MLCOST04-BP01 Select optimal computing instance size - Machine Learning Lens
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MLCOST04-BP01 Select optimal computing instance size

Right size your machine learning training instances according to the algorithm and workload requirements to maximize efficiency and reduce costs. By selecting the most appropriate computing resources, you can improve performance while minimizing unnecessary expenses.

Desired outcome: You can identify and select the optimal computing instance types for your machine learning workloads based on actual resource utilization metrics. You can systematically evaluate different instance options, understand their cost implications, and optimize your machine learning infrastructure spending while maintaining or improving performance.

Common anti-patterns:

  • Using oversized instances for training jobs regardless of model complexity.

  • Ignoring resource utilization metrics during training.

  • Failing to experiment with different instance types to find the optimal cost-performance balance.

  • Not considering the communication overhead in distributed training scenarios.

Benefits of establishing this best practice:

  • Reduced infrastructure costs for machine learning workloads.

  • Improved resource utilization and efficiency.

  • Better understanding of ML workload performance characteristics.

  • Optimization of price-performance ratio for different model types.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Machine learning training workloads vary significantly in their resource requirements based on model complexity, dataset size, and algorithm characteristics. Simple models might not train faster on larger instances because they cannot effectively utilize additional compute resources, and might even train slower due to high GPU communication overhead. By evaluating your workload's resource needs, you can identify the most cost-effective instance configuration.

The key to optimizing instance selection is understanding the actual resource utilization patterns of your machine learning workloads. Start with smaller instances and scale up only when necessary based on performance data. Amazon SageMaker AI provides tools like Debugger to monitor resource utilization and Experiments to compare training performance across different instance configurations. This data-driven approach assists you to avoid paying for unused resources while maintaining optimal training performance.

Implementation steps

  1. Understand your algorithm's resource requirements. Begin by analyzing whether your machine learning algorithm is compute-bound, memory-bound, or I/O-bound. Different algorithms have different scaling characteristics and resource needs. For deep learning workloads, consider whether GPU acceleration would provide significant benefits or if CPU instances would be more cost-effective for your specific model.

  2. Use Amazon SageMaker AI Experiments. Amazon EC2 provides a wide selection of instance types optimized to fit different use cases. Machine learning workloads can use either a CPU or a GPU instance. Select an instance type from the available EC2 instance types depending on the needs of your ML algorithm. Experiment with both CPU and GPU instances to learn which one gives you the best cost configuration. Amazon SageMaker AI lets you use a single instance or a distributed cluster of GPU instances. Use Amazon SageMaker AI Experiments to evaluate alternative options, and identify the size resulting in optimal outcome. With the pricing broken down by time and resources, you can optimize the cost of Amazon SageMaker AI and only pay for what is needed.

  3. Use Amazon SageMaker AI Debugger. Amazon SageMaker AI Debugger automatically monitors the utilization of system resources, such as GPUs, CPUs, network, and memory, and profiles your training jobs to collect detailed ML framework metrics. You can inspect resource metrics visually through SageMaker AI Studio and take corrective actions if the resource is under-utilized to optimize cost.

  4. Start small and scale gradually. Begin with smaller instance sizes for new models and monitor performance. Only increase instance size when you have data showing that your workload can benefit from additional resources. This approach assists you to avoid overprovisioning and unnecessary costs.

  5. Consider the communication overhead. For distributed training across multiple GPUs or instances, evaluate the communication overhead between nodes. In some cases, adding more compute resources might actually slow down training due to increased coordination requirements.

  6. Monitor and analyze training metrics. Track key metrics like CPU/GPU utilization, memory usage, I/O patterns, and training throughput across different instance types to identify bottlenecks and optimization opportunities.

  7. Use Spot Instances for cost savings. For non-critical training jobs, consider using Amazon EC2 Spot Instances through SageMaker AI to reduce costs by up to 90%. Configure your training jobs to checkpoint regularly to minimize the impact of potential interruptions.

  8. Use SageMaker AI Inference Recommender for optimal instance selection. Use SageMaker AI Inference Recommender with enhanced algorithms and support for multi-model endpoints to get sophisticated cost optimization recommendations for your specific workloads.

  9. For generative AI workloads, use foundation model optimization techniques. For generative AI workloads, consider techniques like quantization, distillation, and efficient fine-tuning methods to reduce the computational resources needed while maintaining model quality. Amazon SageMaker AI JumpStart provides optimized foundation models that can significantly reduce training time and resource requirements.

Resources

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