# Create a training job using the API, AWS CLI, SageMaker SDK
<a name="use-training-plan-for-training-jobs-using-api-cli-sdk"></a>

To use SageMaker training plans for your SageMaker training job, specify the `TrainingPlanArn` parameter of the desired plan in the `ResourceConfig` when calling the [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API operation. You can use exactly one plan per job.

**Important**  
The `InstanceType` field set in the `ResourceConfig` section of the `CreateTrainingJob` request must match the`InstanceType` of your training plan.

## Run a training job on a plan using the CLI
<a name="training-job-cli"></a>

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](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) command, see [https://docs.aws.amazon.com/cli/latest/reference/sagemaker/create-training-job.html](https://docs.aws.amazon.com/cli/latest/reference/sagemaker/create-training-job.html).

```
# 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::111122223333:role/DataAndAPIAccessRole \
  --algorithm-specification '{"TrainingInputMode": "File","TrainingImage": "111122223333.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", "TrainingPlanArn" : "arn:aws:sagemaker:us-east-1:111122223333:training-plan/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
<a name="training-job-sdk"></a>

Alternatively, you can create a training job associated with a training plan using the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/v2.html).

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](training-plan-iam-permissions.md).

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](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers-adapt-your-own-private-registry-estimator.html).

```
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)
```