GARDEN
|
6 Troubleshooting Commands
Contributed by stewartshea
Codecollection:
Calculates SLI for GCP Vertex AI Model Garden health using Google Cloud Monitoring Python SDK.
Required IAM Roles:
- roles/monitoring.viewer (for metrics access)
- roles/logging.privateLogViewer (for quick log health check)
Required Permissions:
- monitoring.timeSeries.list
- logging.privateLogEntries.list
Tasks:
Tasks:
- Quick Vertex AI Log Health Check for `${GCP_PROJECT_ID}`
- Calculate Error Rate Score for `${GCP_PROJECT_ID}`
- Calculate Latency Performance Score for `${GCP_PROJECT_ID}`
- Calculate Throughput Usage Score for `${GCP_PROJECT_ID}`
- Check Service Availability Score for `${GCP_PROJECT_ID}`
- Generate Final Vertex AI Model Garden Health Score for `${GCP_PROJECT_ID}`
Source Code
Troubleshooting and remediation tasks for GCP Vertex AI Model Garden using Google Cloud Monitoring Python SDK.
Required IAM Roles:
- roles/monitoring.viewer (for metrics access)
- roles/logging.privateLogViewer (for audit logs access)
- roles/serviceusage.serviceUsageConsumer (for service status checks)
Required Permissions:
- monitoring.timeSeries.list
- logging.privateLogEntries.list
- serviceusage.services.list
Tasks:
Tasks:
- Analyze Vertex AI Model Garden Error Patterns and Response Codes in `GCP_PROJECT_ID`
- Investigate Vertex AI Model Latency Performance Issues in `GCP_PROJECT_ID`
- Monitor Vertex AI Throughput and Token Consumption Patterns in `GCP_PROJECT_ID`
- Check Vertex AI Model Garden API Logs for Issues in `GCP_PROJECT_ID`
- Check Vertex AI Model Garden Service Health and Quotas in `GCP_PROJECT_ID`
- Generate Vertex AI Model Garden Health Summary and Next Steps for `GCP_PROJECT_ID`