def bias_monitor(sagemaker_session):
monitor = ModelBiasMonitor(
role=ROLE,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
volume_size_in_gb=VOLUME_SIZE_IN_GB,
max_runtime_in_seconds=MAX_RUNTIME_IN_SECONDS,
sagemaker_session=sagemaker_session,
tags=TEST_TAGS,
)
return monitor
def _generate_model_monitor(self, mm_type: str) -> Optional[ModelMonitor]:
"""Generates a ModelMonitor object
Generates a ModelMonitor object with required config attributes for
QualityCheckStep and ClarifyCheckStep
Args:
mm_type (str): The subclass type of ModelMonitor object.
A valid mm_type should be one of the following: "DefaultModelMonitor",
"ModelQualityMonitor", "ModelBiasMonitor", "ModelExplainabilityMonitor"
Return:
sagemaker.model_monitor.ModelMonitor or None if the mm_type is not valid
"""
if mm_type == "DefaultModelMonitor":
monitor = DefaultModelMonitor(
role=self.role,
instance_count=self.instance_count,
instance_type=self.instance_type,
volume_size_in_gb=self.volume_size_in_gb,
volume_kms_key=self.volume_kms_key,
output_kms_key=self.output_kms_key,
max_runtime_in_seconds=self.max_runtime_in_seconds,
base_job_name=self.base_job_name,
sagemaker_session=self.sagemaker_session,
env=self.env,
tags=self.tags,
network_config=self.network_config,
)
elif mm_type == "ModelQualityMonitor":
monitor = ModelQualityMonitor(
role=self.role,
instance_count=self.instance_count,
instance_type=self.instance_type,
volume_size_in_gb=self.volume_size_in_gb,
volume_kms_key=self.volume_kms_key,
output_kms_key=self.output_kms_key,
max_runtime_in_seconds=self.max_runtime_in_seconds,
base_job_name=self.base_job_name,
sagemaker_session=self.sagemaker_session,
env=self.env,
tags=self.tags,
network_config=self.network_config,
)
elif mm_type == "ModelBiasMonitor":
monitor = ModelBiasMonitor(
role=self.role,
instance_count=self.instance_count,
instance_type=self.instance_type,
volume_size_in_gb=self.volume_size_in_gb,
volume_kms_key=self.volume_kms_key,
output_kms_key=self.output_kms_key,
max_runtime_in_seconds=self.max_runtime_in_seconds,
base_job_name=self.base_job_name,
sagemaker_session=self.sagemaker_session,
env=self.env,
tags=self.tags,
network_config=self.network_config,
)
elif mm_type == "ModelExplainabilityMonitor":
monitor = ModelExplainabilityMonitor(
role=self.role,
instance_count=self.instance_count,
instance_type=self.instance_type,
volume_size_in_gb=self.volume_size_in_gb,
volume_kms_key=self.volume_kms_key,
output_kms_key=self.output_kms_key,
max_runtime_in_seconds=self.max_runtime_in_seconds,
base_job_name=self.base_job_name,
sagemaker_session=self.sagemaker_session,
env=self.env,
tags=self.tags,
network_config=self.network_config,
)
else:
logging.warning(
'Expected model monitor types: "DefaultModelMonitor", "ModelQualityMonitor", '
'"ModelBiasMonitor", "ModelExplainabilityMonitor"')
return None
return monitor
def test_run_bias_monitor_baseline(
sagemaker_session,
data_config,
model_config,
bias_config,
model_predicted_label_config,
endpoint_name,
ground_truth_input,
upload_actual_data,
):
monitor = ModelBiasMonitor(
role=ROLE,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
volume_size_in_gb=VOLUME_SIZE_IN_GB,
max_runtime_in_seconds=MAX_RUNTIME_IN_SECONDS,
sagemaker_session=sagemaker_session,
tags=TEST_TAGS,
)
baselining_job_name = utils.unique_name_from_base("bias-baselining-job")
print("Creating baselining job: {}".format(baselining_job_name))
monitor.suggest_baseline(
data_config=data_config,
bias_config=bias_config,
model_config=model_config,
model_predicted_label_config=model_predicted_label_config,
job_name=baselining_job_name,
)
assert (monitor.latest_baselining_job_config.
probability_threshold_attribute == BIAS_PROBABILITY_THRESHOLD)
monitoring_schedule_name = utils.unique_name_from_base(
"bias-suggest-baseline")
s3_uri_monitoring_output = os.path.join(
"s3://",
sagemaker_session.default_bucket(),
endpoint_name,
monitoring_schedule_name,
"monitor_output",
)
# Let's test if the schedule can pick up analysis_config from baselining job
monitor.create_monitoring_schedule(
output_s3_uri=s3_uri_monitoring_output,
monitor_schedule_name=monitoring_schedule_name,
endpoint_input=EndpointInput(
endpoint_name=endpoint_name,
destination=ENDPOINT_INPUT_LOCAL_PATH,
start_time_offset=START_TIME_OFFSET,
end_time_offset=END_TIME_OFFSET,
),
ground_truth_input=ground_truth_input,
schedule_cron_expression=CRON,
)
_verify_execution_status(monitor)
_verify_bias_job_description(
sagemaker_session=sagemaker_session,
monitor=monitor,
endpoint_name=endpoint_name,
ground_truth_input=ground_truth_input,
)
monitor.delete_monitoring_schedule()