def predict(self, batch):
"""
PREDICT COMMAND = {
"command": "predict",
"batch": [ REQUEST_INPUT ]
}
:param batch: list of request
:return:
"""
headers, input_batch, req_id_map = Service.retrieve_data_for_inference(
batch)
self.context.request_ids = req_id_map
self.context.request_processor = headers
metrics = MetricsStore(req_id_map, self.context.model_name)
self.context.metrics = metrics
start_time = time.time()
# noinspection PyBroadException
try:
ret = self._entry_point(input_batch, self.context)
except MemoryError:
logger.error("System out of memory", exc_info=True)
return create_predict_response(None, req_id_map,
"Out of resources", 507)
except Exception as e: # pylint: disable=broad-except
logger.warning("Invoking custom service failed.", exc_info=True)
message = "Prediction failed: " + str(e) + " \n " + str(
traceback.format_exc())
return create_predict_response(None, req_id_map, message, 503)
if not isinstance(ret, list):
logger.warning("model: %s, Invalid return type: %s.",
self.context.model_name, type(ret))
return create_predict_response(None, req_id_map,
"Invalid model predict output", 503)
if len(ret) != len(input_batch):
logger.warning(
"model: %s, number of batch response mismatched, expect: %d, got: %d.",
self.context.model_name, len(input_batch), len(ret))
return create_predict_response(
None, req_id_map, "number of batch response mismatched", 503)
duration = round((time.time() - start_time) * 1000, 2)
metrics.add_time(PREDICTION_METRIC, duration)
return create_predict_response(ret,
req_id_map,
"Prediction success",
200,
context=self.context)
def test_metrics(caplog):
"""
Test if metric classes methods behave as expected
Also checks global metric service methods
"""
# Create a batch of request ids
request_ids = {0: 'abcd', 1: "xyz", 2: "qwerty", 3: "hjshfj"}
all_req_ids = ','.join(request_ids.values())
model_name = "dummy model"
# Create a metrics objects
metrics = MetricsStore(request_ids, model_name)
# Counter tests
metrics.add_counter('CorrectCounter', 1, 1)
test_metric = metrics.cache[get_model_key('CorrectCounter', 'count', 'xyz',
model_name)]
assert 'CorrectCounter' == test_metric.name
metrics.add_counter('CorrectCounter', 1, 1)
metrics.add_counter('CorrectCounter', 1, 3)
metrics.add_counter('CorrectCounter', 1)
test_metric = metrics.cache[get_model_key('CorrectCounter', 'count',
all_req_ids, model_name)]
assert 'CorrectCounter' == test_metric.name
metrics.add_counter('CorrectCounter', 3)
test_metric = metrics.cache[get_model_key('CorrectCounter', 'count', 'xyz',
model_name)]
assert test_metric.value == 2
test_metric = metrics.cache[get_model_key('CorrectCounter', 'count',
'hjshfj', model_name)]
assert test_metric.value == 1
test_metric = metrics.cache[get_model_key('CorrectCounter', 'count',
all_req_ids, model_name)]
assert test_metric.value == 4
# Check what is emitted is correct
emit_metrics(metrics.store)
assert "hjshfj" in caplog.text
assert "ModelName:dummy model" in caplog.text
# Adding other types of metrics
# Check for time metric
with pytest.raises(Exception) as e_info:
metrics.add_time('WrongTime', 20, 1, 'ns')
assert "the unit for a timed metric should be one of ['ms', 's']" == e_info.value.args[
0]
metrics.add_time('CorrectTime', 20, 2, 's')
metrics.add_time('CorrectTime', 20, 0)
test_metric = metrics.cache[get_model_key('CorrectTime', 'ms', 'abcd',
model_name)]
assert test_metric.value == 20
assert test_metric.unit == 'Milliseconds'
test_metric = metrics.cache[get_model_key('CorrectTime', 's', 'qwerty',
model_name)]
assert test_metric.value == 20
assert test_metric.unit == 'Seconds'
# Size based metrics
with pytest.raises(Exception) as e_info:
metrics.add_size('WrongSize', 20, 1, 'TB')
assert "The unit for size based metric is one of ['MB','kB', 'GB', 'B']" == e_info.value.args[
0]
metrics.add_size('CorrectSize', 200, 0, 'GB')
metrics.add_size('CorrectSize', 10, 2)
test_metric = metrics.cache[get_model_key('CorrectSize', 'GB', 'abcd',
model_name)]
assert test_metric.value == 200
assert test_metric.unit == 'Gigabytes'
test_metric = metrics.cache[get_model_key('CorrectSize', 'MB', 'qwerty',
model_name)]
assert test_metric.value == 10
assert test_metric.unit == 'Megabytes'
# Check a percentage metric
metrics.add_percent('CorrectPercent', 20.0, 3)
test_metric = metrics.cache[get_model_key('CorrectPercent', 'percent',
'hjshfj', model_name)]
assert test_metric.value == 20.0
assert test_metric.unit == 'Percent'
# Check a error metric
metrics.add_error('CorrectError', 'Wrong values')
test_metric = metrics.cache[get_error_key('CorrectError', '')]
assert test_metric.value == 'Wrong values'