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.

      
       
         
         
           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.

      
       
         
         
           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.