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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: ml_samples_genAI_monitors_configuration.py
DESCRIPTION:
These samples demonstrate how to set up monitors in GenAI
USAGE:
python ml_samples_genAI_monitors_configuration.py
"""
import os
from azure.ai.ml import MLClient
from azure.ai.ml.entities import (
MonitorSchedule,
CronTrigger,
MonitorDefinition,
ServerlessSparkCompute,
MonitoringTarget,
GenerationTokenStatisticsSignal,
GenerationTokenStatisticsMonitorMetricThreshold,
GenerationSafetyQualitySignal,
GenerationSafetyQualityMonitoringMetricThreshold,
BaselineDataRange,
AlertNotification,
LlmData,
)
from azure.ai.ml.entities._inputs_outputs import Input
from azure.ai.ml.constants import MonitorTargetTasks, MonitorDatasetContext
# Authentication package
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
resource_group = os.environ["RESOURCE_GROUP_NAME"]
workspace_name = "test-ws1"
aoai_connection_name = "INSERT_YOUR_AOAI_CONNECTION_NAME"
aoai_deployment_name = "INSERT_YOUR_AOAI_DEPLOYMENT_NAME"
endpoint_name = "INSERT_YOUR_ENDPOINT_NAME"
deployment_name = "INSERT_YOUR_DEPLOYMENT_NAME"
app_trace_name = "app_traces"
app_trace_Version = "1"
# Default Monitor configuration for GenAI apps - Enable monitoring with minimal configurations
ml_client = MLClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group,
workspace_name=workspace_name,
)
class GenAIMonitoringSamples(object):
def ml_gen_ai_monitor_default(self):
# [START default_monitoring]
spark_compute = ServerlessSparkCompute(instance_type="standard_e4s_v3", runtime_version="3.4")
monitoring_target = MonitoringTarget(
ml_task=MonitorTargetTasks.QUESTION_ANSWERING,
endpoint_deployment_id=f"azureml:{endpoint_name}:{deployment_name}",
)
monitoring_target = MonitoringTarget(
ml_task=MonitorTargetTasks.QUESTION_ANSWERING,
endpoint_deployment_id=f"azureml:{endpoint_name}:{deployment_name}",
)
monitor_settings = MonitorDefinition(compute=spark_compute, monitoring_target=monitoring_target)
model_monitor = MonitorSchedule(
name="qa_model_monitor", trigger=CronTrigger(expression="15 10 * * *"), create_monitor=monitor_settings
)
ml_client.schedules.begin_create_or_update(model_monitor)
# [END default_monitoring]
def ml_gen_ai_monitor_advance(self):
# [START advance_monitoring]
spark_compute = ServerlessSparkCompute(instance_type="standard_e4s_v3", runtime_version="3.4")
monitoring_target = MonitoringTarget(
ml_task=MonitorTargetTasks.QUESTION_ANSWERING,
endpoint_deployment_id=f"azureml:{endpoint_name}:{deployment_name}",
)
token_statistics_signal = GenerationTokenStatisticsSignal()
generation_quality_thresholds = GenerationSafetyQualityMonitoringMetricThreshold(
fluency={"acceptable_fluency_score_per_instance": 4, "aggregated_fluency_pass_rate": 0.5},
coherence={"acceptable_coherence_score_per_instance": 4, "aggregated_coherence_pass_rate": 0.5},
)
input_data = Input(
type="uri_folder",
path=f"{endpoint_name}-{deployment_name}-{app_trace_name}:{app_trace_Version}",
)
data_window = BaselineDataRange(lookback_window_size="P7D", lookback_window_offset="P0D")
production_data = LlmData(
data_column_names={"prompt_column": "question", "completion_column": "answer"},
input_data=input_data,
data_window=data_window,
)
generation_quality_signal = GenerationSafetyQualitySignal(
connection_id=f"/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}/connections/{aoai_connection_name}",
metric_thresholds=generation_quality_thresholds,
production_data=[production_data],
sampling_rate=1.0,
properties={
"aoai_deployment_name": aoai_deployment_name,
"enable_action_analyzer": "false",
"azureml.modelmonitor.gsq_thresholds": '[{"metricName":"average_fluency","threshold":{"value":4}},{"metricName":"average_coherence","threshold":{"value":4}}]',
},
)
monitoring_signals = {
"token-usage-signal": token_statistics_signal,
"gsq-signal": generation_quality_signal,
}
alert_notification = AlertNotification(emails=["test@example.com", "def@example.com"])
monitor_settings = MonitorDefinition(
compute=spark_compute,
monitoring_target=monitoring_target,
monitoring_signals=monitoring_signals, # type:ignore
alert_notification=alert_notification,
)
model_monitor = MonitorSchedule(
name="monitor-name-2", trigger=CronTrigger(expression="15 10 * * *"), create_monitor=monitor_settings
)
ml_client.schedules.begin_create_or_update(model_monitor)
if __name__ == "__main__":
sample = GenAIMonitoringSamples()
sample.ml_gen_ai_monitor_default()
sample.ml_gen_ai_monitor_advance()
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