- Overview
- Requirements
- Installation
- Post-installation
- Cluster administration
- Monitoring and alerting
- Migration and upgrade
- Product-specific configuration
- Best practices and maintenance
- Troubleshooting
- How to Troubleshoot Services During Installation
- How to Uninstall the Cluster
- How to clean up offline artifacts to improve disk space
- How to disable TLS 1.0 and 1.1
- How to enable Istio logging
- How to manually clean up logs
- How to clean up old logs stored in the sf-logs bucket
- How to debug failed Automation Suite installations
- How to disable TX checksum offloading
- Unable to run an offline installation on RHEL 8.4 OS
- Error in Downloading the Bundle
- Offline installation fails because of missing binary
- Certificate issue in offline installation
- SQL connection string validation error
- Failure After Certificate Update
- Automation Suite Requires Backlog_wait_time to Be Set 1
- Cannot Log in After Migration
- Setting a timeout interval for the management portals
- Update the underlying directory connections
- Kinit: Cannot Find KDC for Realm <AD Domain> While Getting Initial Credentials
- Kinit: Keytab Contains No Suitable Keys for *** While Getting Initial Credentials
- GSSAPI Operation Failed With Error: An Invalid Status Code Was Supplied (Client's Credentials Have Been Revoked).
- Login Failed for User <ADDOMAIN><aduser>. Reason: The Account Is Disabled.
- Alarm Received for Failed Kerberos-tgt-update Job
- SSPI Provider: Server Not Found in Kerberos Database
- Failure to get the sandbox image
- Pods not showing in ArgoCD UI
- Redis Probe Failure
- RKE2 Server Fails to Start
- Secret Not Found in UiPath Namespace
- ArgoCD goes into progressing state after first installation
- Unexpected Inconsistency; Run Fsck Manually
- Missing Self-heal-operator and Sf-k8-utils Repo
- Degraded MongoDB or Business Applications After Cluster Restore
- Unhealthy Services After Cluster Restore or Rollback
- AI Center skills deployment issues
- Enabling AI Center on the restored cluster
- Using the Automation Suite Diagnostics Tool
- Using the Automation Suite support bundle
- Exploring Logs
AI Center skills deployment issues
Sometimes intermittently DU Model Skill Deployments can fail with Failed to list deployment or Unknown Error when deploying the model for the first time. The workaround is to try deploying the model again. Second time, it will be faster as most of the deployment work of image building would have been done during the first attempt. DU Models takes around 1-1.5 hours for deploying first time, and it will be faster when deploying them again.
In a rare scenario, due to cluster state, asynchronous operations like Skill Deployment or Package upload could be stuck for a long time. If DU Skill deployment is taking more than 2-3 hours, try deploying a simpler model (e.g, TemplateModel). If the model also takes more than an hour, then the mitigation is to restart AI Center services with the following commands:
kubectl -n uipath rollout restart deployment ai-deployer-deployment
kubectl -n uipath rollout restart deployment ai-trainer-deployment
kubectl -n uipath rollout restart deployment ai-pkgmanager-deployment
kubectl -n uipath rollout restart deployment ai-helper-deployment
kubectl -n uipath rollout restart deployment ai-appmanager-deploymentkubectl -n uipath rollout restart deployment ai-deployer-deployment
kubectl -n uipath rollout restart deployment ai-trainer-deployment
kubectl -n uipath rollout restart deployment ai-pkgmanager-deployment
kubectl -n uipath rollout restart deployment ai-helper-deployment
kubectl -n uipath rollout restart deployment ai-appmanager-deploymentWait for the AI Center pods to be back up by verifying with the following command:
kubectl -n uipath get pods | grep ai-*kubectl -n uipath get pods | grep ai-*All the above pods should be Running state with container state shown as 2/2.
