automation-suite
2023.4
false
- Overview
- Requirements
- Installation
- Q&A: Deployment templates
- Configuring the machines
- Configuring the external objectstore
- Configuring an external Docker registry
- Configuring the load balancer
- Configuring the DNS
- Configuring Microsoft SQL Server
- Configuring the certificates
- Online multi-node HA-ready production installation
- Offline multi-node HA-ready production installation
- Disaster recovery - Installing the secondary cluster
- Downloading the installation packages
- install-uipath.sh parameters
- Enabling Redis High Availability Add-On for the cluster
- Document Understanding configuration file
- Adding a dedicated agent node with GPU support
- Adding a dedicated agent Node for Task Mining
- Connecting Task Mining application
- Adding a Dedicated Agent Node for Automation Suite Robots
- Post-installation
- Cluster administration
- Monitoring and alerting
- Migration and upgrade
- Migration options
- Step 1: Moving the Identity organization data from standalone to Automation Suite
- Step 2: Restoring the standalone product database
- Step 3: Backing up the platform database in Automation Suite
- Step 4: Merging organizations in Automation Suite
- Step 5: Updating the migrated product connection strings
- Step 6: Migrating standalone Insights
- Step 7: Deleting the default tenant
- B) Single tenant migration
- 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 bundle
- 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 automatically clean up Longhorn snapshots
- How to disable TX checksum offloading
- How to manually set the ArgoCD log level to Info
- How to generate the encoded pull_secret_value for external registries
- How to address weak ciphers in TLS 1.2
- 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
- First installation fails during Longhorn setup
- SQL connection string validation error
- Prerequisite check for selinux iscsid module fails
- 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
- GPU node affected by resource unavailability
- Volume unable to mount due to not being ready for workloads
- Support bundle log collection failure
- Failure to upload or download data in objectstore
- PVC resize does not heal Ceph
- Failure to resize PVC
- Failure to resize objectstore PVC
- Rook Ceph or Looker pod stuck in Init state
- StatefulSet volume attachment error
- Failure to create persistent volumes
- Storage reclamation patch
- Backup failed due to TooManySnapshots error
- All Longhorn replicas are faulted
- Setting a timeout interval for the management portals
- Update the underlying directory connections
- 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
- 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
- Issues accessing the ArgoCD read-only account
- MongoDB pods in CrashLoopBackOff or pending PVC provisioning after deletion
- Unhealthy services after cluster restore or rollback
- Pods stuck in Init:0/X
- Prometheus in CrashloopBackoff state with out-of-memory (OOM) error
- Missing Ceph-rook metrics from monitoring dashboards
- 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
- Using the Automation Suite Diagnostics Tool
- Using the Automation Suite Support Bundle Tool
- Exploring Logs
Document Understanding configuration file
Automation Suite on Linux Installation Guide
Last updated Sep 5, 2024
Document Understanding configuration file
documentunderstanding
is a property in the Automation Suite's configuration file, cluster_config.json
. It contains configurable values that control the behavior of the Document Understanding service. The installer generates
the default values. Additional changes can be made to further configure the Document Understanding service. If you need to
change any settings related to Document Understanding, the documentunderstanding
section in cluster_config.json
can be edited and the installer can be re-run.
Alternatively, the same changes can be made in the UiPath app in ArgoCD.
"documentunderstanding": {
"enabled": Boolean,
"datamanager": {
"sql_connection_str" : "String"
}
"handwriting": {
"enabled": Boolean,
"max_cpu_per_pod": "Number"
}
}
"documentunderstanding": {
"enabled": Boolean,
"datamanager": {
"sql_connection_str" : "String"
}
"handwriting": {
"enabled": Boolean,
"max_cpu_per_pod": "Number"
}
}
Note:
The data manager SQL connection string is optional only if you want to overwrite the default database with your own.
Handwriting is always enabled for online installation.
"documentunderstanding": {
"enabled": true,
"datamanager": {
"sql_connection_str": "mssql+pyodbc://testadmin:myPassword@mydev-sql.database.windows.net:1433/datamanager?driver=ODBC+Driver+17+for+SQL+Server",
},
"handwriting": {
"enabled": true,
"max_cpu_per_pod": "2"
}
}
"documentunderstanding": {
"enabled": true,
"datamanager": {
"sql_connection_str": "mssql+pyodbc://testadmin:myPassword@mydev-sql.database.windows.net:1433/datamanager?driver=ODBC+Driver+17+for+SQL+Server",
},
"handwriting": {
"enabled": true,
"max_cpu_per_pod": "2"
}
}
Note: The value for
max_cpu_per_pod
is by default 2
, but it can be adjusted according to your needs. For more information on how to do this, see the (optional) max CPU per pod Parameter section.
- Connection string for datamanager
- Required: False.
- This property is generated and populated by the installer, you do not need to set it unless you want to override the default connection string. For more details about connecting to SQL please refer to the Using the configuration file page.
- Settings for the handwriting recognition functionality (part of IntelligentFormExtractor)
- Required: False.
- Setting this to true creates the resources necessary for performing handwriting recognition. This needs to be true to use IntelligentFormExtractor.
- Required: False
- This property is always enabled for online installation, and disabled for offline (air-gapped) installation. For air-gapped installation, you need to install the Document Understanding offline bundle before enabling handwriting.
- The maximum amount of CPUs each container is allowed to use. The recommended value is 2.
- Required: False.
- Default: 2.
If you plan to use Intelligent Form Extractor with handwriting detection feature, you may need to adjust the
handwriting.max_cpu_per_pod
parameter for more processing power.
The following factors are required to calculate the right sizing:
- total volume of documents/year = V
- expected number of handwriting shreds/doc = S
- days in which the workflow processes documents (workdays, all days, weekends, etc) = d
- hours in which the workflow processes documents = h
- Number of CPUs = (V x S / (d x h)) / 1500
As an example, if you expect to have 1 million documents to process for a year using Intelligent Form Extractor for handwriting detection, with 50 shreds on average, running weekdays from 00:00 to 08:00 (8hr), the calculation would be:
Number of CPUs = (1,000,000 x 50 / (250 x 8)) / 1500
= 25,000 / 1500
= 17 CPUs
Number of CPUs = (1,000,000 x 50 / (250 x 8)) / 1500
= 25,000 / 1500
= 17 CPUs
For the single-node evaluation mode, you need to adjust the
max_cpu_per_pod
parameter to 17.
For the multi-node HA-ready production mode (3 nodes), adjust the
max_cpu_per_pod
parameter to 5-6.