MLCOST04-BP07 Use distributed training
Accelerate your machine learning model training process by utilizing distributed computing resources, which can significantly reduce training time and optimize costs. Amazon SageMaker AI distributed training capabilities enable efficient processing of large models and datasets across multiple compute instances.
Desired outcome: You achieve faster training times for your machine learning models by distributing the workload across multiple instances. You optimize resource utilization and reduce overall training costs by using SageMaker AI's managed distributed training capabilities, which automatically handle infrastructure provisioning and termination when training completes. This approach allows you to train complex models that may be too large for a single machine or train standard models much faster through parallel processing.
Common anti-patterns:
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Training large models on a single instance even when they could benefit from distribution.
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Manually managing distributed training infrastructure rather than using managed services.
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Keeping training instances running after training is complete.
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Implementing custom distributed training code when built-in libraries would suffice.
Benefits of establishing this best practice:
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Significantly reduced training time for large models and datasets.
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Cost optimization through efficient resource utilization.
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Ability to train models that are too large to fit on a single GPU.
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Automatic infrastructure management with no need to maintain distributed training clusters.
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Enhanced team productivity by reducing waiting time for model results.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Distributed training allows you to split your machine learning workloads across multiple compute instances to accelerate the training process. This approach is particularly valuable when working with large models or datasets that would otherwise take too long to train on a single instance. Amazon SageMaker AI provides built-in support for distributed training through its specialized libraries that handle the complexity of distributing workloads efficiently.
When implementing distributed training, you need to consider the most appropriate approach based on your model architecture and data size. Data parallelism works by dividing your dataset across multiple GPUs, with each GPU having a complete copy of the model. This approach is ideal for scenarios where your model fits on a single GPU but training on the full dataset is time-consuming. Alternatively, model parallelism is designed for situations where your model is too large to fit on a single GPU. In this case, the model itself is partitioned across multiple GPUs.
SageMaker AI's distributed training libraries automatically handle the communication between nodes and optimize the distribution strategy, making it straightforward to scale your training workloads without managing the underlying infrastructure.
Implementation steps
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Evaluate your workload for distributed training suitability. Assess if your training job would benefit from distribution by considering factors like model size, dataset size, and current training times. Ideal candidates are models that take hours or days to train on a single instance or models too large to fit in a single GPU's memory.
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Choose the appropriate distributed training approach. Select between data parallelism and model parallelism based on your specific needs. Use data parallelism when your model fits on a single GPU but you want to process data faster. Use model parallelism when your model is too large to fit on a single GPU.
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Utilize Amazon SageMaker AI distributed training libraries. Implement distributed training using SageMaker AI's distributed training libraries
, which automatically handle the complexities of distributing workloads across multiple instances. These libraries provide optimized implementations for both data parallelism and model parallelism strategies. -
Configure your training cluster. Define the number and type of instances for your training cluster in your SageMaker AI training job configuration. Consider using GPU-optimized instance types like P3, P4d, or G4dn based on your model requirements and budget constraints.
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Adapt your training script for distributed processing. Modify your training code to work with SageMaker AI's distributed training libraries. For data parallelism, you'll need to use the SageMaker AI data parallelism library to distribute data across workers. For model parallelism, you'll integrate the SageMaker AI model parallelism library to partition your model across devices.
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Monitor and optimize training performance. Use Amazon SageMaker AI Debugger to monitor your distributed training jobs, identify bottlenecks, and optimize resource utilization. Analyze metrics like GPU utilization, communication overhead, and training throughput to fine-tune your distributed training configuration.
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Consider Amazon SageMaker AI HyperPod for persistent training clusters of foundation models. For workloads requiring long-running or repeated distributed training jobs, use Amazon SageMaker AI HyperPod to create persistent, managed clusters that can handle multiple training jobs efficiently while maintaining cost optimization through automatic scaling and resource management.
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Use SageMaker AI HyperPod for persistent training clusters. Use SageMaker AI HyperPod for workloads requiring long-running or repeated distributed training jobs, providing persistent, managed clusters with automatic scaling, checkpoint storage and recovery, and support for various instance types including P5e, G6, and Trn2.
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Use AI-powered code generation for distributed training implementation. Use AI-powered development tools like Amazon Q Developer
and Kiro to generate complex distributed training code, automate infrastructure setup scripts, and accelerate the implementation of distributed training workflows. -
Consider Amazon Bedrock for fine-tuning foundation models. For generative AI applications, consider using Amazon Bedrock for fine-tuning foundation models, model distillation, or continued pretraining, which provides optimized distributed training capabilities specifically designed for large language models.
Resources
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