doc-exports/docs/modelarts/sdk-ref/modelarts_04_0161.html

Obtaining Details About a Training Job

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 job_id and version_id.

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  from modelarts.session import Session from modelarts.estimator import Estimator session = Session() estimator = Estimator(modelarts_session=session, job_id="182626", version_id="278813") job_info = estimator.get_job_info()

• Method 2: Use the training job created in Creating a Training Job.

  1	job_info = job_instance.get_job_info()

• Method 3: Use the training job version object returned in Querying the List of Training Job Versions.

  1	job_info = job_version_instance_list[0].get_job_info()

Parameter Description

Table 1 Estimator request parameters

Parameter	Mandatory	Type	Description
modelarts_session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.
job_id	Yes	String	ID of a training job. Obtain job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response in Obtaining Training Jobs.
version_id	Yes	String	Version ID of a training job. Obtain version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response in Obtaining Training Jobs.

Table 2 get_job_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 in ModelArts API Reference. This parameter is not included when the API call succeeds.
is_success	Boolean	Whether the API call succeeds
job_id	Long	Training job ID
job_name	String	Training job name
job_desc	String	Description of a training job
version_id	Long	Version ID of a training job
version_name	String	Version name of a training job
pre_version_id	Long	Name of the previous version of a training job
engine_type	Short	Engine type of a training job. The mapping between engine_type and engine_name is as follows: engine_type: 1, engine_name: TensorFlow engine_type: 2, engine_name: MXNet engine_type: 3, engine_name: Ray engine_type: 4, engine_name: Caffe engine_type: 5, engine_name: Spark_MLlib engine_type: 9, engine_name: XGBoost-Sklearn engine_type: 10, engine_name: PyTorch engine_type: 12, engine_name: Horovod
engine_name	String	Name of the engine selected for a training job. Currently, the following engines are supported: Caffe Horovod MXNet PyTorch Ray Spark_MLlib TensorFlow XGBoost-Sklearn
engine_id	Long	ID of the engine selected for a training job
engine_version	String	Version of the engine selected for a training job
status	Integer	Status of a training job. For details about job statuses, see Job Statuses.
app_url	String	Code directory of a training job
boot_file_url	String	Boot file of a training job
create_time	Long	Time when a training job is created
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.
duration	Long	Training job running duration, in milliseconds
spec_id	Long	ID of the resource specifications selected for a training job
core	String	Number of cores of the resource specifications
cpu	String	CPU memory of the resource specifications
gpu_num	Integer	Number of GPUs of the resource specifications
gpu_type	String	GPU type of the resource specifications
worker_server_num	Integer	Number of workers in a training job
data_url	String	Dataset of a training job
train_url	String	OBS path to the training job output file
dataset_version_id	String	Dataset version ID of a training job
dataset_id	String	Dataset ID of a training job
data_source	JSON Array	Datasets of a training job
model_id	Long	Model ID of a training job
model_metric_list	JSON Array	Model metrics of a training job
system_metric_list	JSON Array	System monitoring metrics of a training job
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

Table 3 data_source parameters

Parameter	Type	Description
dataset_id	String	Dataset ID of a training job
dataset_version	String	Dataset version ID of a training job
type	String	Dataset type obs: Data from OBS is used. dataset: Data from a specified dataset is used.
data_url	String	OBS bucket path

Table 4 model_metric_list parameters

Parameter	Type	Description
metric	JSON Array	Validation metrics of a class of a training job
total_metric	JSON Array	All validation metrics of a training job

Table 5 system_metric_list parameters

Parameter	Type	Description
cpuUsage	JSON Array	CPU usage of a training job
memUsage	JSON Array	Memory usage of a training job
gpuUtil	JSON Array	GPU usage of a training job

Table 6 metric parameters

Parameter	Type	Description
metric_values	JSON Array	Validation metrics of a class of a training job
reserved_data	JSON Array	Reserved parameter
metric_meta	JSON Array	A class of a training job, including the class ID and name

Table 7 metric_values parameters

Parameter	Type	Description
recall	JSON Array	Recall of a class of a training job
precision	JSON Array	Precision of a class of a training job
accuracy	JSON Array	Accuracy of a class of a training job

Table 8 total_metric parameters

Parameter	Type	Description
total_metric_meta	JSON Array	Reserved parameter
total_reserved_data	JSON Array	Reserved parameter
total_metric_values	JSON Array	All validation metrics of a training job

Table 9 total_metric_values parameters

Parameter	Type	Description
f1_score	Float	F1 score of a training job
recall	Float	Total recall of a training job
precision	Float	Total precision of a training job
accuracy	Float	Total accuracy of a training job

Parent topic: Training Jobs