Create a training job using the API, AWS CLI, SageMaker SDK

To use SageMaker training plans for your SageMaker training job, specify the TrainingPlanArn parameter of the desired plan in the ResourceConfig when calling the CreateTrainingJob API operation. You can use exactly one plan per job.

Run a training job on a plan using the CLI

The following example demonstrates how to create a SageMaker training job and associate it with a provided training plan using the TrainingPlanArn attribute in the create-training-job AWS CLI command.

For more information about how to create a training job using the AWS CLI CreateTrainingJob command, see create-training-job.

# Create a training job
aws sagemaker create-training-job \
  --training-job-name training-job-name \
  ...
    
  --resource-config '{
        "InstanceType": "ml.p5.48xlarge",
        "InstanceCount": 8,
        "VolumeSizeInGB": 10,
        "TrainingPlanArn": "training-plan-arn"
        }
    }' \
    ...

This AWS CLI example command creates a new training job in SageMaker AI passing a training plan in the --resource-config argument.

aws sagemaker create-training-job \
  --training-job-name job-name \
  --role-arn arn:aws:iam::123456789123:role/DataAndAPIAccessRole \
  --algorithm-specification '{"TrainingInputMode": "File","TrainingImage": "123456789123.dkr.ecr.us-east-1.amazonaws.com/algo-image:tag", "ContainerArguments": [" "]}' \
  --input-data-config '[{"ChannelName":"training","DataSource":{"S3DataSource":{"S3DataType":"S3Prefix","S3Uri":"s3://bucketname/input","S3DataDistributionType":"ShardedByS3Key"}}}]' \
  --output-data-config '{"S3OutputPath": "s3://bucketname/output"}' \
  --resource-config '{"VolumeSizeInGB":10,"InstanceCount":4,"InstanceType":"ml.p5.48xlarge", "TrainingJobArn" : "arn:aws:sagemaker:us-east-1:123456789123:training-job/plan-name"}' \
  --stopping-condition '{"MaxRuntimeInSeconds": 1800}' \
  --region us-east-1 

After creating the training job, you can verify that it was properly assigned to the training plan by calling the DescribeTrainingJob API.

aws sagemaker describe-training-job --training-job-name training-job-name

Run a training job on a plan using the SageMaker AI Python SDK

Alternatively, you can create a training job associated with a training plan using the SageMaker Python SDK.

If you are using the SageMaker Python SDK from JupyterLab in Studio to create a training job, ensure that the execution role used by the space running your JupyterLab application has the required permissions to use SageMaker training plans. To learn about the required permissions to use SageMaker training plans, see IAM for SageMaker training plans.

The following example demonstrates how to create a SageMaker training job and associate it with a provided training plan using the training_plan attribute in the Estimator object when using the SageMaker Python SDK.

For more information on the SageMaker Estimator, see Use a SageMaker estimator to run a training job.

import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.estimator import Estimator
from sagemaker.inputs import TrainingInput

# Set up the session and SageMaker client
session = boto3.Session()
region = session.region_name
sagemaker_session = session.client('sagemaker')

# Get the execution role for the training job
role = get_execution_role()

# Define the input data configuration
trainingInput = TrainingInput(
    s3_data='s3://input-path',
    distribution='ShardedByS3Key',
    s3_data_type='S3Prefix'
)

estimator = Estimator(
    entry_point='train.py',
    image_uri="123456789123.dkr.ecr.{}.amazonaws.com/image:tag",
    role=role,
    instance_count=4,
    instance_type='ml.p5.48xlarge',
    training_plan="training-plan-arn",
    volume_size=20,
    max_run=3600,
    sagemaker_session=sagemaker_session,
    output_path="s3://output-path"
)

# Create the training job
estimator.fit(inputs=trainingInput, job_name=job_name)

After creating the training job, you can verify that it was properly assigned to the training plan by calling the DescribeTrainingJob API.

# Check job details
sagemaker_session.describe_training_job(TrainingJobName=job_name)