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cloudwatch.py
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import pandas as pd
from decimal import Decimal
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
endpoint_metrics = {
"Invocation4XXErrors": {"Unit": "None", "Statistics": "Sum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "AWS/SageMaker"},
"Invocation5XXErrors": {"Unit": "None", "Statistics": "Sum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "AWS/SageMaker"},
"Invocations": {"Unit": "None", "Statistics": "Sum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "AWS/SageMaker"},
"InvocationsPerInstance": {"Unit": "None", "Statistics": "Sum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "AWS/SageMaker"},
"ModelLatency": {"Unit": "Microseconds", "Statistics": "None", "ExtendedStatistics": "p99", "Period": 60,
"Namespace": "AWS/SageMaker"},
"OverheadLatency": {"Unit": "Microseconds", "Statistics": "None", "ExtendedStatistics": "p99", "Period": 60,
"Namespace": "AWS/SageMaker"},
"CPUUtilization": {"Unit": "Percent", "Statistics": "Maximum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "/aws/sagemaker/Endpoints"},
"MemoryUtilization": {"Unit": "Percent", "Statistics": "Maximum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "/aws/sagemaker/Endpoints"},
"GPUUtilization": {"Unit": "Percent", "Statistics": "Maximum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "/aws/sagemaker/Endpoints"},
"GPUMemoryUtilization": {"Unit": "Percent", "Statistics": "Maximum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "/aws/sagemaker/Endpoints"},
"DiskUtilization": {"Unit": "Percent", "Statistics": "Maximum", "ExtendedStatistics": "None", "Period": 60,
"Namespace": "/aws/sagemaker/Endpoints"}}
invocation_metrics = ['Invocations', 'ModelLatency', 'OverheadLatency']
invocation_error_metrics = ['Invocation4XXErrors', 'Invocation5XXErrors']
hardware_metrics = ['CPUUtilization', 'MemoryUtilization', 'DiskUtilization']
gpu_metrics = ['GPUUtilization', 'GPUMemoryUtilization']
def get_inference_recommender_job_details(client, job_name):
job_details = client.describe_inference_recommendations_job(
JobName=job_name
)
return job_details
def get_job_results_as_dataframe(client, job_name):
job_details = get_inference_recommender_job_details(client, job_name)
if job_details['Status'] == 'COMPLETED':
endpoint_names = []
variant_names = []
instance_type = []
initial_count = []
cost_per_hour = []
cost_per_inference = []
max_invocations = []
model_latency = []
is_compiled = []
env_parameters = []
inference_recommendations = job_details['InferenceRecommendations']
start_time = job_details['CreationTime']
end_time = job_details['LastModifiedTime']
for endpoint_configuration in inference_recommendations:
endpoint_names.append(endpoint_configuration['EndpointConfiguration']['EndpointName'])
variant_names.append(endpoint_configuration['EndpointConfiguration']['VariantName'])
instance_type.append(endpoint_configuration['EndpointConfiguration']['InstanceType'])
initial_count.append(int(endpoint_configuration['EndpointConfiguration']['InitialInstanceCount']))
cost_per_hour.append(Decimal(endpoint_configuration['Metrics']['CostPerHour']))
cost_per_inference.append(Decimal(endpoint_configuration['Metrics']['CostPerInference']))
max_invocations.append(int(endpoint_configuration['Metrics']['MaxInvocations']))
model_latency.append(int(endpoint_configuration['Metrics']['ModelLatency']))
# is_compiled.append(endpoint_configuration['ModelConfiguration']['Compiled'])
env_parameters.append(endpoint_configuration['ModelConfiguration']
['EnvironmentParameters'])
if len(endpoint_names) == 0:
print(f'No endpoint recommendations for {job_name}')
exit(-1)
data = {
"InstanceType": instance_type,
"MaximumInvocations": max_invocations,
"ModelLatency": model_latency,
