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ModelArts Metrics

Description

The cloud service platform provides Cloud Eye to help you better understand the status of your ModelArts real-time services and models. You can use Cloud Eye to automatically monitor your ModelArts real-time services and models in real time and manage alarms and notifications, so that you can keep track of performance metrics of ModelArts and models.

Namespace

SYS.ModelArts

Monitoring Metrics

Table 1 ModelArts metrics

Metric ID

Metric Name

Meaning

Value Range

Measurement Object & Dimension

Monitoring Interval

cpu_usage

CPU Usage

CPU usage of ModelArts

Unit: %

≥ 0%

Measurement object:

ModelArts models

Dimension:

model_id

1 minute

mem_usage

Memory Usage

Memory usage of ModelArts

Unit: %

≥ 0%

Measurement object:

ModelArts models

Dimension:

model_id

1 minute

gpu_util

GPU Usage

GPU usage of ModelArts

Unit: %

≥ 0%

Measurement object:

ModelArts models

Dimension:

model_id

1 minute

gpu_mem_usage

GPU Memory Usage

GPU memory usage of ModelArts

Unit: %

≥ 0%

Measurement object:

ModelArts models

Dimension:

model_id

1 minute

successfully_called_times

Number of Successful Calls

Times that ModelArts has been successfully called

Unit: Times/min

≥Count/min

Measurement object:

ModelArts models

ModelArts real-time services

Dimension:

model_id,

service_id

1 minute

failed_called_times

Number of Failed Calls

Times that ModelArts failed to be called

Unit: Times/min

≥Count/min

Measurement object:

ModelArts models

ModelArts real-time services

Dimension:

model_id,

service_id

1 minute

total_called_times

API Calls

Times that ModelArts is called

Unit: Times/min

≥Count/min

Measurement object:

ModelArts models

ModelArts real-time services

Dimension:

model_id,

service_id

1 minute

If a measurement object has multiple measurement dimensions, all the measurement dimensions are mandatory when you use an API to query monitoring metrics.

  • The following provides an example of using the multi-dimensional dim to query a single monitoring metric: dim.0=service_id,530cd6b0-86d7-4818-837f-935f6a27414d&dim.1="model_id,3773b058-5b4f-4366-9035-9bbd9964714a
  • The following provides an example of using the multi-dimensional dim to query monitoring metrics in batches:

    "dimensions": [

    {

    "name": "service_id",

    "value": "530cd6b0-86d7-4818-837f-935f6a27414d"

    }

    {

    "name": "model_id",

    "value": "3773b058-5b4f-4366-9035-9bbd9964714a"

    }

    ],

Dimensions

Table 2 Dimension description

Key

Value

service_id

Real-time service ID

model_id

Model ID
Parent topic: Monitoring