5.5.2. Managed Spot Training and Spot Instances

First Principle: Managed Spot Training and direct EC2 Spot Instances fundamentally enable significant cost savings for fault-tolerant ML workloads by leveraging unused AWS compute capacity at a reduced price.

Leveraging EC2 Spot Instances is one of the most effective ways to reduce the cost of machine learning compute on AWS, especially for non-critical or fault-tolerant workloads.

Key Concepts:

• EC2 Spot Instances:
  • What they are: Unused EC2 capacity that AWS offers at a significant discount (up to 90% compared to On-Demand prices).
  • Characteristic: They can be interrupted by AWS with a two-minute warning if AWS needs the capacity back.
  • Use Cases for ML:
    • Self-managed Training: Running custom training jobs on EC2 instances directly.
    • Amazon EMR: Using Spot Instances for worker nodes in EMR clusters for big data processing.
    • Batch Inference: For custom batch inference solutions on EC2.
  • Requirements: Your application/workflow must be fault-tolerant and able to handle interruptions (e.g., by saving progress and resuming).
• Amazon SageMaker Managed Spot Training:
  • What it is: A feature within SageMaker Training Jobs that automates the use of Spot Instances for ML training.
  • Benefits:
    • Significant Cost Savings: Up to 90% reduction in training costs.
    • Automated Checkpointing: SageMaker automatically saves model checkpoints to Amazon S3 at regular intervals (or when an interruption warning is received).
    • Automated Resumption: If a Spot Instance is interrupted, SageMaker can automatically resume the training job from the last saved checkpoint on a new Spot Instance (or On-Demand if Spot capacity is unavailable).
    • Managed Lifecycle: SageMaker handles the provisioning, monitoring, and termination of Spot Instances.
  • Requirements: Your training script must be designed to save and load checkpoints.
  • Ideal Use Cases:
    • Hyperparameter Tuning: Running many trials where individual interruptions are acceptable.
    • Large-scale Experiments: Non-critical training runs that can tolerate restarts.
    • Batch Training: Training models where the completion time is flexible.
• Spot Fleets/Auto Scaling Groups with Spot Instances: For more complex, self-managed ML infrastructure (e.g., custom inference clusters on EC2/ECS/EKS), you can use Spot Fleets or Auto Scaling Groups configured to use Spot Instances to maintain target capacity while optimizing costs.

Considerations:

• Interruption Rate: Spot Instance availability and interruption rates vary by instance type, Availability Zone, and demand.
• Fault Tolerance: Crucial for any workload using Spot Instances.
• Cost vs. Time: While Spot Instances are cheaper, they might take longer to complete if interruptions are frequent.

Scenario: Your data science team frequently runs large-scale deep learning training experiments that are not time-critical. The current compute costs are very high. You need to reduce these costs significantly, even if it means that some training jobs might take longer or need to restart.

Reflection Question: How do Managed Spot Training (for SageMaker training jobs with automated checkpointing) and direct EC2 Spot Instances (for self-managed clusters) fundamentally enable significant cost savings for fault-tolerant ML workloads by leveraging unused AWS compute capacity at a reduced price, making them ideal for experiments and non-critical tasks?

💡 Tip: Always evaluate if your ML workload can tolerate interruptions. If it can, Spot Instances (especially Managed Spot Training in SageMaker) should be your go-to for cost savings.