What are the various Run metrics that can be added in run in AzureML

Question

I have multiple stuff that i want to record while performing ML experiment in AzureML. what are the various objects that can be recorded.

Answer 1

The following metrics can be added to a run while training an experiment.

Scalar

Log a numerical or string value to the run with the given name using azureml.core.Run.log. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.

Example: run.log("accuracy", 0.95)

List

Log a list of values to the run with the given name using azureml.core.Run.log_list.

Example:

run.log_list("accuracies", [0.6, 0.7, 0.87])

Row

Using azureml.core.Run.log_row creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.

Example:

run.log_row("Y over X", x=1, y=0.4)

Table

Log a dictionary object to the run with the given name using azureml.core.Run.log_table.

Example:

run.log_table("Y over X", {"x":[1, 2, 3], "y":[0.6, 0.7, 0.89]})

Image

Log an image to the run record. Use azureml.core.Run.log_image to log an image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.

Example:

run.log_image("ROC", plt)

Reference: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py