"CostPerHour": cost_per_hour,
"CostPerInference": cost_per_inference,
"EndpointName": endpoint_names,
"VariantName": variant_names,
"InitialCount": initial_count,
"EnvParameters": env_parameters,
"StartTime": start_time,
"EndTime": end_time
}
df = pd.DataFrame(data)
return df
else:
print(f"Job {job_name} is in {job_details['Status']} status, we can plot only completed jobs")
exit(0)
def get_cw_metrics(cw_client, endpoint_name, variant_name, metrics_name, start_time, end_time):
if endpoint_metrics[metrics_name]['Statistics'] != 'None':
cw_data = cw_client.get_metric_statistics(
Namespace=endpoint_metrics[metrics_name]['Namespace'],
MetricName=metrics_name,
Dimensions=[
{
'Name': 'EndpointName',
'Value': endpoint_name
},
{
'Name': 'VariantName',
'Value': variant_name
}
],
StartTime=start_time,
EndTime=end_time,
Period=endpoint_metrics[metrics_name]['Period'],
Statistics=[
endpoint_metrics[metrics_name]['Statistics'],
],
Unit=endpoint_metrics[metrics_name]['Unit']
)
elif endpoint_metrics[metrics_name]['ExtendedStatistics'] != 'None':
cw_data = cw_client.get_metric_statistics(
Namespace=endpoint_metrics[metrics_name]['Namespace'],
MetricName=metrics_name,
Dimensions=[
{
'Name': 'EndpointName',
'Value': endpoint_name
},
{
'Name': 'VariantName',
'Value': variant_name
}
],
StartTime=start_time,
EndTime=end_time,
Period=endpoint_metrics[metrics_name]['Period'],
ExtendedStatistics=[
endpoint_metrics[metrics_name]['ExtendedStatistics'],
],
Unit=endpoint_metrics[metrics_name]['Unit']
)
else:
print(f'Both ExtendedStatistics & Statistics are None')
exit(0)
return cw_data
def get_x_from_datapoints(datapoints):
timestamps = []
for datapoint in datapoints:
timestamps.append(datapoint['Timestamp'])
return timestamps
def get_y_from_datapoints(datapoints, metric_statistics):
values = []
for datapoint in datapoints:
values.append(datapoint[metric_statistics])
return values
def get_y_from_extended_datapoints(datapoints, metric_statistics):
values = []
for datapoint in datapoints:
values.append(datapoint['ExtendedStatistics'][metric_statistics])
return values
def get_unit_from_datapoints(datapoints):
for datapoint in datapoints:
return datapoint['Unit']
def sort_cw_datapoints_by_timestamp(datapoints):
datapoints.sort(key=lambda x: x["Timestamp"])
return datapoints
def get_endpoint_metrics(sm_client, cw_client, region, job_name, include_plots=False):
df = get_job_results_as_dataframe(sm_client, job_name)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 3)
for record in df.to_dict('records'):
if include_plots:
fig = plt.figure(figsize=(20, 16), constrained_layout=True)
fig.suptitle(f"Instance type {record['InstanceType']} Endpoint {record['EndpointName']}",
fontsize=16)
spec = gridspec.GridSpec(ncols=3, nrows=3, figure=fig)
f_ax1 = fig.add_subplot(spec[0, 0])
f_ax2 = fig.add_subplot(spec[0, 1])
f_ax3 = fig.add_subplot(spec[0, 2])
f_ax4 = fig.add_subplot(spec[1, 0])
f_ax5 = fig.add_subplot(spec[1, 1])
f_ax6 = fig.add_subplot(spec[1, 2])
f_ax7 = fig.add_subplot(spec[2, 0])
f_ax8 = fig.add_subplot(spec[2, 1])
f_ax9 = fig.add_subplot(spec[2, 2])
invocation_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'], 'Invocations',
record['StartTime'], record['EndTime'])
sorted_invocation_data = sort_cw_datapoints_by_timestamp(invocation_data['Datapoints'])
if include_plots:
f_ax1_x = get_x_from_datapoints(sorted_invocation_data)
f_ax1_y = get_y_from_datapoints(sorted_invocation_data, 'Sum')
f_ax1.set_title('Invocations')
f_ax1.set_ylabel('No of Invocations')
f_ax1.plot(f_ax1_x, f_ax1_y)
model_latency_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'], 'ModelLatency',
record['StartTime'], record['EndTime'])
sorted_model_latency_data = sort_cw_datapoints_by_timestamp(model_latency_data['Datapoints'])
if include_plots:
f_ax2_x = get_x_from_datapoints(sorted_model_latency_data)
