def process(args):
"""
General procedure of analysis
args - arguments from command line input
"""
analyzer = Analyzer(args.workload_conf_file)
if args.offline:
process_offline_data(args, analyzer)
else:
strict = True if args.fense_type == 'gmm-strict' else False
analyzer.build_model(args.util_file, args.metric_file,
args.thresh, strict, args.verbose)
def test_load_and_save():
"""
Load, save and load again full and thin analyzer instances, and verify that content is identical.
"""
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/1_example'
data_dir = os.path.join(base_dir, relative_data_dir)
# ----------------------------
# load and save full analyzer
# ----------------------------
# load analyzer
analyzer_file = os.path.join(data_dir, 'analyzer_full.p')
analyzer_full = Analyzer.load(analyzer_file)
# save analyzer in temporary directory
outupt_dir = os.path.join(data_dir, 'tmp_full')
shutil.rmtree(outupt_dir, ignore_errors=True) # delete existing temp dir
# save instance with image dir
output_file_full = os.path.join(outupt_dir, 'analyzer.p')
analyzer_full.save(output_file_full, save_thin_instance=False, save_images_in_dir=True, image_name_template='{:08d}.png')
# ----------------------------
# load and save thin analyzer
# ----------------------------
analyzer_file = os.path.join(data_dir, 'analyzer_thin.p')
analyzer_thin = Analyzer.load(analyzer_file, load_images_from_dir=True, sfx='png') # use lossless png image, not jpg
# save analyzer in temporary directory
outupt_dir = os.path.join(data_dir, 'tmp_thin')
shutil.rmtree(outupt_dir, ignore_errors=True) # delete existing temp dir
# save instance with image dir
output_file_thin = os.path.join(outupt_dir, 'analyzer.p')
analyzer_thin.save(output_file_thin, save_thin_instance=True, save_images_in_dir=True, image_name_template='{:08d}.png') # use lossless png image, not jpg
# ----------------------------------------------------------------
# load analyzers from saved dirs and compare content thin analyzer
# ----------------------------------------------------------------
# load analyzers
analyzer_full2 = Analyzer.load(output_file_full)
analyzer_thin2 = Analyzer.load(output_file_thin, load_images_from_dir=True, sfx='png') # use lossless png image, not jpg
# compare content
is_equal = compare_instances(analyzer_full2, analyzer_thin2)
assert is_equal
def load_test_analyzer(data_dir='ILSVRC2015_00078000'):
"""
Load analyzer object from saved test data.
Parameters
----------
data_dir : str, optional
Data directory name, should be name of one of the folders inside analyze/tests/data/.
Returns
-------
anaylzer : Analyzer
Analyzer object.
analyzer_root_dir : str
Path of analyzer root directory, such that full images path is the concatenation of analyzer_root_dir and
analyzer.data image_path.
"""
# load reference analyzer
base_dir = os.path.dirname(__file__)
relative_data_dir = os.path.join('data', data_dir)
data_dir = os.path.join(base_dir, relative_data_dir)
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
analyzer_root_dir = os.path.join(base_dir, '..')
return analyzer, analyzer_root_dir
def test_visualize_example():
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
analyze_root_dir = os.path.abspath(os.path.join(base_dir, '..'))
os.chdir(analyze_root_dir)
# load analyzer
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=True)
# visualize example
save_fig_path = os.path.join(base_dir, 'save_fig_example.png')
frame_id = 40
display = False # True
image = analyzer.visualize_example(key=frame_id,
class_names=CLASS_NAMES,
display=display,
save_fig_path=save_fig_path)
# compare to reference image
image_ref_path = os.path.join(
base_dir, 'data/visualizations/visualization_example.png')
image_ref = cv2.imread(image_ref_path, cv2.IMREAD_UNCHANGED)
is_close = np.isclose(image, image_ref,
atol=2) # allow up to 2 gray level difference
is_all_close = np.all(is_close)
assert is_all_close
def _test_message(self, message: Descriptor):
table_resolver = TableResolver()
analyzer = Analyzer(table_resolver)
analyzer.generate_tables_for_file(self._create_file_with_message(message))
result = list(table_resolver.tables)
self.assertTrue(len(result) is 1)
result_table: Table = result[0]
