125 lines
5 KiB
Markdown
125 lines
5 KiB
Markdown
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# Azure ML Lesson 2 Lab
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## 1. Set environmental variables
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1. Run VS Code in a Azure ML remote instance as shown before.
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2. Press `File > Open Folder` and navigate to `azuremlpythonsdk-v2/` to open the exercise.
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**IMPORTANT** Relative paths are assumed to be initialized from the `azuremlpythonsdk-v2` folder.
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Open the file `initialize_constants.py`, there are three variables that should be updated:
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- AZURE_WORKSPACE_NAME
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- AZURE_RESOURCE_GROUP
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- AZURE_SUBSCRIPTION_ID
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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.
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## 2. Load a workspace
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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`.
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When finished, run this file and check that it is executed without errors.
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## 3. Load a Compute Cluster
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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`.
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When finished, run this file and check that it is executed without errors.
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What would happen if the compute cluster is not present?
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## 4. Create a tabular dataset
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Open the file `data_tabular.py` , several gaps should be filled which are marked with `XXXX`:
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1. `ml_client = XXXXX()`
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Hint: look into previous files.
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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()]`
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Hint: Try to get one object from the class [Data](https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.entities.data?view=azure-python) and check their attributes.
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3. Which should be the `path` parameter in `path=XXXXX`?
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4. Which input should you give in `ml_client.data.create_or_update(XXXXX)`?
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When finished, run this file and check that it is executed without errors.
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## 5. Create and register an environment
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Open the file `environment.py` , several gaps should be filled which are marked with `XXXX`:
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1. `ml_client = XXXXX()`
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Hint: look into previous files.
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2. Get a list of environments already registered and modify the following:
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`env_list = XXXXX`
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Hint: look into previous files.
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3. Which class should be used to register the environment?
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Hint: Take a look [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=python)
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When finished, run this file and check that it is executed without errors.
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## 6. Train a model from a tabular dataset using a remote compute
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Open the file `azml_01_experiment_remote_compute.py` , several gaps should be filled which are marked with `XXXX`:
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1. `ml_client = XXXX()`
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Hint: look into previous files.
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2. Complete the `latest_version_dataset` definition.
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Hint: Take a look [here](https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-azure-ml-in-a-day#deploy-the-model-to-the-endpoint)
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3. Complete the `Input` part.
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Hint: Take a look [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?tabs=python)
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4. Complete the `command` part.
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Hint: Take a look [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?tabs=python)
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When finished, run this file and check that it is executed without errors.
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### 7. Tune hyperparameters using a remote compute
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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:
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- learning_rate: one of the values 0.01, 0.1, 1.0
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- n_estimators: one of the values 10, 100
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Hint: Use the previous file as template.
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Hint: For the `Hyperdrive settings` format, look [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-sweep-in-pipeline)
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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.
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Hint: Use as a template the file `data/diabetes_training.py`.
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When finished, run this file and check that it is executed without errors.
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## 8. Create a real-time inferencing service
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Open the file `azml_03_realtime_inference.py` , several gaps should be filled which are marked with `XXXX`.
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Hint: Take a look [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models?tabs=fromjob%2Cmir%2Csdk)
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When finished, run this file and check that it is executed without errors.
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## 9. Test the inference service
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Open the file `azml_04_test_inference.py` , several gaps should be filled which are marked with `XXXX`.
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Hint: Check [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2&tabs=python)
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