do not use range_test results for tuning
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@ -448,6 +448,9 @@ def train_ddpg(train_ddpg_input):
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params = JSONUtil.loads(session.hyperparameters)
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session_results = Result.objects.filter(session=session,
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creation_time__lt=result.creation_time)
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for i, result in enumerate(session_results):
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if 'range_test' in result.metric_data.name:
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session_results.pop(i)
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target_data = {}
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target_data['newest_result_id'] = result_id
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@ -153,6 +153,8 @@ class DataUtil(object):
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rowlabels = np.empty(len(results), dtype=int)
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for i, result in enumerate(results):
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if 'range_test' in result.metric_data.name:
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continue
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param_data = JSONUtil.loads(result.knob_data.data)
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if len(param_data) != len(knob_labels):
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raise Exception(
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@ -645,7 +645,7 @@ def handle_result_files(session, files, execution_times=None):
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if 'status' in summary and summary['status'] == "range_test":
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# The metric should not be used for learning because the driver did not run workload
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# We tag the metric as invalid, so later they will be set to the worst result
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metric_data.name = metric_data.name + '*'
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metric_data.name = 'range_test_' + metric_data.name + '*'
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metric_data.save()
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# Create a new workload if this one does not already exist
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