# Sort the fields by name.
def sorted_by_fields_name(fields):
return fields.sort(key=lambda field: field.name)
for table_field, message_field in zip(sorted_by_fields_name(result_table.fields),
sorted_by_fields_name(message.fields)):
self._test_field(table_field, message_field)
def test_empty_preds_gts():
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
# load analyzer
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
# initialize total confusion matrix
num_classes = len(CLASS_NAMES)
cm_total = np.zeros((num_classes + 1, num_classes + 1))
# iterate over frames
counter = 0
for frame_id, item in analyzer.items():
# unpack data
prediction, ground_truth, image_path, image = analyzer.unpack_item(
item)[0:4]
if counter == 1: # delete predictions
prediction = Box([], image_shape=(100, 200))
elif counter == 2: # delete groud truths
ground_truth = Box([], image_shape=(100, 200))
elif counter == 3: # delete image shape
try:
ground_truth = Box([], image_shape=None)
except ValueError as e:
cond3 = e.__repr__(
) == "ValueError('image_shape must be tuple of length 2 (height, width) or 3 (height, width, channels), got None',)"
# calculate confusion matrix of current frame
cm = ConfusionMatrix.calculate_confusion_matrix(prediction,
ground_truth,
CLASS_NAMES,
normalize=None,
score_th=0.3,
iou_th=0.5,
iou_criterion='all',
score_criterion='all',
display=False)
if counter == 1: # delete predictions
cond1 = cm[:, -1].sum() == 3
elif counter == 2: # delete groud truths
cond2 = cm[-1, :].sum() == 3
# analyze cm
# advance counter
counter += 1
# check conditions
assert cond1
assert cond2
assert cond3
def test_analyzer_mutable_mapping_implementation():
"""
Analyzer class inherits from collections.abc.MutableMapping, which demands implementation of
several class methods such as __setitem__, __iter__, etc.
Here we will check the correctness of these implementations.
"""
# load analyzer
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
analyzer2 = Analyzer()
# check iteration
for frame_id, item in analyzer.items():
# check unpacking
prediction, ground_truth, image_path, image = analyzer.unpack_item(
item)[0:4]
# check __setitem by assigning to second analyzer
analyzer2[frame_id] = item
# add attributes to analyzer2
analyzer2.image_resize_factor = analyzer.image_resize_factor
analyzer2.video_processor = analyzer.video_processor
analyzer2.output_dir = analyzer.output_dir
analyzer2.class_names = analyzer.class_names
analyzer2.bbox_match_method = analyzer.bbox_match_method
analyzer2.iou_th = analyzer.iou_th
analyzer2.score_th = analyzer.score_th
# compare 2 analyzers
is_equal = compare_instances(analyzer, analyzer2)
assert is_equal
# check __delitem__
keys = list(analyzer.keys())
key_to_delete = keys[0]
del analyzer[key_to_delete]
def test_initialize_with_simple_wrapper():
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
# load analyzer
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
# initialize total confusion matrix
num_classes = len(CLASS_NAMES)
cm_total = np.zeros((num_classes + 1, num_classes + 1))
def generate_report():
# load reference analyzer
base_dir = os.path.dirname(__file__)
relative_data_dir = '../tests/data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
# set output directory
analyzer.output_dir = os.path.join(base_dir, 'output', 'generate_report')
os.makedirs(analyzer.output_dir, exist_ok=True)
# generate report
analyzer.evaluate_performance(generate_report=True)
pass
def main():
# Read request message from stdin
data = sys.stdin.buffer.read()
# Parse request
request = plugin.CodeGeneratorRequest()
request.ParseFromString(data)
# Create response
response = plugin.CodeGeneratorResponse()
