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| The SageMaker Distributed Data Parallel Library Overview |
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| SageMaker's distributed data parallel library extends SageMaker’s training |
| capabilities on deep learning models with near-linear scaling efficiency, |
| achieving fast time-to-train with minimal code changes. |
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| When training a model on a large amount of data, machine learning practitioners |
| will often turn to distributed training to reduce the time to train. |
| In some cases, where time is of the essence, |
| the business requirement is to finish training as quickly as possible or at |
| least within a constrained time period. |
| Then, distributed training is scaled to use a cluster of multiple nodes, |
| meaning not just multiple GPUs in a computing instance, but multiple instances |
| with multiple GPUs. However, as the cluster size increases, it is possible to see a significant drop |
| in performance due to communications overhead between nodes in a cluster. |
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| SageMaker's distributed data parallel library addresses communications overhead in two ways: |
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| 1. The library performs AllReduce, a key operation during distributed training that is responsible for a |
| large portion of communication overhead. |
| 2. The library performs optimized node-to-node communication by fully utilizing AWS’s network |
| infrastructure and Amazon EC2 instance topology. |
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| To learn more about the core features of this library, see |
| `Introduction to SageMaker's Distributed Data Parallel Library |
| <https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-intro.html>`_ |
| in the SageMaker Developer Guide. |
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