model_analyzer/record/metrics_manager.py
def _get_gpu_inference_metrics(self):
        """
        Stops GPU monitor and aggregates any records
        that are GPU specific
        Returns
        -------
        dict
            keys are gpu ids and values are metric values
            in the order specified in self._gpu_metrics
        """

        # Stop and destroy DCGM monitor
        gpu_records = self._gpu_monitor.stop_recording_metrics()

        # Insert all records into aggregator and get aggregated DCGM records
        gpu_record_aggregator = RecordAggregator()
        gpu_record_aggregator.insert_all(gpu_records)

        records_groupby_gpu = {}
        records_groupby_gpu = gpu_record_aggregator.groupby(
            self._gpu_metrics, lambda record: record.device_uuid())

        gpu_metrics = defaultdict(list)
        for _, metric in records_groupby_gpu.items():
            for gpu_uuid, metric_value in metric.items():
                gpu_metrics[gpu_uuid].append(metric_value)
        return gpu_metrics

        Stops GPU monitor and aggregates any records
        that are GPU specific
        Returns
        -------
        dict
            keys are gpu ids and values are metric values
            in the order specified in self._gpu_metrics
        
Stops GPU monitor and aggregates any records
that are GPU specific
Returns

dict
keys are gpu ids and values are metric values
in the order specified in self._gpu_metrics
Stops GPU monitor and aggregates any records that are GPU specific Returns dict keys are gpu ids and values are metric values in the order specified in self . _gpu_metrics
def _get_gpu_inference_metrics(self):
        gpu_records = self._gpu_monitor.stop_recording_metrics()
        gpu_record_aggregator = RecordAggregator()
        gpu_record_aggregator.insert_all(gpu_records)
        records_groupby_gpu = {}
        records_groupby_gpu = gpu_record_aggregator.groupby(
            self._gpu_metrics, lambda record: record.device_uuid())
        gpu_metrics = defaultdict(list)
        for _, metric in records_groupby_gpu.items():
            for gpu_uuid, metric_value in metric.items():
                gpu_metrics[gpu_uuid].append(metric_value)
        return gpu_metrics
def _get_gpu_inference_metrics ( self ) : gpu_records = self . _gpu_monitor . stop_recording_metrics ( ) gpu_record_aggregator = RecordAggregator ( ) gpu_record_aggregator . insert_all ( gpu_records ) records_groupby_gpu = { } records_groupby_gpu = gpu_record_aggregator . groupby ( self . _gpu_metrics , lambda record : record . device_uuid ( ) ) gpu_metrics = defaultdict ( list ) for _ , metric in records_groupby_gpu . items ( ) : for gpu_uuid , metric_value in metric . items ( ) : gpu_metrics [ gpu_uuid ] . append ( metric_value ) return gpu_metrics
Stops GPU monitor and aggregates any records
that are GPU specific
Returns
Stops GPU monitor and aggregates any records that are GPU specific Returns
"""
        Stops GPU monitor and aggregates any records
        that are GPU specific
        Returns
        -------
        dict
            keys are gpu ids and values are metric values
            in the order specified in self._gpu_metrics
        """ # Stop and destroy DCGM monitor # Insert all records into aggregator and get aggregated DCGM records
self