Azure Machine Learning Compute Management

0.4.1 · abandoned · verified Sat Apr 11

The Microsoft Azure Machine Learning Compute Management Client Library for Python provides programmatic access to manage compute resources for Azure Machine Learning. It is a very old version (0.4.1) released in early 2019 and is considered abandoned, with its functionality largely superseded by newer Azure Machine Learning SDKs and resource management interfaces.

Warnings

breaking This library is effectively abandoned. Its last release was 0.4.1 in January 2019. It never reached a 1.0 stable version. Functionality it manages ('Operationalization Clusters') has been deprecated or fundamentally changed in modern Azure Machine Learning, making this library largely non-functional for current use cases.
Fix: Migrate to `azure-mgmt-machinelearningservices` (for resource management) or `azure-ai-ml` (for client operations) for current Azure Machine Learning functionalities.
gotcha Authentication for this old library typically relies on legacy methods like `msrestazure.azure_active_directory.ServicePrincipalCredentials` or `msrestazure.azure_active_directory.ApplicationTokenCredentials`. It does not natively support modern authentication mechanisms like `azure-identity.DefaultAzureCredential` without significant effort or custom adaptation, which is generally not recommended for an abandoned library.
Fix: If absolutely necessary to use, ensure `msrestazure` is installed and use `ServicePrincipalCredentials` with explicit `AZURE_TENANT_ID`, `AZURE_CLIENT_ID`, and `AZURE_CLIENT_SECRET` environment variables. Otherwise, migrate to a current Azure SDK.
deprecated The concepts and resources managed by `azure-mgmt-machinelearningcompute` (e.g., Operationalization Clusters) are largely obsolete. Azure Machine Learning has evolved significantly, and compute management is now handled through different APIs and resource models.
Fix: Utilize the modern Azure Machine Learning SDKs (e.g., `azure-ai-ml`) for data plane operations and `azure-mgmt-machinelearningservices` for management plane operations to interact with current Azure ML compute resources (e.g., Azure Machine Learning Compute instances, Kubernetes clusters, etc.).

Install

pip install azure-mgmt-machinelearningcompute Install latest version

Imports

MachineLearningComputeManagementClient

from azure.mgmt.machinelearningcompute import MachineLearningComputeManagementClient

Quickstart

Demonstrates instantiating the `MachineLearningComputeManagementClient` using `ServicePrincipalCredentials`, which was common for this library's era. It then attempts to list available management operations. Note that this library manages 'Operationalization Clusters', a concept largely deprecated in modern Azure Machine Learning. Running this example successfully may require a legacy Azure environment and careful credential setup.

import os
from msrestazure.azure_active_directory import ServicePrincipalCredentials
from azure.mgmt.machinelearningcompute import MachineLearningComputeManagementClient

# This library is very old and typically requires legacy authentication methods like Service Principal.
# Ensure AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, and AZURE_SUBSCRIPTION_ID are set.

tenant_id = os.environ.get("AZURE_TENANT_ID", "")
client_id = os.environ.get("AZURE_CLIENT_ID", "")
client_secret = os.environ.get("AZURE_CLIENT_SECRET", "")
subscription_id = os.environ.get("AZURE_SUBSCRIPTION_ID", "")

if not all([tenant_id, client_id, client_secret, subscription_id]):
    print("Please set AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, and AZURE_SUBSCRIPTION_ID environment variables.")
    print("This library uses legacy authentication that may not integrate with modern Azure Identity methods.")
else:
    try:
        credentials = ServicePrincipalCredentials(
            client_id=client_id,
            secret=client_secret,
            tenant=tenant_id
        )
        client = MachineLearningComputeManagementClient(credentials, subscription_id)

        print("Attempting to list available operations (might fail if resources are deprecated)...")
        operations = list(client.operations.list())
        if operations:
            print(f"Found {len(operations)} operations. First one: {operations[0].name}")
        else:
            print("No operations found or unable to list.")

    except Exception as e:
        print(f"An error occurred: {e}")
        print("This library is abandoned and its operations may no longer be functional or supported.")

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