Path1.Mod2.c - Explore Workspace Developer Tools - Python SDK

Question 1

The command for installing the Python SDK package (using pip)

Answer 1

pip install azure-ai-ml

Question 2

The min Python version required to install the Python ML SDK.

The Python ML SDK is already installed under these conditions

Answer 2

Minimum Python 3.7 to install the SDK

When using notebooks in Azure ML Studio and using Python 3.10 or later

Question 3

MLClient class usage and four required Parameters

Answer 3

Use the MLClient constructor to authenticate and get a connected instance from your environment to your Workspace:

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

ml_client = MLClient(
    DefaultAzureCredential(), 
subscription_id, 
resource_group, 
workspace
)

Required Parameters:
- a TokenCredential instance (generally can use DefaultAzureCredential())
- subscription_id
- resource_group
- workspace_name

Question 4

The purpose of having an MLClient instance connected to your Workspace

Answer 4

It authenticates your Environment, enabling interaction capabilities with your Workspace, and allowing creation and management of workspace assets and resources.

Question 5

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The three methods in the MLClient class for managing resources and the one that
- makes a blocking call
- makes an async call
- can be leveraged for Workspace Reuse

Answer 5

• begin_create_or_update - Create or update an Azure ML Resource asynchronously
• create_or_update - Create or update an Azure ML Resource, blocking
• from_config - Return a workspace object from an existing Azure ML Workspace. Reads the Workspace config from a file, else throws an exception if one can’t be found. This one allows you to save then reuse your Workspace in different Python notebooks

Read MLClient

Question 6

The DataStore classes for connecting your Workspace to:
- A Blob Datastore
- A Data Lake (two of them)
- File Datastore

Answer 6

• AzureBlobDataStore
• AzureDataLakeGen1Datastore
• AzureDataLakeGen2Datastore
• AzureFileDatastore

Read Classes

Question 7

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command constructor for creating a Job as a Command

Answer 7

For a command:

from azure.ai.ml import command

job = command(
    code="./src",
    command="python train.py",
    environment="AzureML-sklearn-0.24-ubuntu18.04-py37-cpu@latest",
    compute="aml-cluster",
    experiment_name="train-model"
)

returned_job = ml_client.create_or_update(job)

Note that environment can be an environment image file specified by name, or an Environment instance.