automation-suite
2.2510
false
- Overview
- Requirements
- Deployment templates
- Manual: Preparing the installation
- Manual: Preparing the installation
- Step 2: Configuring the OCI-compliant registry for offline installations
- Step 3: Configuring the external objectstore
- Step 4: Configuring High Availability Add-on
- Step 5: Configuring SQL databases
- Step 7: Configuring the DNS
- Step 8: Configuring the disks
- Step 9: Configuring kernel and OS level settings
- Step 10: Configuring the node ports
- Step 11: Applying miscellaneous settings
- Step 12: Validating and installing the required RPM packages
- Cluster_config.json Sample
- General configuration
- Profile configuration
- Certificate configuration
- Database configuration
- External Objectstore configuration
- Pre-signed URL configuration
- ArgoCD configuration
- Kerberos authentication configuration
- External OCI-compliant registry configuration
- Disaster recovery: Active/Passive and Active/Active configurations
- High Availability Add-on configuration
- Orchestrator-specific configuration
- Insights-specific configuration
- Process Mining-specific configuration
- Document Understanding-specific configuration
- Automation Suite Robots-specific configuration
- AI Center-specific configuration
- Monitoring configuration
- Optional: Configuring the proxy server
- Optional: Enabling resilience to zonal failures in a multi-node HA-ready production cluster
- Optional: Passing custom resolv.conf
- Optional: Increasing fault tolerance
- Adding a dedicated agent node with GPU support
- Adding a Dedicated Agent Node for Automation Suite Robots
- Step 15: Configuring the temporary Docker registry for offline installations
- Step 16: Validating the prerequisites for the installation
- Manual: Performing the installation
- Post-installation
- Cluster administration
- Managing products
- Getting Started with the Cluster Administration portal
- Migrating Redis from in-cluster to external High Availability Add-on
- Migrating data between objectstores
- Migrating in-cluster objectstore to external objectstore
- Migrating from in-cluster registry to an external OCI-compliant registry
- Switching to the secondary cluster manually in an Active/Passive setup
- Disaster Recovery: Performing post-installation operations
- Converting an existing installation to multi-site setup
- Guidelines on upgrading an Active/Passive or Active/Active deployment
- Guidelines on backing up and restoring an Active/Passive or Active/Active deployment
- Scaling a single-node (evaluation) deployment to a multi-node (HA) deployment
- Monitoring and alerting
- Migration and upgrade
- Migrating between Automation Suite clusters
- Upgrading Automation Suite
- Downloading the installation packages and getting all the files on the first server node
- Retrieving the latest applied configuration from the cluster
- Updating the cluster configuration
- Configuring the OCI-compliant registry for offline installations
- Executing the upgrade
- Performing post-upgrade operations
- 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 clear Redis data
- 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 disable streaming logs for AI Center
- How to debug failed Automation Suite installations
- How to delete images from the old installer after upgrade
- How to disable TX checksum offloading
- How to manually set the ArgoCD log level to Info
- How to expand AI Center storage
- How to generate the encoded pull_secret_value for external registries
- How to address weak ciphers in TLS 1.2
- How to check the TLS version
- How to work with certificates
- How to schedule Ceph backup and restore data
- How to collect DU usage data with in-cluster objectstore (Ceph)
- How to install RKE2 SELinux on air-gapped environments
- How to clean up old differential backups on an NFS server
- Error in downloading the bundle
- Offline installation fails because of missing binary
- Certificate issue in offline installation
- SQL connection string validation error
- Azure disk not marked as SSD
- Failure after certificate update
- Antivirus causes installation issues
- Automation Suite not working after OS upgrade
- Automation Suite requires backlog_wait_time to be set to 0
- Temporary registry installation fails on RHEL 8.9
- Frequent restart issue in uipath namespace deployments during offline installations
- DNS settings not honored by CoreDNS
- Upgrade fails due to unhealthy Ceph
- RKE2 not getting started due to space issue
- Upgrade fails due to classic objects in the Orchestrator database
- Ceph cluster found in a degraded state after side-by-side upgrade
- Service upgrade fails for Apps
- In-place upgrade timeouts
- Upgrade fails in offline environments
- snapshot-controller-crds pod in CrashLoopBackOff state after upgrade
- Upgrade fails due to overridden Insights PVC sizes
- Setting a timeout interval for the management portals
- Authentication not working after migration
- 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 due to invalid status code
- Alarm received for failed Kerberos-tgt-update job
- SSPI provider: Server not found in Kerberos database
- Login failed for AD user due to disabled account
- ArgoCD login failed
- Update the underlying directory connections
- 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
- Missing Ceph-rook metrics from monitoring dashboards
- Mismatch in reported errors during diagnostic health checks
- No healthy upstream issue
- Redis startup blocked by antivirus
- Running High Availability with Process Mining
- Process Mining ingestion failed when logged in using Kerberos
- Unable to connect to AutomationSuite_ProcessMining_Warehouse database using a pyodbc format connection string
- Airflow installation fails with sqlalchemy.exc.ArgumentError: Could not parse rfc1738 URL from string ''
- How to add an IP table rule to use SQL Server port 1433
- Automation Suite certificate is not trusted from the server where CData Sync is running
- Running the diagnostics tool
- Using the Automation Suite support bundle
- Exploring Logs
- Exploring summarized telemetry
Automation Suite on Linux installation guide
Last updated Nov 13, 2025
If you are trying unsuccesfully to deploy a Document Understanding ML Skill on AI Center, check the following solutions.
If you are installing the Automation Suite offline, please double check if the Document Understanding bundle has been downloaded and installed.
The bundle includes the base image (e.g., model library) for the models to properly run on AI Center after uploading the ML Packages via AI Center UI.
For details about installing Document Understanding bundle, please refer to the documentation here. To add Document Understanding bundle, please follow the documentation to re-run the Document Understanding bundle installation.
Even if you have installed the Document Understanding bundle for offline installation, another issue might occur along with
this error message:
modulenotfounderror: no module named 'ocr.release'; 'ocr' is not a package.
When creating a Document Understanding OCR ML Package in AI Center, keep in mind that it cannot be named ocr or OCR, which conflicts with a folder in the package. Please make sure to choose another name.
Sometimes, intermittently, Document Understanding 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. The second time, the deployment is be faster as most of the deployment work of image building would have been done during the first attempt. Document Understanding ML Packages take around 1-1.5 hours for deploying the first time, and these are faster when deploying them again.
