Updated on 2024-06-12 GMT+08:00

Obtaining Models

Sample Code

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

  • Scenario 1: Obtain all models of a user.
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    from modelarts.session import Session 
    from modelarts.model import Model
    
    session = Session() 
    model_list = Model.get_model_list(session)
    
  • Scenario 2: Obtain the models of a user based on search criteria.
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    from modelarts.session import Session 
    from modelarts.model import Model
    
    session = Session() 
    model_list = Model.get_model_list(session, model_status="published", model_name="digit", order="desc")
    

Parameters

Table 1 Query parameters

Parameter

Mandatory

Type

Description

model_name

No

String

Model name. Fuzzy match is supported.

model_version

No

String

Model version

model_status

No

String

Model status. The value can be publishing, published, or failed. You can obtain jobs based on their statuses.

description

No

String

Description. Fuzzy match is supported.

offset

No

Integer

Index of the page to be queried. Default value: 0

limit

No

Integer

Maximum number of records returned on each page. Default value: 280

sort_by

No

String

Sorting mode. The value can be create_at, model_version, or model_size. Default value: create_at

order

No

String

Sorting order. The value can be asc or desc, indicating the ascending or descending order. Default value: desc

workspace_id

No

String

Workspace ID. Default value: 0

Table 2 get_model_list parameters

Parameter

Type

Description

total_count

Integer

Total number of models that meet the search criteria when no paging is implemented

count

Integer

Number of models

models

model array

Model metadata

Table 3 model parameters

Parameter

Type

Description

model_id

String

Model ID

model_name

String

Model name

model_version

String

Model version

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

tenant

String

Tenant to whom a model belongs

project

String

Project to which a model belongs

owner

String

User to whom a model belongs

create_at

Long

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

description

String

Model description

source_type

String

Model source type. This parameter is valid only when the model is deployed through ExeML. The value is auto.