Choosing Between Amazon SageMaker Training Jobs and Amazon SageMaker HyperPod: A Quick Decision-Making Guide for ML Workloads

Introduction

As machine learning workloads grow in complexity and scale, choosing the right training infrastructure becomes crucial. In this topic, we'll quickly explore the key differences between Amazon SageMaker training jobs and Amazon SageMaker HyperPod.

Amazon SageMaker Training Jobs

Amazon SageMaker training jobs are managed, on-demand ML training tasks that provide a serverless experience for training machine learning models on Amazon SageMaker. They provide a straightforward way to train ML models.

Common Use Cases for Standard Amazon SageMaker Training Jobs:

• Training supervised learning models (classification/regression) for tasks like customer churn prediction, fraud detection, and price forecasting.
• Training deep learning models for computer vision, NLP, and recommendation systems that fit in single-instance memory and don't require persistent infrastructure.

Here's a typical high level implementation:

estimator = PyTorch(
    entry_point='train.py',
    role=role,
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    framework_version='1.8.0',
    hyperparameters={
        'epochs': 10,
        'batch-size': 64
    }
)
estimator.fit()

Key Benefits:

• Simple setup and execution
• Pay-per-use pricing model
• Ideal for periodic training needs
• Lower operational overhead

Amazon SageMaker HyperPod

Amazon SageMaker HyperPod helps you provision resilient clusters for running machine learning (ML) workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs). It accelerates development of FMs by removing undifferentiated heavy-lifting involved in building and maintaining large-scale compute clusters powered by thousands of accelerators such as AWS Trainium instance family.

HyperPod offers a persistent cluster approach for ML training:

{
           "InstanceGroupName": "worker-group-1",
           "InstanceType": "ml.g5.12xlarge",
           "InstanceCount": 2,
           "InstanceStorageConfigs": [
               {
                   "EbsVolumeConfig": {
                       "VolumeSizeInGB": 500
                   }
               }
           ],
           "LifeCycleConfig": {
               "SourceS3Uri": "s3://$Lifecycle_Bucket/src",
               "OnCreate": "on_create.sh"
           },
           "ExecutionRole": "$Sagemaker_Execution_Role_ARN",
           "ThreadsPerCore": 1
       }

Common Use Cases for Amazon SageMaker HyperPod

• Training and fine-tuning Large Language Models (LLMs) and foundation models that require significant computational resources
• Production-scale distributed training for enterprise-level deep learning workloads requiring persistent infrastructure
• Long-running research and experimentation projects with complex hyperparameter optimization needs and continuous model improvements

Key Benefits:

• Persistent cluster infrastructure
• Optimized for continuous workloads
• Workload orchestration using SLURM or Amazon EKS
• Advanced resource management
• Better cost efficiency at scale

Comparison Table

Feature	SageMaker Training Jobs	SageMaker HyperPod
Infrastructure Type	Ephemeral (Serverless)	Persistent Clusters
Best For	Periodic training, smaller models	Large models, continuous training
Cost Model	Pay-per-use	Reserved capacity or On-demand pricing
Setup Time	Minutes	Hours (but persists)
Checkpointing	Basic	Advanced with auto-recovery
Scale	Single to few instances	Up to hundreds of instances
Use Cases	Traditional ML, small-medium DL	LLMs, Foundation Models
Resource Management	Automatic provisioning/cleanup	Managed persistent clusters

Making the Right Choice

Choose Standard Training Jobs when:

• Running periodic training workloads
• Need pay-per-use pricing
• Operating with smaller teams
• Requiring simple setup
• Working with limited budgets
• Performing development and testing

Choose HyperPod when:

• Training large language models
• Need persistent infrastructure
• Running continuous training workloads
• Require distributed training
• Working with foundation models
• Need advanced checkpointing

Cost Considerations

Standard Training Jobs

• Pay only for actual training time
• No minimum commitment
• Higher per-hour rates
• Includes infrastructure management

HyperPod

• Reserved capacity or On-Demand pricing
• Additional storage costs for persistence

Conclusion

Choosing between Amazon SageMaker Training Jobs and HyperPod depends primarily on your specific ML training and operational needs. Standard Training Jobs are ideal for smaller models and periodic training with their serverless, pay-as-you-go approach. HyperPod, on the other hand, excels at training large-scale models like LLMs with its persistent infrastructure and advanced features.

Consider these key factors when deciding:

• Model size and complexity
• Training frequency and duration
• Budget constraints
• Team requirements
• Infrastructure persistence needs

By carefully evaluating these aspects, you can select the most cost-effective and efficient training infrastructure for your machine learning workflows.

Article co-authors: Sashank Bulusu