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How to use workspace diagnostics

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APPLIES TO: Python SDK azureml v1

Important

This article provides information on using the Azure Machine Learning SDK v1. The SDK v1 is deprecated as of March 31, 2025 and support for it will end on June 30, 2026. You're able to install and use the SDK v1 until that date.

We recommend that you transition to the SDK v2 before June 30, 2026. For more information on the SDK v2, see What is the Azure Machine Learning Python SDK v2 and the SDK v2 reference.

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

  • An Azure Machine Learning workspace. For steps for creating a workspace, see Create the workspace.

  • The Azure Machine Learning SDK for Python v2. To install the SDK, use the following command:

    pip install azure-ai-ml azure-identity

    To update an existing installation of the SDK to the latest version, use the following command:

    pip install --upgrade azure-ai-ml azure-identity

    For more information, see Azure Machine Learning Package client library for Python.

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.

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.

APPLIES TO: Python SDK azure-ai-ml v2 (current)

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.

APPLIES TO: Python SDK azureml 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.

For more information, see the Workspace reference.

For more information, see the Workspace.diagnose_workspace() reference.

Next step

Manage Azure Machine Learning workspaces in the portal or with the Python SDK (v2)