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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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عرض SKILL.md
| name | azure-ai-ml-py |
| description | Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | cloud-infrastructure |
| category | azure |
| risk | unknown |
| source | community |
| tags | ["skill","cloud-infrastructure","azure"] |
Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.
pip install azure-ai-ml
AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_RESOURCE_GROUP=<your-resource-group>
AZURE_ML_WORKSPACE_NAME=<your-workspace-name>
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 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()
)
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()
for ws in ml_client.workspaces.list():
print(f"{ws.name}: {ws.location}")
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)
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)
from azure.ai.ml.entities import Model
from azure.ai.ml.constants import AssetTypes
model = Model(
name="my-model",
version="1",
path="./model/",
type=AssetTypes.CUSTOM_MODEL,
description="My trained model"
)
ml_client.models.create_or_update(model)
for model in ml_client.models.list(name="my-model"):
print(f"{model.name} v{model.version}")
from azure.ai.ml.entities import AmlCompute
cluster = AmlCompute(
name="cpu-cluster",
type="amlcompute",
size="Standard_DS3_v2",
min_instances=0,
max_instances=4,
idle_time_before_scale_down=120
)
ml_client.compute.begin_create_or_update(cluster).result()
for compute in ml_client.compute.list():
print(f"{compute.name}: {compute.type}")
from azure.ai.ml import command, Input
job = command(
code="./src",
command="python train.py --data ${{inputs.data}} --lr ${{inputs.learning_rate}}",
inputs={
"data": Input(type="uri_folder", path="azureml:my-dataset:1"),
"learning_rate": 0.01
},
environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest",
compute="cpu-cluster",
display_name="training-job"
)
returned_job = ml_client.jobs.create_or_update(job)
print(f"Job URL: {returned_job.studio_url}")
ml_client.jobs.stream(returned_job.name)
from azure.ai.ml import dsl, Input, Output
from azure.ai.ml.entities import Pipeline
@dsl.pipeline(
compute="cpu-cluster",
description="Training pipeline"
)
def training_pipeline(data_input):
prep_step = prep_component(data=data_input)
train_step = train_component(
data=prep_step.outputs.output_data,
learning_rate=0.01
)
return {"model": train_step.outputs.model}
pipeline = training_pipeline(
data_input=Input(type="uri_folder", path="azureml:my-dataset:1")
)
pipeline_job = ml_client.jobs.create_or_update(pipeline)
from azure.ai.ml.entities import Environment
env = Environment(
name="my-env",
version="1",
image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04",
conda_file="./environment.yml"
)
ml_client.environments.create_or_update(env)
for ds in ml_client.datastores.list():
print(f"{ds.name}: {ds.type}")
default_ds = ml_client.datastores.get_default()
print(f"Default: {default_ds.name}")
| Property | Operations |
|---|---|
workspaces | create, get, list, delete |
jobs | create_or_update, get, list, stream, cancel |
models | create_or_update, get, list, archive |
data | create_or_update, get, list |
compute | begin_create_or_update, get, list, delete |
environments | create_or_update, get, list |
datastores | create_or_update, get, list, get_default |
components | create_or_update, get, list |
This skill is applicable to execute the workflow or actions described in the overview.