# Running a SageMaker training job
<a name="cluster-specific-configurations-run-sagemaker-training-job"></a>

SageMaker HyperPod Recipes supports submitting a SageMaker training job. Before you submit the training job, you must update the cluster configuration, `sm_job.yaml`, and install corresponding environment.

## Use your recipe as a SageMaker training job
<a name="cluster-specific-configurations-cluster-config-sm-job-yaml"></a>

You can use your recipe as a SageMaker training job if you aren't hosting a cluster. You must modify the SageMaker training job configuration file, `sm_job.yaml`, to run your recipe.

```
sm_jobs_config:
  output_path: null 
  tensorboard_config:
    output_path: null 
    container_logs_path: null
  wait: True 
  inputs: 
    s3: 
      train: null
      val: null
    file_system:  
      directory_path: null
  additional_estimator_kwargs: 
    max_run: 1800
```

1. `output_path`: You can specify where you're saving your model to an Amazon S3 URL.

1. `tensorboard_config`: You can specify a TensorBoard related configuration such as the output path or TensorBoard logs path.

1. `wait`: You can specify whether you're waiting for the job to be completed when you submit your training job.

1. `inputs`: You can specify the paths for your training and validation data. The data source can be from a shared filesystem such as Amazon FSx or an Amazon S3 URL.

1. `additional_estimator_kwargs`: Additional estimator arguments for submitting a training job to the SageMaker training job platform. For more information, see [Algorithm Estimator](https://sagemaker.readthedocs.io/en/stable/api/training/algorithm.html).