Found that some of the runs did not get failed and were in running state. I assume these might be consuming compute targets. I tried to cancel the run which are in Running state from Azure ML Runs web UI, but was not working.
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First get the experiment object that have the Runs to cancel.
experiment = Experiment.list(workspace=workspace_obj, experiment_name='experiment_name')[0]
or
experiment = ws.experiments['experiment_name']
Cancel all runs which are in running state.
for run in experiment.get_runs():
print(run.id)
if run.status=="Running":
run.cancel()
Again check the status of each run:
for run in experiment.get_runs():
print(run.id)
print(run.status)

Saurabh Kansal
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