azure-ai-ml-py

Files (3)

• SKILL.md
• capabilities.md · references/
• non-hero-scenarios.md · references/

SKILL.md (first lines)

# Azure Machine Learning SDK v2 for Python

Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.

## Installation

```bash
pip install azure-ai-ml
```

## Environment Variables

```bash
AZURE_SUBSCRIPTION_ID=<your-subscription-id>  # Required for all auth methods
AZURE_RESOURCE_GROUP=<your-resource-group>  # Required for all auth methods
AZURE_ML_WORKSPACE_NAME=<your-workspace-name>  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

## Authentication & Lifecycle

> **🔑 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 `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) 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 <Client>(...) as client:`
>    - Async: `async with <Client>(...) 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.

```python
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
import os

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with MLClient(
    credential=credential,
    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"],
    resource_group_name=os.environ["AZURE_RESOURCE_GROUP"],
    workspace_name=os.environ["AZURE_ML_WORKSPACE_NAME"]
) as ml_client:
    for ws in ml_client.workspaces.list():
        print(ws.name)
```

### From Config File

```python
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# Uses config.json in current directory or parent
with MLClient.from_config(
    credential=DefaultAzureCredential()
) as ml_client:
    for ws in ml_client.workspaces.list():
        print(ws.name)
```

> **Long-lived `ml_client`:** Subsequent examples in this skill assume `ml_client` 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 ml_client:` block so the underlying HTTP transport closes cleanly on exit.

## Workspace Management

### Create Workspace

```python
from azure.ai.ml.entities import Workspace

ws = Workspace(
    name="my-workspace",
    location="eastus",
    display_name="My Workspace",
    description="ML workspace for experiments",
    tags={"purpose": "demo"}
)

ml_client.workspaces.begin_create(ws).result()
```

### List Workspaces

```python
for ws in ml_client.workspaces.list():
    print(f"{ws.name}: {ws.location}")
```

## Data Assets

### Register Data

```python
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes

# Register a file
my_data = Data(
    name="my-dataset",
    version="1",
    path="azureml://datastores/workspaceblobstore/paths/data/train.csv",
    type=AssetTypes.URI_FILE,
    description="Training data"
)

ml_client.data.create_or_update(my_data)
```

### Register Folder

```python
my_data = Data(
    name="my-folder-dataset",
    version="1",

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