azure-mgmt-machinelearningcompute
Microsoft Azure Machine Learning Compute Management Client Library for Python
Decision gist · record as of 2026-08-14
Yes, if you are actively using Azure Machine Learning Compute and need programmatic management of clusters via Python. The package is stable, permissively licensed, and has no known vulnerabilities. However, verify that it aligns with your Azure SDK strategy—Microsoft has released newer, unified SDKs since 2018, so check whether your project should migrate to a more current client library to ensure long-term support.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Azure subscription credentials and appropriate AAD authentication setup; Python 2.7 or 3.4+ as tested in the package.
- Low install friction with three stable Azure dependencies.
- Package is actively maintained with recent commits, though the latest release is from 2018; suitable for production use if your Azure ML workflows haven't migrated to newer SDK versions.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.
last release 2018-05-29 (2999 days) · last repo commit 2026-08-14 · 5,588 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,647,429 downloads/mo, #3,690 on PyPI
Alternatives
Verify before relying
pip install azure-mgmt-machinelearningcompute
from azure.mgmt.machinelearningcompute import MachineLearningComputeManagementClient
from msrestazure.azure_active_directory import AADTokenCredentials
credentials = AADTokenCredentials(token, client_id)
client = MachineLearningComputeManagementClient(credentials, subscription_id)- Whether this package is still recommended for new Azure ML projects or if migration to newer unified SDK is required.
- Current support status for Python versions beyond 3.6, given the package's 2018 release date.
What it is and what it does
This package is a Python client library for the Azure Machine Learning Compute Management API, part of Microsoft's Azure SDK for Python. It wraps Azure Resource Manager (ARM) endpoints to let you programmatically provision, configure, and tear down machine learning compute resources—clusters, nodes, and associated infrastructure—directly from Python code.
The library depends on msrestazure, azure-common, and azure-mgmt-nspkg to handle authentication, HTTP communication, and namespace management. It's designed for developers building automation, infrastructure-as-code tools, or integration layers that need to manage Azure ML compute at scale. The package has been tested against Python 2.7 and 3.4–3.6, though it has not received updates since 2018.
Use it for
- Automate provisioning of Azure ML compute clusters as part of a CI/CD pipeline or infrastructure-as-code workflow.
- Build a multi-tenant ML platform that dynamically allocates compute resources based on workload demand.
- Delete and recycle compute clusters programmatically to control Azure spending and resource lifecycle.
- Integrate Azure ML cluster management into a custom orchestration or workflow tool written in Python.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using Azure Machine Learning Compute and need programmatic management of clusters via Python.
The package is stable, permissively licensed, and has no known vulnerabilities. However, verify that it aligns with your Azure SDK strategy—Microsoft has released newer, unified SDKs since 2018, so check whether your project should migrate to a more current client library to ensure long-term support.
Install
azure-mgmt-machinelearningcompute on PyPI
Before you install
Low install friction with three stable Azure dependencies. Package is actively maintained with recent commits, though the latest release is from 2018; suitable for production use if your Azure ML workflows haven't migrated to newer SDK versions.
Requires Azure subscription credentials and appropriate AAD authentication setup; Python 2.7 or 3.4+ as tested in the package.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects.
Quickstart
pip install azure-mgmt-machinelearningcompute
from azure.mgmt.machinelearningcompute import MachineLearningComputeManagementClient
from msrestazure.azure_active_directory import AADTokenCredentials
credentials = AADTokenCredentials(token, client_id)
client = MachineLearningComputeManagementClient(credentials, subscription_id)Verify before relying
- Whether this package is still recommended for new Azure ML projects or if migration to newer unified SDK is required.
- Current support status for Python versions beyond 3.6, given the package's 2018 release date.
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesmsrestazureazure-commonazure-mgmt-nspkg |
| Maintenance | Actively maintained 2,999 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,647,429 / month, #3,690 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6 |
Evidence: azure_mgmt_machinelearningcompute-0.4.1-py2.py3-none-any.whl
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See also azure-mgmt-devspaces · azure-mgmt-datalake-analytics · azure-mgmt-datalake-store · azure-mgmt-compute · azure-mgmt-redisenterprise · azure-mgmt-servicefabric · azure-mgmt-resource-templatespecs · azure-mgmt-core · azure-mgmt-hybridcompute · azure-mgmt-batchai