# TODO: clean this part.
# Generate code
table_resolver = TableResolver()
analyzer = Analyzer(table_resolver)
pool = DescriptorPool()
for proto_file in request.proto_file:
pool.Add(proto_file)
analyzer.generate_tables_for_file(
file_descriptor=pool.FindFileByName(proto_file.name))
analyzer.link_tables_references()
writer = ProtoPluginResponseWriter()
writer.write(generator=KotlinExposedGenerator(),
tables=table_resolver.tables,
plugin_response=response)
# Serialise response message
output = response.SerializeToString()
# Write to stdout
sys.stdout.buffer.write(output)
response = urllib2.urlopen(req, json.dumps(analyzedDict))
html = response.read()
html = json.loads(html)
print "Success!\n"
print "Result:"
for key, value in html.iteritems():
if isinstance(value, dict):
print str(key)+":"
for key2, value2 in value.iteritems():
print " "+str(key2)+": "+str(value2)
else:
print str(key)+": "+str(value)
if __name__ == "__main__":
directory = "../device/data/analyze/"
analyzer = Analyzer(directory)
while True:
if filesAvailable(directory):
print "\nNew files are available.\nWaiting for all data..."
sleep(2)
print "Data received.\nStart analyzing..."
analyzedDict = analyzer.analyze()
print "Data analyzed.\nStart posting..."
postData(analyzedDict)
sleep(5)
import os
from analyze.analyzer import Analyzer
from analyze.viewer import AnalyzerViewer
if __name__ == '__main__':
import os
import panel as pn
pn.extension()
# load analyzer
base_dir = os.path.dirname(__file__)
relative_data_dir = '../tests/data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
os.chdir('..') # go one level up
# initialize viewer
viewer = AnalyzerViewer(analyzer, resize_factor=2.)
# view analyzer
viewer.view()
print('Done!')
def basic_usage_example():
"""
Simulate inference loop by using saved analyzer.
"""
# load reference analyzer
analyzer_ref, analyze_root_dir = load_test_analyzer()
base_dir = os.path.dirname(__file__)
images_path = os.path.abspath(
os.path.join(base_dir, '..', 'tests/data/ILSVRC2015_00078000/images'))
# create new analyzer
output_dir = os.path.join(base_dir, 'output', 'simple_usage_example')
analyzer = Analyzer(
output_dir=output_dir,
output_video_name='video.avi',
class_names=analyzer_ref.class_names,
bbox_match_method='pred_bbox_center',
score_th=0.25,
iou_th=0.4,
)
# set output images format and directory
output_images_dir = os.path.join(output_dir, 'images')
os.makedirs(output_images_dir, exist_ok=True)
output_image_format = 'jpg' # png
pattern_length = 8
output_image_pattern = '%0{}d.{}'.format(pattern_length,
output_image_format)
images_with_boxes = []
# simulate inference loop by iterating over data saved in analyzer_ref
counter = 0
for frame_id, item in analyzer_ref.items():
# inference simulation
prediction, ground_truth, image_path, image, _ = analyzer_ref.unpack_item(
item)
image_path = os.path.join(images_path, os.path.basename(image_path))
# log results in analyzer
# IMPORTANT:
# in real usage of analyzer you will probably need to to some pre-processing to convert inference output to the
# form that analyzer expects. mainly - convert predictions and ground_truth to bounding_box.Box().
analyzer.update_analyzer(key=frame_id,
prediction=prediction,
ground_truth=ground_truth,
image_path=image_path,
analyze_performance=True)
# optional: save visualizations
image_out_path = os.path.join(output_images_dir,
output_image_pattern % frame_id)
image_with_boxes = analyzer.visualize_example(
key=frame_id,
image=image,
show_predictions=True,
show_ground_truth=True,
class_names=analyzer_ref.class_names,
rgb2bgr=False,
display=False,
save_fig_path=image_out_path,
)
# optional: save images with visualizations in a list - later will be used for video creation
images_with_boxes.append(image_with_boxes)
# optional: periodically save analyzer and video, can omit and save only at inference loop end
if np.mod(counter, 20) == 0:
analyzer.save(save_thin_instance=False, save_images_in_dir=True)
analyzer.update_video(images_with_boxes)
counter += 1
# save final analyzer and video
analyzer.save(save_thin_instance=True, save_images_in_dir=False)
analyzer.update_video(images_with_boxes)
# analyze full run - save performance report in output folder
analyzer.evaluate_performance(generate_report=True)
print('basic_usage_example - done!')
