Preprocessing - Deep Learning AMI
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Preprocessing

Data preprocessing through transformations or augmentations can often be a CPU-bound process, and this can be the bottleneck in your overall pipeline. Frameworks have built-in operators for image processing, but DALI (Data Augmentation Library) demonstrates improved performance over frameworks’ built-in options.

  • NVIDIA Data Augmentation Library (DALI): DALI offloads data augmentation to the GPU. It is not preinstalled on the DLAMI, but you can access it by installing it or loading a supported framework container on your DLAMI or other Amazon Elastic Compute Cloud instance. Refer to the DALI project page on the NVIDIA website for details. For an example use-case and to download code samples, see the SageMaker Preprocessing Training Performance sample.

  • nvJPEG: a GPU-accelerated JPEG decoder library for C programmers. It supports decoding single images or batches as well as subsequent transformation operations that are common in deep learning. nvJPEG comes built-in with DALI, or you can download from the NVIDIA website's nvjpeg page and use it separately.

You might be interested in these other topics on GPU monitoring and optimization: