An experiment is a grouping of many runs from a specified script. It always belongs to a workspace. When we submit a run, we provide an experiment name. Information for the run is stored under that experiment. If the name doesn't exist when we submit an experiment, a new experiment is automatically created.
A run is a single execution of a training script. An experiment will typically contain multiple runs.
A run has the following characteristics
- Metrics
- Child Runs
- Outputs
- Logs
A run also has metrics and parameters associated with it
A run also has outputs associated with it
Code
Create the workspace
from azureml.core import Workspace from azureml.core import Workspace from azureml.core.authentication import InteractiveLoginAuthentication sid = '<your-subscription-id>' forced_interactive_auth = InteractiveLoginAuthentication(tenant_id="<your-tenant-id>", force=True) ws = Workspace.create(name='azureml_workspace', subscription_id= sid, resource_group='rgazureml', create_resource_group = True, location='centralus' ) Create an experiment and a run
from azureml.core import Experiment # create an experiment exp = Experiment(workspace=ws, name='trial_exp') # start a run run = exp.start_logging() # log a number run.log('trial', 30) # log a list (Fibonacci numbers) run.log_list('my list', [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]) # finish the run run.complete() print('Finished logging') Create another run
# start a run run = exp.start_logging() # log a number run.log('trial2', 35) # log a list run.log_list('my list2', [1, 1, 2, 2, 5, 5, 13, 13, 13, 13]) # finish the run run.complete() References
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