Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during training makes possible an incredible wealth of new applications that utilize deep learning.

Language: English
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