Monitor Azure ML experiment runs and metrics

• 12/05/2019
• 10 minutes to read
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APPLIES TO: Basic edition Enterprise edition                    (Upgrade to Enterprise edition)

Enhance the model creation process by tracking your experiments and monitoring run metrics. In this article, learn how to add logging code to your training script, submit an experiment run, monitor that run, and inspect the results in Azure Machine Learning.

Available metrics to track

The following metrics can be added to a run while training an experiment. To view a more detailed list of what can be tracked on a run, see the Run class reference documentation.

Choose a logging option

If you want to track or monitor your experiment, you must add code to start logging when you submit the run. The following are ways to trigger the run submission:

• Run.start_logging - Add logging functions to your training script and start an interactive logging session in the specified experiment. start_logging creates an interactive run for use in scenarios such as notebooks. Any metrics that are logged during the session are added to the run record in the experiment.
• ScriptRunConfig - Add logging functions to your training script and load the entire script folder with the run. ScriptRunConfig is a class for setting up configurations for script runs. With this option, you can add monitoring code to be notified of completion or to get a visual widget to monitor.

Set up the workspace

Before adding logging and submitting an experiment, you must set up the workspace.

1. Load the workspace. To learn more about setting the workspace configuration, see workspace configuration file.

   from azureml.core import Experiment, Run, Workspace
   import azureml.core
   
   ws = Workspace.from_config()
   

Option 1: Use start_logging

start_logging creates an interactive run for use in scenarios such as notebooks. Any metrics that are logged during the session are added to the run record in the experiment.

The following example trains a simple sklearn Ridge model locally in a local Jupyter notebook. To learn more about submitting experiments to different environments, see Set up compute targets for model training with Azure Machine Learning.

1. Create a training script in a local Jupyter notebook.

   # load diabetes dataset, a well-known small dataset that comes with scikit-learn
   from sklearn.datasets import load_diabetes
   from sklearn.linear_model import Ridge
   from sklearn.metrics import mean_squared_error
   from sklearn.model_selection import train_test_split
   from sklearn.externals import joblib
   
   X, y = load_diabetes(return_X_y = True)
   columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
   data = {
      "train":{"X": X_train, "y": y_train},        
      "test":{"X": X_test, "y": y_test}
   }
   reg = Ridge(alpha = 0.03)
   reg.fit(data['train']['X'], data['train']['y'])
   preds = reg.predict(data['test']['X'])
   print('Mean Squared Error is', mean_squared_error(preds, data['test']['y']))
   joblib.dump(value = reg, filename = 'model.pkl');
   

2. Add experiment tracking using the Azure Machine Learning SDK, and upload a persisted model into the experiment run record. The following code adds tags, logs, and uploads a model file to the experiment run.

    # Get an experiment object from Azure Machine Learning
    experiment = Experiment(workspace=ws, name="train-within-notebook")
   
    # Create a run object in the experiment
    run =  experiment.start_logging()
    # Log the algorithm parameter alpha to the run
    run.log('alpha', 0.03)
   
    # Create, fit, and test the scikit-learn Ridge regression model
    regression_model = Ridge(alpha=0.03)
    regression_model.fit(data['train']['X'], data['train']['y'])
    preds = regression_model.predict(data['test']['X'])
   
    # Output the Mean Squared Error to the notebook and to the run
    print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))
    run.log('mse', mean_squared_error(data['test']['y'], preds))
   
    # Save the model to the outputs directory for capture
    model_file_name = 'outputs/model.pkl'
   
    joblib.dump(value = regression_model, filename = model_file_name)
   
    # upload the model file explicitly into artifacts 
    run.upload_file(name = model_file_name, path_or_stream = model_file_name)
   
    # Complete the run
    run.complete()
   

   The script ends with run.complete(), which marks the run as completed. This function is typically used in interactive notebook scenarios.

Option 2: Use ScriptRunConfig

ScriptRunConfig is a class for setting up configurations for script runs. With this option, you can add monitoring code to be notified of completion or to get a visual widget to monitor.

This example expands on the basic sklearn Ridge model from above. It does a simple parameter sweep to sweep over alpha values of the model to capture metrics and trained models in runs under the experiment. The example runs locally against a user-managed environment.

1. Create a training script train.py.

   # train.py
   
   import os
   from sklearn.datasets import load_diabetes
   from sklearn.linear_model import Ridge
   from sklearn.metrics import mean_squared_error
   from sklearn.model_selection import train_test_split
   from azureml.core.run import Run
   from sklearn.externals import joblib
   
   import numpy as np
   
   #os.makedirs('./outputs', exist_ok = True)
   
   X, y = load_diabetes(return_X_y = True)
   
   run = Run.get_context()
   
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
   data = {"train": {"X": X_train, "y": y_train},
          "test": {"X": X_test, "y": y_test}}
   
