birbnetes/cnn-classification-service
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Added tracing to classification
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Pünkösd Marcell
2021-07-30 15:24:57 +02:00
parent 0245cd7b6a
commit 1acdd6d21c
3 changed files with 44 additions and 30 deletions
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34
cnn_classification_service/magic_doer.py
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@@ -18,29 +18,33 @@ class MagicDoer:
requests_session = SessionTracing(propagate=True)
@classmethod
def run_everything(cls, parameters: dict) -> dict:
def run_everything(cls, parameters: dict, span: opentracing.span.Span) -> dict:
tag = parameters['tag']
sample_file_handle, sample_file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav", dir="/dev/shm")
span.log_kv({'event': 'sampleFileOpened', 'sampleTag': tag})
response = None
try:
# Download Sample
object_path = urljoin(Config.STORAGE_SERVICE_URL, f"object/{tag}")
with opentracing.tracer.start_span('downloadSample', child_of=span):
# Download Sample
object_path = urljoin(Config.STORAGE_SERVICE_URL, f"object/{tag}")
logging.info(f"Downloading sample: {tag} from {object_path}")
r = cls.requests_session.get(object_path)
with open(sample_file_handle, 'wb') as f:
f.write(r.content)
logging.info(f"Downloading sample: {tag} from {object_path}")
r = cls.requests_session.get(object_path)
with open(sample_file_handle, 'wb') as f:
f.write(r.content)
logging.debug(f"Downloaded sample to {sample_file_path}")
logging.debug(f"Downloaded sample to {sample_file_path}")
# Get a classifier that uses the default model
model_details, classifier = cls.classifier_cache.get_default_classifier()
with opentracing.tracer.start_span('loadClassifier', child_of=span):
# Get a classifier that uses the default model
model_details, classifier = cls.classifier_cache.get_default_classifier()
# do the majic
classification_start_time = time.time()
predicted_class_name, labeled_predictions = classifier.predict(sample_file_path)
classification_duration = time.time() - classification_start_time
with opentracing.tracer.start_span('runClassifier', child_of=span) as child_span:
# do the majic
classification_start_time = time.time()
predicted_class_name, labeled_predictions = classifier.predict(sample_file_path, child_span)
classification_duration = time.time() - classification_start_time
response = {
"tag": tag,
@@ -57,6 +61,8 @@ class MagicDoer:
except FileNotFoundError:
pass
span.log_kv({'event': 'sampleFileDeleted', 'sampleTag': tag})
if not response:
logging.error("Something went wrong during classification!")
else: