def test_record_utilization(self):
# One measurement every 0.01 seconds
frequency = 0.01
monitoring_time = 10
metrics = [GPUUtilization]
gpus = ['all']
dcgm_monitor = DCGMMonitor(gpus, frequency, metrics)
dcgm_monitor.start_recording_metrics()
time.sleep(monitoring_time)
records = dcgm_monitor.stop_recording_metrics()
# Assert instance types
for record in records:
self.assertIsInstance(record.device(), GPUDevice)
self.assertIsInstance(record.value(), float)
self.assertTrue(record.value() <= 100)
self.assertTrue(record.value() == TEST_RECORD_VALUE)
self.assertIsInstance(record.timestamp(), int)
# The number of records should be dividable by number of metrics
self.assertTrue(len(records) % len(metrics) == 0)
self.assertTrue(len(records) > 0)
self.assertTrue(records[-1].timestamp() -
records[0].timestamp() >= monitoring_time)
dcgm_monitor.destroy()
def test_immediate_start_stop(self):
frequency = 1
metrics = [GPUUsedMemory, GPUFreeMemory]
dcgm_monitor = DCGMMonitor(self._gpus, frequency, metrics)
dcgm_monitor.start_recording_metrics()
dcgm_monitor.stop_recording_metrics()
dcgm_monitor.destroy()
def test_record_memory(self):
# One measurement every 0.01 seconds
frequency = 0.01
monitoring_time = 10
metrics = [GPUUsedMemory, GPUFreeMemory]
gpus = ['all']
dcgm_monitor = DCGMMonitor(gpus, frequency, metrics)
dcgm_monitor.start_recording_metrics()
time.sleep(monitoring_time)
records = dcgm_monitor.stop_recording_metrics()
# Assert instance types
for record in records:
self.assertIsInstance(record.device(), GPUDevice)
self.assertIsInstance(record.value(), float)
self.assertTrue(record.value() == TEST_RECORD_VALUE)
self.assertIsInstance(record.timestamp(), int)
# The number of records should be dividable by number of metrics
self.assertTrue(len(records) % len(metrics) == 0)
self.assertTrue(len(records) > 0)
self.assertTrue(records[-1].timestamp() -
records[0].timestamp() >= monitoring_time)
with self.assertRaises(TritonModelAnalyzerException):
dcgm_monitor.stop_recording_metrics()
dcgm_monitor.destroy()
metrics = ['UndefinedTag']
with self.assertRaises(TritonModelAnalyzerException):
DCGMMonitor(gpus, frequency, metrics)
def test_immediate_start_stop(self):
frequency = 0.01
tags = [GPUUsedMemory, GPUFreeMemory]
gpus = ['all']
dcgm_monitor = DCGMMonitor(gpus, frequency, tags)
dcgm_monitor.start_recording_metrics()
dcgm_monitor.stop_recording_metrics()
dcgm_monitor.destroy()
def test_gpu_id(self):
frequency = 0.01
metrics = [GPUUsedMemory, GPUFreeMemory]
gpus = ['UndefinedId']
with self.assertRaises(TritonModelAnalyzerException):
DCGMMonitor(gpus, frequency, metrics)
gpus = [str(TEST_UUID, encoding='ascii')]
dcgm_monitor = DCGMMonitor(gpus, frequency, metrics)
dcgm_monitor.destroy()
def test_record_power(self):
# One measurement every 0.01 seconds
frequency = 1
monitoring_time = 2
metrics = [GPUPowerUsage]
dcgm_monitor = DCGMMonitor(self._gpus, frequency, metrics)
dcgm_monitor.start_recording_metrics()
time.sleep(monitoring_time)
records = dcgm_monitor.stop_recording_metrics()
# Assert instance types
for record in records:
self.assertIsInstance(record.device_uuid(), str)
self.assertIsInstance(record.value(), float)
self.assertTrue(record.value() == TEST_RECORD_VALUE)
self.assertIsInstance(record.timestamp(), int)
# The number of records should be dividable by number of metrics
self.assertTrue(len(records) % len(metrics) == 0)
self.assertTrue(len(records) > 0)
self.assertTrue(records[-1].timestamp() -
records[0].timestamp() >= monitoring_time)
dcgm_monitor.destroy()
class MetricsManager:
"""
This class handles the profiling
categorization of metrics
"""
def __init__(self, config, metric_tags, server, result_manager):
"""
Parameters
----------
config : AnalyzerConfig
The model analyzer's config
metric_tags : List of str
The list of tags corresponding to the metric types to monitor.
