AI Center troubleshooting

Error when uploading dataset files

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 connection

This error message can show up because of some browser configurations.

1. Open the browser console and get the DNS of the objectstore url. It will be in the form of objectstore.xxx.xx.
2. Make sure that the objectstore DNS is resolvable either by adding it to host file or talking to your network administrator.
3. 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 page

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.

In order to run and view pipelines, Read permissions on the ML Packages are mandatory.

Service deployment is stuck

Service deployment can get stuck because of the 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.

Run the following SQL command in the AI Center database to release the lock:

UPDATE DATABASECHANGELOGLOCK SET LOCKED = 0, LOCKGRANTED = null, LOCKEDBY = null

Error when importing an ML Package

An 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 destination

Get sourcePackageName from the imported model's metadata.json file and upload an out of the box ML Package (bundle) using the same name.

Error while installing AI Center connected to external Orchestrator

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)

.

Make sure that you are using TLS 1.2 and HTTP/2 before proceeding with the installation.

ML Skill deployment failed after migration

ML Skill deployment might fail after migrating to 2022.10

After upgrading to AI Center 2022.10.0 and moving to external storage, making a skill created in 2022.4 public will fail.

Note: This issue was fixed in 2022.10.2. To fix this, upgrade to a newer version.

For 2022.10.0, run this script to fix the issue.

Recreating databases

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]
GO

AI Center provisioning failure after upgrading to 2023.10

Description

When 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

Solution

To resolve this issue, you need to perform a rollout restart of the ai-trainer deployment. To do this, run the following command:

kubectl -n uipath rollout restart deploy ai-trainer-deployment

Deployments and pipelines fail with error

If you get the following error: Kubernetes operation failed to create secret, check that the secret is properly populated.

To recover:

1. Resync AICenter from ArgoCD, to populate the cluster credentials secrets.
2. Restart the deployer deployment from ArgoCD or the virtual machine.