f_ax2_y = get_y_from_extended_datapoints(sorted_model_latency_data, 'p99')
f_ax2.set_title('ModelLatency')
f_ax2.set_ylabel(get_unit_from_datapoints(sorted_model_latency_data))
f_ax2.plot(f_ax2_x, f_ax2_y)
overhead_latency_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'OverheadLatency', record['StartTime'], record['EndTime'])
sort_overhead_latency_data = sort_cw_datapoints_by_timestamp(overhead_latency_data['Datapoints'])
if include_plots:
f_ax3_x = get_x_from_datapoints(sort_overhead_latency_data)
f_ax3_y = get_y_from_extended_datapoints(sort_overhead_latency_data, 'p99')
f_ax3.set_title('OverheadLatency')
f_ax3.set_ylabel(get_unit_from_datapoints(sort_overhead_latency_data))
f_ax3.plot(f_ax3_x, f_ax3_y)
cpu_utilization_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'CPUUtilization', record['StartTime'], record['EndTime'])
sorted_cpu_utilization_data = sort_cw_datapoints_by_timestamp(cpu_utilization_data['Datapoints'])
if include_plots:
f_ax4_x = get_x_from_datapoints(sorted_cpu_utilization_data)
f_ax4_y = get_y_from_datapoints(sorted_cpu_utilization_data, 'Maximum')
f_ax4.set_title('CPUUtilization')
f_ax4.set_ylabel(get_unit_from_datapoints(sorted_cpu_utilization_data))
f_ax4.plot(f_ax4_x, f_ax4_y)
memory_utilization_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'MemoryUtilization', record['StartTime'], record['EndTime'])
sorted_memory_utilization_data = sort_cw_datapoints_by_timestamp(memory_utilization_data['Datapoints'])
if include_plots:
f_ax5_x = get_x_from_datapoints(sorted_memory_utilization_data)
f_ax5_y = get_y_from_datapoints(sorted_memory_utilization_data, 'Maximum')
f_ax5.set_title('MemoryUtilization')
f_ax5.set_ylabel(get_unit_from_datapoints(sorted_memory_utilization_data))
f_ax5.plot(f_ax5_x, f_ax5_y)
disk_utilization_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'DiskUtilization', record['StartTime'], record['EndTime'])
sorted_disk_utilization_data = sort_cw_datapoints_by_timestamp(disk_utilization_data['Datapoints'])
if include_plots:
f_ax6_x = get_x_from_datapoints(sorted_disk_utilization_data)
f_ax6_y = get_y_from_datapoints(sorted_disk_utilization_data, 'Maximum')
f_ax6.set_title('DiskUtilization')
f_ax6.set_ylabel(get_unit_from_datapoints(sorted_disk_utilization_data))
f_ax6.plot(f_ax6_x, f_ax6_y)
user_error_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'Invocation4XXErrors', record['StartTime'], record['EndTime'])
sorted_user_error_data = sort_cw_datapoints_by_timestamp(user_error_data['Datapoints'])
if include_plots:
f_ax7_x = get_x_from_datapoints(sorted_user_error_data)
f_ax7_y = get_y_from_datapoints(sorted_user_error_data, 'Sum')
f_ax7.set_title('Invocation4XXErrors')
f_ax7.set_ylabel(get_unit_from_datapoints(sorted_user_error_data))
f_ax7.plot(f_ax7_x, f_ax7_y)
system_error_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'Invocation5XXErrors', record['StartTime'], record['EndTime'])
sorted_system_error_data = sort_cw_datapoints_by_timestamp(system_error_data['Datapoints'])
if include_plots:
f_ax8_x = get_x_from_datapoints(sorted_system_error_data)
f_ax8_y = get_y_from_datapoints(sorted_system_error_data, 'Sum')
f_ax8.set_title('Invocation5XXErrors')
f_ax8.set_ylabel(get_unit_from_datapoints(sorted_system_error_data))
f_ax8.plot(f_ax8_x, f_ax8_y)
per_instance_data = get_cw_metrics(cw_client, record['EndpointName'], record['VariantName'],
'InvocationsPerInstance', record['StartTime'], record['EndTime'])
sorted_per_instance_data = sort_cw_datapoints_by_timestamp(per_instance_data['Datapoints'])
if include_plots:
f_ax9_x = get_x_from_datapoints(sorted_per_instance_data)
f_ax9_y = get_y_from_datapoints(sorted_per_instance_data, 'Sum')
f_ax9.set_title('InvocationsPerInstance')
f_ax9.set_ylabel(get_unit_from_datapoints(sorted_per_instance_data))
f_ax9.plot(f_ax9_x, f_ax9_y)
if include_plots:
plt.show()
return df