azure-ai-ml-py

原文涵盖的主题

• Azure Machine Learning SDK v2 for Python
• Installation
• Environment Variables
• Authentication & Lifecycle
• From Config File
• Workspace Management
• Create Workspace
• List Workspaces

Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute. 🔑 Two rules apply to every code sample below: 1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation. - Local dev: DefaultAzureCredential works as-is. - Production: set AZURETOKENCREDENTIALS=prod (or AZURETOKENCREDENTIALS= ) to constrain the credential chain to production-safe credentials. 2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically: - Sync: with (...) as client: - Async: async with (...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio) Snippets may abbreviate this setup, but production code should always follow both rules. Long-lived mlclient: Subsequent examples in this skill assume mlclient was created via the pattern above and is alive for the lifetime of your script. In production, wrap your top-level workflow in a single with MLClient(...) as mlclient: block so the underlying HTTP tr

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