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How to troubleshoot your deployments and monitors in Azure AI Studio

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Note

Azure AI Studio is currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

This article provides instructions on how to troubleshoot your deployments and monitors in Azure AI Studio.

Deployment issues

For the general deployment error code reference, you can go to the Azure Machine Learning documentation. Much of the information there also applies to Azure AI Studio deployments.

Question: I got the following error message. What should I do? "Use of Azure OpenAI models in Azure Machine Learning requires Azure OpenAI Services resources. This subscription or region doesn't have access to this model."

Answer: You might not have access to this particular Azure OpenAI model. For example, your subscription might not have access to the latest GPT model yet or this model isn't offered in the region you want to deploy to. You can learn more about it on Azure OpenAI Service models.

Question: I got an "out of quota" error message. What should I do?

Answer: For more information about managing quota, see:

Question: After I deployed a prompt flow, I got an error message "Tool load failed in 'search_question_from_indexed_docs': (ToolLoadError) Failed to load package tool 'Vector Index Lookup': (HttpResponseError) (AuthorizationFailed)". How can I resolve this?

Answer: You can follow this instruction to manually assign ML Data scientist role to your endpoint to resolve this issue. It might take several minutes for the new role to take effect.

  1. Go to your project and select AI project settings from the left menu.
  2. Select the link to your resource group.
  3. Once you're redirected to the resource group in Azure portal, Select Access control (IAM) on the left navigation menu.
  4. Select Add role assignment.
  5. Select Azure ML Data Scientist and select Next.
  6. Select Managed Identity.
  7. Select + Select members.
  8. Select Machine Learning Online Endpoints in the Managed Identity dropdown field.
  9. Select your endpoint name.
  10. Select Select.
  11. Select Review + Assign.
  12. Return to AI Studio and go to the deployment details page (YourProject > Deployments > YourDeploymentName).
  13. Test the prompt flow deployment.

Question: I got the following error message about the deployment failure. What should I do to troubleshoot?

ResourceNotFound: Deployment failed due to timeout while waiting for Environment Image to become available. Check Environment Build Log in ML Studio Workspace or Workspace storage for potential failures. Image build summary: [N/A]. Environment info: Name: CliV2AnonymousEnvironment, Version: 'Ver', you might be able to find the build log under the storage account 'NAME' in the container 'CONTAINER_NAME' at the Path 'PATH/PATH/image_build_aggregate_log.txt'.

You might have come across an ImageBuildFailure error: This happens when the environment (docker image) is being built. For more information about the error, you can check the build log for your <CONTAINER NAME> environment.

Answer: These error messages refer to a situation where the deployment build failed. You want to read the build log to troubleshoot further. There are two ways to access the build log.

Option 1: Find the build log for the Azure default blob storage.

  1. Go to your project in Azure AI Studio and select the settings icon on the lower left corner.
  2. Select your Azure AI hub resource name under Resource configurations on the AI project settings page.
  3. On the Azure AI hub overview page, select your storage account name. This should be the name of storage account listed in the error message you received. You'll be taken to the storage account page in the Azure portal.
  4. On the storage account page, select Containers under Data Storage on the left menu.
  5. Select the container name listed in the error message you received.
  6. Select through folders to find the build logs.

Option 2: Find the build log within Azure Machine Learning studio, which is a separate portal from Azure AI Studio.

  1. Go to Azure Machine Learning studio.
  2. Select Endpoints on the left navigation menu.
  3. Select your endpoint name. It might be identical to your deployment name.
  4. Select the Environment link in the deployment section.
  5. Select Build log on the top of the environment details page.

Question: I got an error message "UserErrorFromQuotaService: Simultaneous count exceeded for subscription". What does it mean and how can I resolve it?

Answer: This error message means the shared quota pool has reached the maximum number of requests it can handle. Try again at a later time when the shared quota is freed up for use.

Question: I deployed a web app but I don't see a way to launch it or find it.

Answer: We're working on improving the user experience of web app deployment at this time. For the time being, here's a tip: if your web app launch button doesn't become active after a while, try deploy again using the 'update an existing app' option. If the web app was properly deployed, it should show up on the dropdown list of your existing web apps.

Question: I deployed a model but I don't see it in the playground. Answer: Playground only supports a few select models, such as Azure OpenAI models and Llama-2. If playground support is available, you see the Open in playground button on the model deployment's Details page.

Next steps