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) print(model_list)
- 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") print(model_list)
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 |
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 |
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. |
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