Querying the Details About a Training Job Configuration

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.

Method 1: Use the specified config_name.

      
           from modelarts.session import Session
from modelarts.estimator import Estimator
session = Session()
estimator = Estimator(modelarts_session=session, config_name="my_job_config")
job_paras_info = estimator.get_job_configs_info()
print(job_paras_info)

Method 2: Use the object returned in Creating a Training Job Configuration.

      
           job_paras_info = job_config_instance.get_job_configs_info()
print(job_paras_info)

Method 3: Use the object returned in Querying the List of Training Job Parameter Configuration Objects.

      
           job_paras_info = job_config_instance_list[0].get_job_configs_info()
print(job_paras_info)

Parameters

**Table 1** Estimator request parameters
Parameter	Mandatory	Type	Description
modelarts_session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.
config_name	Yes	String	Name of a training job parameter configuration

**Table 2** **get_job_configs_info** response parameters
Parameter	Type	Description
error_msg	String	Error message when the API call fails. This parameter is not included when the API call succeeds.
error_code	String	Error code when the API fails to be called. For details, see Error Codes. This parameter is not included when the API call succeeds.
config_name	String	Name of a training job parameter configuration
config_desc	String	Description of a training job parameter configuration
worker_server_num	Integer	Number of workers in a training job
app_url	String	Code directory of a training job
boot_file_url	String	Boot file of a training job
model_id	Long	Model ID of a training job
parameter	JSON Array	Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
spec_id	Long	ID of the resource specifications selected for a training job
data_url	String	Dataset of a training job
dataset_id	String	Dataset ID of a training job
dataset_version_id	String	Dataset version ID of a training job
engine_type	Short	Engine type of a training job
engine_name	String	Name of the engine selected for a training job
engine_id	Long	ID of the engine selected for a training job
engine_version	String	Version of the engine selected for a training job
train_url	String	OBS URL of the output file of a training job. By default, this parameter is left blank. Example value: /usr/train/
log_url	String	OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/train/
user_image_url	String	SWR URL of the custom image used by a training job
user_command	String	Boot command used to start the container of the custom image of a training job
is_success	Boolean	Whether the API call succeeds