model incompatibility.

Seldon Core Model server scaling issues

Incorrect scaling policies or lack of metrics.

Seldon Core Model server environment variable issues

Missing or incorrectly set environment variables.

Seldon Core Model server health check failures

Health check endpoint misconfiguration or server unresponsiveness.

Seldon Core Model server port conflicts

Port already in use by another process or service.

Seldon Core Model server dependency errors

Missing or incompatible dependencies in the model server environment.

Seldon Core Invalid model URI

The model URI is incorrectly specified or inaccessible.

Seldon Core Model server timeout

Long processing time or network latency.

Seldon Core Resource quota exceeded

Deployment exceeds the namespace's resource quota.

Seldon Core Model server not starting

Startup script errors or missing dependencies.

Seldon Core Model server image pull errors

Incorrect image name or lack of permissions to pull the image.

Seldon Core Model server logs not accessible

Logging configuration issues or insufficient permissions.

Seldon Core Data drift detection not working

Incorrect configuration of data drift detection parameters.

Seldon Core Model server not responding

Model server process is not running or is unresponsive.

Seldon Core Model server not scaling

HPA (Horizontal Pod Autoscaler) misconfiguration or missing metrics.

Seldon Core Custom resource definition (CRD) not found

CRD not installed or incorrectly defined.

Seldon Core Authentication errors with model endpoint

Missing or incorrect authentication tokens or headers.

Seldon Core Canary deployment not functioning

Incorrect traffic split configuration or missing canary definition.

Seldon Core Metrics not being collected

Prometheus not scraping the model metrics endpoint.

Seldon Core Model versioning not working

Incorrect version labels or annotations in the SeldonDeployment.

Seldon Core Ingress not routing traffic

Ingress resource misconfiguration or missing annotations.

Seldon Core Seldon Core Operator not running

Operator pod is not scheduled or has crashed.

Seldon Core Model prediction latency is high

Resource bottlenecks or inefficient model code.

Seldon Core Model server crashing

Insufficient memory or CPU resources allocated to the model server.

Seldon Core Invalid REST request format

The request payload does not match the expected input format for the model.

Seldon Core gRPC request failure

Incorrect gRPC service definition or request format.

Seldon Core Connection refused to model endpoint

Network policies or service misconfiguration blocking access.

Seldon Core Model not loading in container

Model file path is incorrect or the model file is missing.

Seldon Core Pods stuck in Pending state

Insufficient resources or node affinity/taints preventing pod scheduling.

Seldon Core Failed to create SeldonDeployment

The SeldonDeployment resource definition is incorrect or missing required fields.

Backed by

Resources

Contact

Platform

Connect

Deep Sea Tech Inc. — Made with ❤️ in & 🏢

Doctor Droid