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On this page

Creating a Training Job Configuration

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.

  • Example 1: Create a training job parameter configuration using the data stored on OBS.
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    from modelarts.session import Session
    from modelarts.estimator import Estimator
    session = Session()
    estimator = Estimator(
                          modelarts_session=session,
                          framework_type='PyTorch',                                     # AI engine name
                          framework_version='PyTorch-1.0.0-python3.6',                  # AI engine version
                          code_dir='/bucket/src/',                                      # Training script directory
                          boot_file='/bucket/src/pytorch_sentiment.py',                 # Training boot script directory
                          log_url='/bucket/log/',                                       # Training log directory
                          hyperparameters=[
                                           {"label":"classes",
                                            "value": "10"},    
                                           {"label":"lr",
                                            "value": "0.001"}
                                           ],
                          output_path='/bucket/output/',                                 # Training output directory
                          train_instance_type='modelarts.vm.gpu.p100',                   # Training environment flavor
                          train_instance_count=1)                                        # Number of training nodes
    job_config_instance = estimator.create_job_configs(config_name='my_job_config', inputs='/bucket/data/train/', config_desc='my job config')
    
  • Example 2: Create a training job parameter configuration using a dataset.
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    from modelarts.session import Session
    from modelarts.estimator import Estimator
    session = Session()
    estimator = Estimator(
                          modelarts_session=session,
                          framework_type='PyTorch',                                     # AI engine name
                          framework_version='PyTorch-1.0.0-python3.6',                  # AI engine version
                          code_dir='/bucket/src/',                                      # Training script directory
                          boot_file='/bucket/src/pytorch_sentiment.py',                 # Training boot script directory
                          log_url='/bucket/log/',                                       # Training log directory
                          hyperparameters=[
                                           {"label":"classes",
                                            "value": "10"},    
                                           {"label":"lr",
                                            "value": "0.001"}
                                          ],
                          output_path='/bucket/output/',                                 # Training output directory
                          train_instance_type='modelarts.vm.gpu.p100',                   # Training environment flavor
                          train_instance_count=1)                                        # Number of training nodes
    job_config_instance = estimator.create_job_configs(config_name='my_job_config', dataset_id='4AZNvFkN7KYr5EdhFkH', dataset_version_id='UOF9BIeSGArwVt0oI6T', config_desc='my job config')
    

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.

train_instance_count

Yes

Long

Number of workers in a training job

code_dir

No

String

Code directory of a training job, for example, /bucket/src/. Leave this parameter blank when model_name is set.

boot_file

No

String

Boot file of a training job, which needs to be stored in the code directory. For example, /bucket/src/boot.py. Leave this parameter blank when model_name is set.

model_name

No

Long

Name of the built-in algorithm used by a training job. If you have configured model_name, you do not need to configure app_url, boot_file_url, framework_type, and framework_version.

output_path

Yes

String

Output path of a training job

hyperparameters

No

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.

log_url

No

String

OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/

train_instance_type

Yes

Long

Resource flavor selected for a training job. If you choose to train on the training platform, obtain the value by calling the API described in Querying the List of Resource Flavors.

framework_type

No

String

Engine selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.

framework_version

No

String

Engine version selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.

job_description

No

String

Description of a training job

user_image_url

No

String

SWR URL of the custom image used by a training job. Example value: 100.125.5.235:20202/jobmng/custom-cpu-base:1.0

user_command

No

String

Boot command used to start the container of the custom image of a training job. The format is bash /home/work/run_train.sh python /home/work/user-job-dir/app/train.py {python_file_parameter}.

Table 2 create_job_configs request parameters

Parameter

Mandatory

Type

Description

config_name

No

String

Name of a training job parameter configuration. The value is a string of 1 to 20 characters consisting of only digits, letters, underscores (_), and hyphens (-). By default, if this parameter is left blank, the value is dynamically generated by date.

config_desc

No

String

Description of a training job parameter configuration. The value is a string of 0 to 256 characters. By default, this parameter is left blank.

inputs

No

String

OBS storage path of a training job

dataset_id

No

String

Dataset ID of a training job. This parameter must be used with dataset_version_id, but cannot be used with inputs.

dataset_version_id

No

String

Dataset version ID of a training job. This parameter must be used with dataset_id, but cannot be used with inputs.

Table 3 create_job_configs response parameters

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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