Which solution will meet these requirements?
Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.
Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.
Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.
Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.
Explanations:
AWS Batch with EC2 Spot Instances can reduce costs, but it doesn’t provide built-in model checkpointing. Without checkpointing, the model might need to be retrained if an interruption occurs, resulting in potential loss of progress.
While EC2 Spot Instances with a Spot Instance interruption notice could help save progress, manually handling the snapshot to Amazon S3 adds operational complexity. It does not provide an automated solution like checkpointing.
AWS Lambda is not suitable for training large models due to its resource limits and lack of long-running job support. It also does not provide built-in mechanisms for checkpointing or efficient model saving for large-scale training.
Managed spot training in Amazon SageMaker provides a cost-effective solution with built-in checkpointing. This feature ensures that if training is interrupted, progress is saved, minimizing the risk of losing work or needing to retrain.
From what I’ve seen, the answer is:
Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.
I map out that the answer is:
Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.