def test_confusion_matrix():
base_dir = os.path.dirname(__file__)
relative_data_dir = 'data/ILSVRC2015_00078000'
data_dir = os.path.join(base_dir, relative_data_dir)
# load analyzer
analyzer_file = os.path.join(data_dir, 'analyzer.p')
analyzer = Analyzer.load(analyzer_file, load_images_from_dir=False)
# initialize total confusion matrix
num_classes = len(CLASS_NAMES)
cm_total = np.zeros((num_classes + 1, num_classes + 1))
# iterate over frames
for frame_id, item in analyzer.items():
# unpack data
prediction, ground_truth, image_path, image = analyzer.unpack_item(
item)[0:4]
# calculate confusion matrix of current frame
cm = ConfusionMatrix.calculate_confusion_matrix(prediction,
ground_truth,
CLASS_NAMES,
normalize=None,
score_th=0.3,
iou_th=0.5,
iou_criterion='all',
score_criterion='all',
display=False)
# analyze cm
# add current cm to total cm
cm_total += cm
# analyze cm_total
# normalize cm
cm_total_norm = ConfusionMatrix.normalize_confusion_matrix(cm_total,
norm_type='gt')
# save_fig_name = os.path.join(data_dir, 'analysis/confusion_matrix.png')
# ConfusionMatrix.plot_confusion_matrix(cm_total_norm, display_labels=CLASS_NAMES, save_fig_name=save_fig_name, display=False) # for display
cm_total_ref = np.array([
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 159., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 37., 0., 0., 0., 0., 0., 0., 0., 30.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
],
[
0., 0., 0., 0., 0., 0., 0., 11., 0., 11., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 11., 0., 0., 0., 0., 0., 0., 0., 0.
],
])
is_equal_mat = cm_total_ref == cm_total
is_equal = cm_total == pytest.approx(cm_total_ref)
assert is_equal
def main():
""" Script entry point. """
ctx = Context()
ctx.args = parse_arguments()
ctx.cgroup_driver = detect_cgroup_driver()
ctx.analyzer = Analyzer(ctx.args.workload_conf_file,
ctx.args.thresh_file)
init_wlset(ctx)
init_sysmax(ctx)
if ctx.args.enable_prometheus:
ctx.prometheus.start()
if ctx.args.control:
ctx.cpuq = CpuQuota(ctx.sysmax_util, ctx.args.margin_ratio,
ctx.args.verbose)
quota_controller = NaiveController(ctx.cpuq, ctx.args.quota_cycles)
ctx.llc = LlcOccup(Resource.BUGET_LEV_MIN, ctx.args.exclusive_cat)
llc_controller = NaiveController(ctx.llc, ctx.args.llc_cycles)
if ctx.args.disable_cat:
ctx.llc = LlcOccup(Resource.BUGET_LEV_FULL, exclusive=False)
ctx.controllers = {Contention.CPU_CYC: quota_controller}
else:
ctx.controllers = {Contention.CPU_CYC: quota_controller,
Contention.LLC: llc_controller}
if ctx.args.record:
cols = ['time', 'cid', 'name', Metric.UTIL]
with open(Analyzer.UTIL_FILE, 'w') as utilf:
utilf.write(','.join(cols) + '\n')
threads = [Thread(target=monitor, args=(mon_util_cycle,
ctx, ctx.args.util_interval))]
if ctx.args.collect_metrics:
if ctx.args.record:
cols = ['time', 'cid', 'name', Metric.INST, Metric.CYC,
Metric.CPI, Metric.L3MPKI, Metric.L3MISS, Metric.NF,
Metric.UTIL, Metric.L3OCC, Metric.MBL, Metric.MBR,
Metric.L2STALL, Metric.MEMSTALL, Metric.L2SPKI,
Metric.MSPKI]
with open(Analyzer.METRIC_FILE, 'w') as metricf:
metricf.write(','.join(cols) + '\n')
threads.append(Thread(target=monitor,
args=(mon_metric_cycle,
ctx, ctx.args.metric_interval)))
for thread in threads:
thread.start()
print('eris agent version', __version__, 'is started!')
try:
for thread in threads:
thread.join()
except KeyboardInterrupt:
print('Shutdown eris agent ...exiting')
ctx.shutdown = True
except Exception:
traceback.print_exc(file=sys.stdout)
sys.exit(0)
