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how-to-workspace-diagnostic-api.md

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title titleSuffix description services ms.service ms.subservice ms.author author ms.reviewer ms.date ms.topic ms.custom monikerRange
How to use workspace diagnostics
Azure Machine Learning
Learn how to use Azure Machine Learning workspace diagnostics in the Azure portal or with the Python SDK.
machine-learning
machine-learning
enterprise-readiness
jhirono
jhirono
larryfr
03/27/2024
how-to
sdkv2, devx-track-python
azureml-api-2 || azureml-api-1

How to use workspace diagnostics

:::moniker range="azureml-api-2" [!INCLUDE sdk v2] :::moniker-end :::moniker range="azureml-api-1" [!INCLUDE sdk v1] :::moniker-end

Azure Machine Learning provides a diagnostic API that can be used to identify problems with your workspace. Errors returned in the diagnostics report include information on how to resolve the problem.

You can use the workspace diagnostics from the Azure Machine Learning studio or Python SDK.

Prerequisites

:::moniker range="azureml-api-2" [!INCLUDE sdk] :::moniker-end :::moniker range="azureml-api-1"

Diagnostics from studio

From the Azure Machine Learning studio, you can run diagnostics on your workspace to check your setup. To run diagnostics, select the '?' icon in the upper right corner of the page. Then select Run workspace diagnostics.

:::image type="content" source="./media/how-to-workspace-diagnostic-api/diagnostics.png" alt-text="Screenshot of the workspace diagnostics button.":::

After diagnostics run, a list of any detected problems is returned. This list includes links to possible solutions.

Diagnostics from Python

The following snippet demonstrates how to use workspace diagnostics from Python.

:::moniker range="azureml-api-2" [!INCLUDE sdk v2]

from azure.ai.ml import MLClient
from azure.ai.ml.entities import Workspace
from azure.identity import DefaultAzureCredential

subscription_id = '<your-subscription-id>'
resource_group = '<your-resource-group-name>'
workspace = '<your-workspace-name>'

ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
resp = ml_client.workspaces.begin_diagnose(workspace).result()
# Inspect the attributes of the response you are interested in
for result in resp.application_insights_results:
    print(f"Diagnostic result: {result.code}, {result.level}, {result.message}")

The response is a DiagnoseResponseResultValue object that contains information on any problems detected with the workspace. :::moniker-end :::moniker range="azureml-api-1" [!INCLUDE sdk v1]

from azureml.core import Workspace

ws = Workspace.from_config()

diag_param = {
      "value": {
      }
    }

resp = ws.diagnose_workspace(diag_param)
print(resp)

The response is a JSON document that contains information on any problems detected with the workspace. The following JSON is an example response:

{
    "value": {
        "user_defined_route_results": [],
        "network_security_rule_results": [],
        "resource_lock_results": [],
        "dns_resolution_results": [{
            "code": "CustomDnsInUse",
            "level": "Warning",
            "message": "It is detected VNet '/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Network/virtualNetworks/<virtual-network-name>' of private endpoint '/subscriptions/<subscription-id>/resourceGroups/<myresourcegroup>/providers/Microsoft.Network/privateEndpoints/<workspace-private-endpoint>' is not using Azure default DNS. You need to configure your DNS server and check https://learn.microsoft.com/azure/machine-learning/how-to-custom-dns to make sure the custom DNS is set up correctly."
        }],
        "storage_account_results": [],
        "key_vault_results": [],
        "container_registry_results": [],
        "application_insights_results": [],
        "other_results": []
    }
}

If no problems are detected, an empty JSON document is returned. :::moniker-end

:::moniker range="azureml-api-2" For more information, see the Workspace reference. :::moniker-end :::moniker range="azureml-api-1" For more information, see the Workspace.diagnose_workspace() reference. :::moniker-end

Next step

[!div class="nextstepaction"] Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)