run_context.md

Using Run Context to manage the pipeline execution

Leverging Run Context

Azure ML allows execution of a Python script in a container that can be sent/run on AML compute clusters instead of a local machine. This could be a data transformation script, a training script, or an inferencing script. The below examples shows how to do this for a simple training script (stand alone, in absence of a pipeline)

from azureml.core import Experiment
experiment_name = 'train-on-amlcompute'
experiment = Experiment(workspace = ws, name = experiment_name)

from azureml.core import ScriptRunConfig
src = ScriptRunConfig(source_directory=project_folder, 
                      script='train.py', 
                      compute_target=cpu_cluster, 
                      environment=myenv)
 
run = experiment.submit(config=src)

These "runs" are executed via a submit command from an experiment. Being able to log information to the run from within the script itself (in the above example train.py) is key.

In this repo, iris_supervised_model.py leverages run context to log metrics, tables, and properties. run = Run.get_context() This is the magic line that connects a vanilla Python script to the context of the run, inside the experiment, inside the Azure ML workspace.

** TIP ** When relying on run context of Azure ML (such as environment variables being passed in from the driver script) performing the following check early in the script can allow defaults to be set for anything that would have been passed in. This allows for local testing which is a time saver.

if (run.id.startswith('OfflineRun')):
	os.environ['AZUREML_DATAREFERENCE_irisdata'] = '.\sample_data.csv'
	os.environ['AZUREML_DATAREFERENCE_model_output'] = '.\model_output'

Now, metrics can be logged
run.log("accuracy",best_score) and tables
run.log_confusion_matrix('Confusion matrix '+name, confusion_matrix(Y_train, model.predict(X_train))) See this sample notebook for all the things logging.

Managing the pipeline execution

In a pipeline, each run is at the step level, or a child of a parent run which is the pipeline itself.

It may be best to log important metrics or properties at the pipeline level rather than at the step level (or both). run.parent will get the parent run context. The code below sets the two properties by passing in a dictionary as the parameter and those same values on two tags as well.

run.parent.add_properties({'best_model':best_model[0],'accuracy':best_score})
run.parent.tag("best_model",best_model[0])
run.parent.tag("accuracy",best_score)

Properties are immutable while tags are not, however tags are more prodominent in the Azure ML Run UI so they are easier to read.

To review the added properties click "Raw JSON" under "see all properties".

Now that the results of the training are published to the parent pipeline tags (and properties), they can be used to control what happens in execution of later steps. In register_model.py, the accuracy score is going to control if this model will be registered or not. For details, review Model Registration