Today, a lot of customers are using TensorFlow to train deep learning models for their clickthrough rate in advertising and personalization recommendations in ecommerce. As the behavior of their clients change, they can accumulate large amounts of new data every day. Model iteration is one of a data scientist’s daily jobs, but they face the problem of taking too long to train on large datasets.
Amazon SageMaker is a fully managed machine learning (ML) platform that could help data scientists focus on models instead of infrastructure, with native support for bring-your-own-algorithms and frameworks such as TensorFlow and PyTorch. SageMaker offers flexible distributed training options that adjust to your specific workflows. Because many data scientists may lack experience in the acceleration training process, in this post we show you the factors that matter for fast deep learning model training and the best practices of acceleration training for TensorFlow 1.x on SageMaker.

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