Querying the List of Training Job Parameter Configuration Objects
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.
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from modelarts.session import Session from modelarts.estimator import Estimator session = Session() job_config_instance_list = Estimator.get_job_configs_object_list(modelarts_session=session, is_show=True, per_page=10, page=1, sort_by="create_time", order="asc", search_content="configname") print(job_config_instance_list) |
Parameters
Parameter |
Mandatory |
Type |
Description |
---|---|---|---|
modelarts_session |
Yes |
Object |
Session object. For details about the initialization method, see Session Authentication. |
per_page |
No |
Integer |
Number of job parameters displayed on each page. The value range is [1, 1000]. Default value: 10 |
page |
No |
Integer |
Index of the page to be queried. Default value: 1 |
sortBy/sort_by |
No |
String |
When AK/SK-based authentication is used, the parameter name is sortBy. When the username and password are used for authentication, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, engine_type, or create_time. Default value: job_name |
order |
No |
String |
Sorting order. The options are as follows:
|
search_content |
No |
String |
Search content, for example, a parameter name. By default, this parameter is left blank. |
is_show |
No |
Boolean |
Whether to print the training job parameter configuration list. Default value: True |
A training object list is returned in the successful response to get_job_configs_object_list. For details, see Table 2.
Parameter |
Type |
Description |
---|---|---|
TrainingJob |
Object |
Training object. This object contains attributes such as config_name, and operations on a training job parameter configuration, such as querying or deleting the training job parameter configuration. For example, you can use job_config_instance.config_name to obtain the name of a training job parameter configuration. |
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