Train a model by using Python SDKv2

In order to design and implement a data science solution on Azure, you can utilize the Python SDKv2 to train a model. The process involves preparing and uploading the data, creating a training script, setting up a compute target, and submitting the model training job. Let’s explore each step in detail.

Step 1: Prepare and Upload Data

Prior to training a model, it is crucial to ensure that the data is properly prepared and uploaded to Azure Machine Learning. This involves creating a dataset, registering it, and making it accessible for model training. Azure ML offers several data handling options including data filtering, transformation, and splitting to preprocess and clean the data.

Here’s an example of how to create and register a dataset from a file in Azure ML:

python
from azureml.core import Workspace, Dataset

# Connect to your Azure ML workspace
workspace = Workspace.from_config()

# Create a file dataset
datastore = workspace.get_default_datastore()
file_dataset = Dataset.File.from_files(path=(datastore, ‘data/*.csv’))

# Register the dataset in Azure ML workspace
dataset = file_dataset.register(workspace=workspace, name=’my_dataset’)

Step 2: Create a Training Script

To train a model, you need to write a training script that defines the model architecture, trains the model using the provided data, and saves the trained model to a specified location. Ensure that your script adheres to the Azure ML runtime environment, where Azure ML provides specific SDKs and packages for model training.

You can utilize popular machine learning libraries like scikit-learn, TensorFlow, or PyTorch within your training script. Here’s an example of a simple training script using scikit-learn:

python
from azureml.core import Dataset
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Load the dataset
dataset = Dataset.get_by_name(workspace, ‘my_dataset’)
df = dataset.to_pandas_dataframe()

# Prepare the data
X = df.drop(columns=[‘target’])
y = df[‘target’]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LogisticRegression()
model.fit(X_train, y_train)

# Save the trained model
model.save(‘outputs/my_model.pkl’)

Step 3: Set up a Compute Target

Azure ML allows you to choose a compute target where the model training job will run. You can choose from various compute options such as local compute, Azure Machine Learning Compute, or remote compute. The choice of compute target depends on your requirements, data size, and available resources.

Here’s an example of setting up Azure Machine Learning Compute as the compute target:

python
from azureml.core.compute import AmlCompute, ComputeTarget
from azureml.core.compute_target import ComputeTargetException

# Define the compute configuration
compute_name = ‘my-compute’
compute_vm_size = ‘STANDARD_DS3_V2’
min_nodes = 0
max_nodes = 4

# Create or retrieve the Azure Machine Learning compute
try:
compute_target = ComputeTarget(workspace=workspace, name=compute_name)
print(‘Found existing compute target.’)
except ComputeTargetException:
compute_config = AmlCompute.provisioning_configuration(vm_size=compute_vm_size,
min_nodes=min_nodes,
max_nodes=max_nodes)
compute_target = ComputeTarget.create(workspace, compute_name, compute_config)
compute_target.wait_for_completion(show_output=True)

Step 4: Submit the Model Training Job

Once the data is prepared, the training script is created, and the compute target is set up, you can submit the model training job using the Azure Machine Learning SDK. This will initiate the training process and monitor its progress.

python
from azureml.core import Experiment
from azureml.core.script_run_config import ScriptRunConfig

# Create an experiment
experiment_name = ‘my_experiment’
experiment = Experiment(workspace, experiment_name)

# Create a script run configuration
src = ScriptRunConfig(source_directory=’.’,
script=’train.py’,
compute_target=compute_target)

# Submit the experiment
run = experiment.submit(src)
run.wait_for_completion(show_output=True)

By following these steps, you can effectively train your data science models using Python SDKv2 and Azure Machine Learning. Remember to consult the official Microsoft documentation for more detailed information on each step and explore the advanced capabilities offered by Azure ML to fine-tune your models and optimize the training process.