Querying the Details About a Model

You can use the API to query the information about a model object.

Sample Code

In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.

  • Method 1: Query the details about the model created in Importing a Model.

    from modelarts.session import Session
from modelarts.model import Model
session = Session()
model_instance = Model(session, model_id="input your model_id")
model_info = model_instance.get_model_info()

  • Method 2: Query the details about a model object returned in Obtaining the Model Object List.

    from modelarts.session import Session
from modelarts.model import Model
session = Session()
model_object_list = Model.get_model_object_list(session)
model_instance = model_object_list[0]
model_info = model_instance.get_model_info()

Parameter Description

Table 1 get_model_info response parameters

Parameter

Type

Description

model_id

String

Model ID

model_name

String

Model name

model_version

String

Model version

tenant

String

Tenant

project

String

Project

owner

String

User

create_at

Long

Time when a model is created, in milliseconds calculated from 1970.1.1 0:0:0 UTC

source_location

String

OBS path where a model resides

source_job_id

String

ID of the source training job

source_job_version

String

Version of the source training job

source_type

String

Type of a model source

  • If a model is deployed by an ExeML project, the value is auto.

  • If a model is deployed by a training job or OBS model file, this parameter is left blank.

model_type

String

Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.

model_size

Long

Model size, in bytes

model_status

String

Model status. The value can be publishing, published, or failed.

description

String

Model description

execution_code

String

OBS path for storing the execution code. The name of the execution code file is fixed to customize_service.py.

schema_doc

String

Download address of the model schema file

image_address

String

Execution image path of a model. Before the image is built, that is, before a model has been published as a service, this parameter is left blank.

input_params

params array

Collection of input parameters of a model. By default, this parameter is left blank.

output_params

params array

Collection of output parameters of a model. By default, this parameter is left blank.

dependencies

dependency array

Package required for running the code and model

model_metrics

String

Model evaluation parameter. This parameter is returned only when source_job_id and source_job_version are assigned values and the corresponding training job has evaluation results.

apis

String

All apis input and output parameters of the model

Table 2 params parameters

Parameter

Type

Description

url

String

API URL

param_name

String

Parameter name, which contains a maximum of 64 characters

param_type

String

Parameter type. The value can be int, string, float, timestamp, date, or file.

min

Number

When param_type is set to int or float and min is set during model creation, the value will be returned. By default, this parameter is left blank.

max

Number

When param_type is set to int or float and max is set during model creation, the value will be returned. By default, this parameter is left blank.

param_desc

String

Parameter description, which contains a maximum of 100 characters. By default, this parameter is left blank.

Table 3 dependency parameters

Parameter

Type

Description

installer

String

Installer

packages

package array

Collection of dependency packages

Table 4 package parameters

Parameter

Type

Description

package_name

String

Name of a dependency package

package_version

String

Version of a dependency package

restraint

String

Version filtering criterion. The options are as follows:

  • EXACT: the specified version

  • ATLEAST: not earlier than the specified version

  • ATMOST: not later than the specified version

Table 5 metric parameters

Parameter

Mandatory

Type

Description

f1

Yes

Double

Mean

recall

Yes

Double

Recall

precision

Yes

Double

Precision

accuracy

Yes

Double

Accuracy

last updated: 2024-12-11 13:55 UTC - commit: afb4a7dfa15e256dc933e946d9e874db041cb35c