azure-ai-ml-py

Azure Machine Learning SDK v2 for Python

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

Installation

pip install azure-ai-ml

Environment Variables

AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_RESOURCE_GROUP=<your-resource-group>
AZURE_ML_WORKSPACE_NAME=<your-workspace-name>

Authentication

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

ml_client = MLClient(
    credential=DefaultAzureCredential(),
    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"],
    resource_group_name=os.environ["AZURE_RESOURCE_GROUP"],
    workspace_name=os.environ["AZURE_ML_WORKSPACE_NAME"]
)

From Config File

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

# Uses config.json in current directory or parent
ml_client = MLClient.from_config(
    credential=DefaultAzureCredential()
)

Workspace Management

Create Workspace

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

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

Data Assets

Register Data

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

my_data = Data(
    name="my-folder-dataset",
    version="1",
    path="azureml://datastores/workspaceblobstore/paths/data/",
    type=AssetTypes.URI_FOLDER
)

ml_client.data.create_or_update(my_data)