Running a SageMaker training job - Amazon SageMaker AI
Use your recipe as a SageMaker training job
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Running a SageMaker training job

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

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.

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

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

  4. 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.

  5. additional_estimator_kwargs: Additional estimator arguments for submitting a training job to the SageMaker training job platform. For more information, see Algorithm Estimator.