33-AzureML-2/azuremlpythonsdk-v2/README.md

Azure ML Lesson 2 Lab

1. Set environmental variables

1. Run VS Code in a Azure ML remote instance as shown before.
2. Press File > Open Folder and navigate to azuremlpythonsdk-v2/ to open the exercise.

IMPORTANT Relative paths are assumed to be initialized from the azuremlpythonsdk-v2 folder.

Open the file initialize_constants.py, there are three variables that should be updated:

• AZURE_WORKSPACE_NAME

• AZURE_RESOURCE_GROUP

• AZURE_SUBSCRIPTION_ID

Open your workspace at in https://ml.azure.com. At the top right, select the workspace name, then copy the workspace name, the subscription id and the resource name.

2. Load a workspace

Open the file ml_client.py and understand how a ML client object is loaded or created. In this lab, the namespace was already created. Just fill the name of the variables from initialize_constants.py.

When finished, run this file and check that it is executed without errors.

3. Load a Compute Cluster

Open the file compute_aml.py and understand how a compute cluster is loaded or created. In this lab, the compute cluster was already created but some variables should be added, which are marked with XXXX.

When finished, run this file and check that it is executed without errors.

What would happen if the compute cluster is not present?

4. Create a tabular dataset

Open the file data_tabular.py , several gaps should be filled which are marked with XXXX:

1. ml_client = XXXXX()

   Hint: look into previous files.

2. How can you get the names of the datasets already registered in if name_dataset not in [XXXXX for env in ml_client.data.list()]

   Hint: Try to get one object from the class Data and check their attributes.

3. Which should be the path parameter in path=XXXXX?

4. Which input should you give in ml_client.data.create_or_update(XXXXX)?

When finished, run this file and check that it is executed without errors.

5. Create and register an environment

Open the file environment.py , several gaps should be filled which are marked with XXXX:

1. ml_client = XXXXX()

   Hint: look into previous files.

2. Which class should be used to register the environment?

   Hint: Take a look here

When finished, run this file and check that it is executed without errors.

6. Train a model from a tabular dataset using a remote compute

Open the file azml_01_experiment_remote_compute.py , several gaps should be filled which are marked with XXXX:

1. ml_client = XXXX()

   Hint: look into previous files.

2. Complete the latest_version_dataset definition.

   Hint: Take a look here

3. Complete the Input part.

   Hint: Take a look here

4. Complete the command part.

   Hint: Take a look here

When finished, run this file and check that it is executed without errors.

7. Tune hyperparameters using a remote compute

Open the file azml_02_hyperparameters_tuning.py , several gaps should be filled which are marked with XXXX. The hyperparameter search should be defined in the following space:

• learning_rate: one of the values 0.01, 0.1, 1.0

• n_estimators: one of the values 10, 100

Hint: Use the previous file as template.

Hint: For the Hyperdrive settings format, look here

Open the file diabetes_hyperdrive/diabetes_training.py , several gaps should be filled which are marked with XXXX. A Gradient Boosting classification model should be trained and the auc and the accuracy in the test set should be computed.

Hint: Use as a template the file data/diabetes_training.py.

When finished, run this file and check that it is executed without errors.

8. Create a real-time inferencing service

Open the file azml_03_realtime_inference.py , several gaps should be filled which are marked with XXXX.

Hint: Take a look here

When finished, run this file and check that it is executed without errors.

9. Test the inference service

Open the file azml_04_test_inference.py , several gaps should be filled which are marked with XXXX.

Hint: Check here