server : TritonServer
Handle to the instance of Triton being used
result_manager : ResultManager
instance that manages the result tables and
adding results
"""
self._server = server
self._gpus = config.gpus
self._monitoring_interval = config.monitoring_interval
self._perf_analyzer_path = config.perf_analyzer_path
self._config = config
self._result_manager = result_manager
self._dcgm_metrics = []
self._perf_metrics = []
self._cpu_metrics = []
self._create_metric_tables(metrics=MetricsManager.get_metric_types(
tags=metric_tags))
def _create_metric_tables(self, metrics):
"""
Splits up monitoring metrics into various
categories, defined in ___init___ and
requests result manager to make
corresponding table
"""
# Separates metrics and objectives into related lists
for metric in metrics:
if metric in DCGMMonitor.model_analyzer_to_dcgm_field:
self._dcgm_metrics.append(metric)
elif metric in PerfAnalyzer.perf_metrics:
self._perf_metrics.append(metric)
elif metric in CPUMonitor.cpu_metrics:
self._cpu_metrics.append(metric)
self._result_manager.create_tables(
gpu_specific_metrics=self._dcgm_metrics,
non_gpu_specific_metrics=self._perf_metrics + self._cpu_metrics)
def profile_server(self):
"""
Runs the DCGM monitor on the triton server without the perf_analyzer
Raises
------
TritonModelAnalyzerException
"""
self._start_monitors()
server_gpu_metrics = self._get_gpu_inference_metrics()
self._result_manager.add_server_data(data=server_gpu_metrics)
def profile_model(self, perf_config, perf_output_writer=None):
"""
Runs monitors while running perf_analyzer with a specific set of
arguments. This will profile model inferencing.
Parameters
----------
perf_config : dict
The keys are arguments to perf_analyzer The values are their
values
perf_output_writer : OutputWriter
Writer that writes the output from perf_analyzer to the output
stream/file. If None, the output is not written
Returns
-------
(dict of lists, list)
The gpu specific and non gpu metrics
"""
# Start monitors and run perf_analyzer
self._start_monitors()
perf_analyzer_metrics_or_status = self._get_perf_analyzer_metrics(
perf_config, perf_output_writer)
# Failed Status
if perf_analyzer_metrics_or_status == 1:
self._stop_monitors()
self._destroy_monitors()
return None, None
else:
perf_analyzer_metrics = perf_analyzer_metrics_or_status
# Get metrics for model inference and combine metrics that do not have GPU ID
model_gpu_metrics = self._get_gpu_inference_metrics()
model_cpu_metrics = self._get_cpu_inference_metrics()
model_non_gpu_metrics = list(perf_analyzer_metrics.values()) + list(
model_cpu_metrics.values())
return model_gpu_metrics, model_non_gpu_metrics
def _start_monitors(self):
"""
Start any metrics monitors
"""
self._dcgm_monitor = DCGMMonitor(self._gpus, self._monitoring_interval,
self._dcgm_metrics)
self._cpu_monitor = CPUMonitor(self._server, self._monitoring_interval,
self._cpu_metrics)
self._dcgm_monitor.start_recording_metrics()
self._cpu_monitor.start_recording_metrics()
def _stop_monitors(self):
"""
Stop any metrics monitors
"""
self._dcgm_monitor.stop_recording_metrics()
self._cpu_monitor.stop_recording_metrics()
def _destroy_monitors(self):
"""
Destroy the monitors created by start
"""
self._dcgm_monitor.destroy()
self._cpu_monitor.destroy()
def _get_perf_analyzer_metrics(self, perf_config, perf_output_writer=None):
"""
Gets the aggregated metrics from the perf_analyzer
Parameters
----------
perf_config : dict
The keys are arguments to perf_analyzer The values are their
values
perf_output_writer : OutputWriter
Writer that writes the output from perf_analyzer to the output
stream/file. If None, the output is not written
Raises
------
TritonModelAnalyzerException
"""
try:
perf_analyzer = PerfAnalyzer(
path=self._perf_analyzer_path,
config=perf_config,
timeout=self._config.perf_analyzer_timeout,
max_cpu_util=self._config.perf_analyzer_cpu_util)
status = perf_analyzer.run(self._perf_metrics)
# PerfAnalzyer run was not succesful
if status == 1:
return 1
except FileNotFoundError as e:
raise TritonModelAnalyzerException(
f"perf_analyzer binary not found : {e}")
if perf_output_writer:
perf_output_writer.write(perf_analyzer.output() + '\n')
perf_records = perf_analyzer.get_records()
perf_record_aggregator = RecordAggregator()
perf_record_aggregator.insert_all(perf_records)
return perf_record_aggregator.aggregate()
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._dcgm_metrics
"""
# Stop and destroy DCGM monitor
dcgm_records = self._dcgm_monitor.stop_recording_metrics()
