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

Creating 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.

  • 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.