   # list of numbers from 0.0 to 1.0 with a 0.05 interval
   alphas = mylib.get_alphas()
   
   for alpha in alphas:
      # Use Ridge algorithm to create a regression model
      reg = Ridge(alpha = alpha)
      reg.fit(data["train"]["X"], data["train"]["y"])
   
      preds = reg.predict(data["test"]["X"])
      mse = mean_squared_error(preds, data["test"]["y"])
      # log the alpha and mse values
      run.log('alpha', alpha)
      run.log('mse', mse)
   
      model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
      # save model in the outputs folder so it automatically get uploaded
      with open(model_file_name, "wb") as file:
          joblib.dump(value = reg, filename = model_file_name)
   
      # upload the model file explicitly into artifacts 
      run.upload_file(name = model_file_name, path_or_stream = model_file_name)
   
      # register the model
      #run.register_model(file_name = model_file_name)
   
      print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))
   
   

2. The train.py script references mylib.py which allows you to get the list of alpha values to use in the ridge model.

   # mylib.py
   
   import numpy as np
   
   def get_alphas():
      # list of numbers from 0.0 to 1.0 with a 0.05 interval
      return np.arange(0.0, 1.0, 0.05)
   

3. Configure a user-managed local environment.

   from azureml.core import Environment
   
   # Editing a run configuration property on-fly.
   user_managed_env = Environment("user-managed-env")
   
   user_managed_env.python.user_managed_dependencies = True
   
   # You can choose a specific Python environment by pointing to a Python path 
   #user_managed_env.python.interpreter_path = '/home/johndoe/miniconda3/envs/myenv/bin/python'
   

4. Submit the train.py script to run in the user-managed environment. This whole script folder is submitted for training, including the mylib.py file.

   from azureml.core import ScriptRunConfig
   
   exp = Experiment(workspace=ws, name="train-on-local")
   src = ScriptRunConfig(source_directory='./', script='train.py')
   src.run_config.environment = user_managed_env
   run = exp.submit(src)
   

Manage a run

The Start, monitor, and cancel training runs article highlights specific Azure Machine Learning workflows for how to manage your experiments.

View run details

View active/queued runs from the browser

Compute targets used to train models are a shared resource. As such, they may have multiple runs queued or active at a given time. To see the runs for a specific compute target from your browser, use the following steps:

1. From the Azure Machine Learning studio, select your workspace, and then select Compute from the left side of the page.

2. Select Training Clusters to display a list of compute targets used for training. Then select the cluster.

   

3. Select Runs. The list of runs that use this cluster is displayed. To view details for a specific run, use the link in the Run column. To view details for the experiment, use the link in the Experiment column.

   

   Tip

   A run can contain child runs, so one training job can result in multiple entries.

Once a run completes, it is no longer displayed on this page. To view information on completed runs, visit the Experiments section of the studio and select the experiment and run. For more information, see the Query run metrics section.

Monitor run with Jupyter notebook widget

When you use the ScriptRunConfig method to submit runs, you can watch the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes.

1. View the Jupyter widget while waiting for the run to complete.

   from azureml.widgets import RunDetails
   RunDetails(run).show()
   

   

You can also get a link to the same display in your workspace.

print(run.get_portal_url())

2. [For automated machine learning runs] To access the charts from a previous run. Replace <<experiment_name>> with the appropriate experiment name:

   from azureml.widgets import RunDetails
   from azureml.core.run import Run
   
   experiment = Experiment (workspace, <<experiment_name>>)
   run_id = 'autoML_my_runID' #replace with run_ID
   run = Run(experiment, run_id)
   RunDetails(run).show()
   

   

To view further details of a pipeline click on the Pipeline you would like to explore in the table, and the charts will render in a pop-up from the Azure Machine Learning studio.

Get log results upon completion

Model training and monitoring occur in the background so that you can run other tasks while you wait. You can also wait until the model has completed training before running more code. When you use ScriptRunConfig, you can use run.wait_for_completion(show_output = True) to show when the model training is complete. The show_output flag gives you verbose output.

Query run metrics

You can view the metrics of a trained model using run.get_metrics(). You can now get all of the metrics that were logged in the example above to determine the best model.

View the experiment in your workspace in Azure Machine Learning studio

When an experiment has finished running, you can browse to the recorded experiment run record. You can access the history from the Azure Machine Learning studio.

Navigate to the Experiments tab and select your experiment. You are brought to the experiment run dashboard, where you can see tracked metrics and charts that are logged for each run. In this case, we logged MSE and the alpha values.

You can drill down to a specific run to view its outputs or logs, or download the snapshot of the experiment you submitted so you can share the experiment folder with others.

Viewing charts in run details

There are various ways to use the logging APIs to record different types of metrics during a run and view them as charts in Azure Machine Learning studio.

Example notebooks

The following notebooks demonstrate concepts in this article:

• how-to-use-azureml/training/train-within-notebook
• how-to-use-azureml/training/train-on-local
• how-to-use-azureml/training/logging-api/logging-api.ipynb

Learn how to run notebooks by following the article, Use Jupyter notebooks to explore this service.

Next steps

Try these next steps to learn how to use the Azure Machine Learning SDK for Python:

• See an example of how to register the best model and deploy it in the tutorial, Train an image classification model with Azure Machine Learning.

• Learn how to Train PyTorch Models with Azure Machine Learning.