self._destroy_monitors()
# Insert all records into aggregator and get aggregated DCGM records
dcgm_record_aggregator = RecordAggregator()
dcgm_record_aggregator.insert_all(dcgm_records)
records_groupby_gpu = {}
records_groupby_gpu = dcgm_record_aggregator.groupby(
self._dcgm_metrics, lambda record: record.device().device_id())
gpu_metrics = defaultdict(list)
for _, metric in records_groupby_gpu.items():
for gpu_id, metric_value in metric.items():
gpu_metrics[gpu_id].append(metric_value)
return gpu_metrics
def _get_cpu_inference_metrics(self):
"""
Stops any monitors that just need the records to be aggregated
like the CPU mmetrics
"""
cpu_records = self._cpu_monitor.stop_recording_metrics()
self._destroy_monitors()
cpu_record_aggregator = RecordAggregator()
cpu_record_aggregator.insert_all(cpu_records)
return cpu_record_aggregator.aggregate()
@staticmethod
def get_metric_types(tags):
"""
Parameters
----------
tags : list of str
Human readable names for the
metrics to monitor. They correspond
to actual record types.
Returns
-------
List
of record types being monitored
"""
return [RecordType.get(tag) for tag in tags]
class MetricsManager:
"""
This class handles the profiling
categorization of metrics
"""
def __init__(self, config, metric_tags, server, result_manager):
"""
Parameters
----------
config : AnalyzerConfig
The model analyzer's config
metric_tags : List of str
The list of tags corresponding to the metric types to monitor.
server : TritonServer
Handle to the instance of Triton being used
result_manager : ResultManager
instance that manages the result tables and
adding results
"""
self._server = server
self._gpus = config.gpus
self._monitoring_interval = config.monitoring_interval
self._perf_analyzer_path = config.perf_analyzer_path
self._result_manager = result_manager
self._dcgm_metrics = []
self._perf_metrics = []
self._cpu_metrics = []
self._create_metric_tables(metrics=MetricsManager.get_metric_types(
tags=metric_tags))
def _create_metric_tables(self, metrics):
"""
Splits up monitoring metrics into various
categories, defined in ___init___ and
requests result manager to make
corresponding table
"""
# Separates metrics and objectives into related lists
for metric in metrics:
if metric in DCGMMonitor.model_analyzer_to_dcgm_field:
self._dcgm_metrics.append(metric)
elif metric in PerfAnalyzer.perf_metrics:
self._perf_metrics.append(metric)
elif metric in CPUMonitor.cpu_metrics:
self._cpu_metrics.append(metric)
self._result_manager.create_tables(
gpu_specific_metrics=self._dcgm_metrics,
non_gpu_specific_metrics=self._perf_metrics + self._cpu_metrics,
aggregation_tag='Max')
def configure_result_manager(self, config_model):
"""
Processes the constraints and objectives
for given ConfigModel and creates a result
comparator to pass to the result manager
Parameters
----------
config_model : ConfigModel
The config model object for the model that is currently being
run
"""
constraints = {}
# Construct dict of record types for objectives and constraints
objective_tags = list(config_model.objectives().keys())
objective_metrics = MetricsManager.get_metric_types(
tags=objective_tags)
objectives = {
objective_metrics[i]: config_model.objectives()[objective_tags[i]]
for i in range(len(objective_tags))
}
# Constraints may be empty
if config_model.constraints():
constraint_tags = list(config_model.constraints().keys())
constraint_metrics = MetricsManager.get_metric_types(
tags=constraint_tags)
constraints = {
constraint_metrics[i]:
config_model.constraints()[constraint_tags[i]]
for i in range(len(constraint_tags))
}
self._result_comparator = ResultComparator(
gpu_metric_types=self._dcgm_metrics,
non_gpu_metric_types=self._perf_metrics + self._cpu_metrics,
metric_objectives=objectives)
self._result_manager.set_constraints_and_comparator(
constraints=constraints, comparator=self._result_comparator)
def profile_server(self, default_value):
"""
Runs the DCGM monitor on the triton server without the perf_analyzer
Parameters
----------
default_value : str
The value to fill in for columns in the table that don't apply to
profiling server only
Raises
------
TritonModelAnalyzerException
"""
self._start_monitors()
server_gpu_metrics = self._get_gpu_inference_metrics()
self._result_manager.add_server_data(data=server_gpu_metrics,
default_value=default_value)
def profile_model(self, perf_config, perf_output_writer=None):
"""
Runs monitors while running perf_analyzer with a specific set of
arguments. This will profile model inferencing.
