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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. | |
| 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. | |
| SageMaker's distributed data parallel library addresses communications overhead in two ways: | |
| 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. | |
| 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. | |