- Release Notes
- Before you begin
- Getting started
- Installing AI Center
- Migration and upgrade
- Projects
- Datasets
- Data Labeling
- ML packages
- Out of the box packages
- Pipelines
- ML Skills
- ML Logs
- Document UnderstandingTM in AI Center
- AI Center API
- How to
- Licensing
- Basic Troubleshooting Guide
- AI Center troubleshooting
AI Center troubleshooting
When uploading dataset files, an error can occur.
When uploading dataset files, the following error can occur:
Failed to upload item(s), it may be due to a slow or lost internet connectionFailed to upload item(s), it may be due to a slow or lost internet connectionThis error message can show up because of some browser configurations.
-
Open the browser console and get the DNS of the
objectstore url. It will be in the form ofobjectstore.xxx.xx. - Make sure that the objectstore DNS is resolvable either by adding it to host file or talking to your network administrator.
- Once the DNS is resolved, if the certificate is not trusted, make sure you trust the certificate inside your browser before uploading the item.
Error on Pipelines pages even though permissions are in place for running pipelines
When trying to view or run pipelines, an error can be occur, even though permissions to run pipelines are in place.
DATABASECHANGELOGLOCK lock not being released by one service
On rare occasions, if you restart the machine two times consecutively, service deployment can get stuck because of the DATABASECHANGELOGLOCK lock not being released by one service. In this case you will see UiPath® AI Center pods restarting continuously.
UPDATE DATABASECHANGELOGLOCK SET LOCKED = 0, LOCKGRANTED = null, LOCKEDBY = nullUPDATE DATABASECHANGELOGLOCK SET LOCKED = 0, LOCKGRANTED = null, LOCKEDBY = nullAn error occurs when importing an ML Package
While importing an ML Package, the following error message can occur:
Please check projects or public ML Package combination in destinationPlease check projects or public ML Package combination in destinationsourcePackageName from the imported model's
metadata.json file and upload an out of the box ML Package (bundle)
using the same name.
An error occurs while installing AI Center connected to an external Orchestrator.
The following error message can occur while installing AI Center connected to an external Orchestrator:
curl: (92) HTTP/2 stream 0 was not closed cleanly: HTTP_1_1_REQUIRED (err 13)curl: (92) HTTP/2 stream 0 was not closed cleanly: HTTP_1_1_REQUIRED (err 13).
ML Skill deployment might fail after migrating to 2022.10
If there is an issue with your databases, you can recreate them from scratch directly post-installation.
You can do this by running an SQL command to drop all the DBs and recreate them as follows:
USE [master]
ALTER DATABASE [AutomationSuite_AICenter] SET SINGLE_USER WITH ROLLBACK IMMEDIATE
DROP DATABASE [AutomationSuite_AICenter]
CREATE DATABASE [AutomationSuite_AICenter]
GOUSE [master]
ALTER DATABASE [AutomationSuite_AICenter] SET SINGLE_USER WITH ROLLBACK IMMEDIATE
DROP DATABASE [AutomationSuite_AICenter]
CREATE DATABASE [AutomationSuite_AICenter]
GOWhen upgrading from 2023.4.3 to 2023.10, you run into issues with provisioning AI Center.
The system shows the following exception, and the tenant creation fails:
"exception":"sun.security.pkcs11.wrapper.PKCS11Exception:
CKR_KEY_SIZE_RANGE
Kubernetes operation failed to create secret, check that the secret is properly populated.
- Resync AICenter from ArgoCD, to populate the cluster credentials secrets.
- Restart the deployer deployment from ArgoCD or the virtual machine.
- Error when uploading dataset files
- Error on Pipelines page
- Service deployment is stuck
- Error when importing an ML Package
- Error while installing AI Center connected to external Orchestrator
- ML Skill deployment failed after migration
- Recreating databases
- AI Center provisioning failure after upgrading to 2023.10
- Description
- Solution
- Deployments and pipelines fail with error
