I have multiple stuff that i want to record while performing ML experiment in AzureML. what are the various objects that can be recorded.
What are the various Run metrics that can be added in run in AzureML
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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:
Table
Log a dictionary object to the run with the given name using azureml.core.Run.log_table.
Example:
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:
Reference: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py