Parameters
----------
perf_config : dict
The keys are arguments to perf_analyzer The values are their
values
perf_output_writer : OutputWriter
Writer that writes the output from perf_analyzer to the output
stream/file. If None, the output is not written
"""
# Start monitors and run perf_analyzer
self._start_monitors()
perf_analyzer_metrics = self._get_perf_analyzer_metrics(
perf_config, perf_output_writer)
# Get metrics for model inference and combine metrics that do not have GPU ID
model_gpu_metrics = self._get_gpu_inference_metrics()
model_cpu_metrics = self._get_cpu_inference_metrics()
model_non_gpu_metric_values = list(
perf_analyzer_metrics.values()) + list(model_cpu_metrics.values())
# Construct a measurement
model_measurement = Measurement(
gpu_data=model_gpu_metrics,
non_gpu_data=model_non_gpu_metric_values,
perf_config=perf_config,
comparator=self._result_comparator)
self._result_manager.add_model_data(measurement=model_measurement)
def _start_monitors(self):
"""
Start any metrics monitors
"""
self._dcgm_monitor = DCGMMonitor(self._gpus, self._monitoring_interval,
self._dcgm_metrics)
self._cpu_monitor = CPUMonitor(self._server, self._monitoring_interval,
self._cpu_metrics)
self._dcgm_monitor.start_recording_metrics()
self._cpu_monitor.start_recording_metrics()
def _destroy_monitors(self):
"""
Destroy the monitors created by start
"""
self._dcgm_monitor.destroy()
self._cpu_monitor.destroy()
def _get_perf_analyzer_metrics(self, perf_config, perf_output_writer=None):
"""
Gets the aggregated metrics from the perf_analyzer
Parameters
----------
perf_config : dict
The keys are arguments to perf_analyzer The values are their
values
perf_output_writer : OutputWriter
Writer that writes the output from perf_analyzer to the output
stream/file. If None, the output is not written
Raises
------
TritonModelAnalyzerException
"""
try:
perf_analyzer = PerfAnalyzer(path=self._perf_analyzer_path,
config=perf_config)
perf_analyzer.run(self._perf_metrics)
except FileNotFoundError as e:
raise TritonModelAnalyzerException(
f"perf_analyzer binary not found : {e}")
if perf_output_writer:
perf_output_writer.write(perf_analyzer.output() + '\n')
perf_records = perf_analyzer.get_records()
perf_record_aggregator = RecordAggregator()
perf_record_aggregator.insert_all(perf_records)
return perf_record_aggregator.aggregate()
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._dcgm_metrics
"""
# Stop and destroy DCGM monitor
dcgm_records = self._dcgm_monitor.stop_recording_metrics()
self._destroy_monitors()
# Insert all records into aggregator and get aggregated DCGM records
dcgm_record_aggregator = RecordAggregator()
dcgm_record_aggregator.insert_all(dcgm_records)
records_groupby_gpu = {}
records_groupby_gpu = dcgm_record_aggregator.groupby(
self._dcgm_metrics, lambda record: record.device().device_id())
gpu_metrics = defaultdict(list)
for _, metric in records_groupby_gpu.items():
for gpu_id, metric_value in metric.items():
gpu_metrics[gpu_id].append(metric_value)
return gpu_metrics
def _get_cpu_inference_metrics(self):
"""
Stops any monitors that just need the records to be aggregated
like the CPU mmetrics
"""
cpu_records = self._cpu_monitor.stop_recording_metrics()
self._destroy_monitors()
cpu_record_aggregator = RecordAggregator()
cpu_record_aggregator.insert_all(cpu_records)
return cpu_record_aggregator.aggregate()
@staticmethod
def get_metric_types(tags):
"""
Parameters
----------
tags : list of str
Human readable names for the
metrics to monitor. They correspond
to actual record types.
Returns
-------
List
of record types being monitored
"""
return [RecordType.get(tag) for tag in tags]