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Help Center/ ModelArts/ SDK Reference/ Training Management (Old Version)/ Training Jobs/ Querying the Details About a Training Job

Querying the Details About a Training Job

Updated on 2024-03-21 GMT+08:00

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()
    print(job_info)
    
  • Method 2: Use the training job created in Creating a Training Job.
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    job_info = job_instance.get_job_info()
    print(job_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.

job_id

Yes

String

ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.

version_id

Yes

String

Version ID of a training job. You can query version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response obtained in Querying the List of 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.

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 the 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

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