Distributed and Managed Spot Training
Explore how to optimize machine learning training on AWS using SageMaker's distributed training strategies. Understand the differences between data and model parallelism, manage training costs with Managed Spot Training, and accelerate GPU performance with Training Compiler and Warm Pools. This lesson helps you select the right approach to scale large datasets and models effectively while balancing cost and resource efficiency.
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With data delivery and regularization strategies in place, the next bottleneck in the ML training pipeline emerges when datasets and models outgrow a single instance. A 500 GB image dataset or a billion-parameter transformer cannot train efficiently on one GPU without unacceptable wall-clock time or out-of-memory failures. For the AWS Certified Machine Learning Engineer – Associate exam, you must select the right scaling and cost-optimization strategy for a given scenario.
This lesson covers four SageMaker capabilities that address this challenge directly: distributed training through data and model parallelism, Managed Spot Training for cost reduction, SageMaker Training Compiler for GPU-level optimization, and Warm Pools to eliminate provisioning overhead. Each capability interacts with instance selection. Choosing an ml.p4d.24xlarge for multi-GPU distributed deep learning, for example, fundamentally changes which strategies apply. Candidates who can match the right technique to the right bottleneck will navigate exam scenarios with confidence.
Data parallelism vs. model parallelism
Distributed training splits the training workload across multiple GPUs or instances. The two primary strategies differ in what gets split: the data or the model itself.
How data parallelism works
In data parallelism, each GPU receives a different slice of the training batch. Every worker computes forward and backward passes independently, then gradients are aggregated across all workers using an