Memory consumption of neural networks depends on many factors. Why do we see so many questions about memory consumption on data science forums? Why are the most commonly used datasets and neural networks focused on images with dimensions hovering around the 224 x 224 image size? You would argue that it could easily fit more than 32 uncompressed (8 GB) 8K images, let alone 32 8K jpegs (896 MB). The memory capacity of a modern data center GPU ranges from 16 GB to 80 GB. An uncompressed 8K image (7680 x 4320) consumes 265 MB. Part 3 of this series covered batch sizes and mentioned a batch size of 32, which seems a small number nowadays. You can expect an application on your platform that incorporates such functionality. Many organizations are looking for ways to increase revenue or decrease costs by applying image classification, object identification, edge perception, or pattern discovery to their business processes. Plenty of content discusses using image classification to distinguish cats from dogs in a picture, but let’s look beyond the scope of pet projects. Or, to end this paragraph with a happy note, find all the pictures of your dog on your phone. It’s used to assist with your medical diagnosis. For example, we use it to unlock our phones using facial recognition or exit parking structures smoothly using license plate recognition. Most of us use a form of computer vision daily. What exactly happens when an input is presented to a neural network, and why do data scientists mainly struggle with out-of-memory errors? Besides Natural Language Processing (NLP), computer vision is one of the most popular applications of deep learning networks. This article dives deeper into the memory consumption of deep learning neural network architectures. He is an author of the vSphere host and clustering deep dive series, podcast host for the Unexplored Territory podcast, and you can follow him on Twitter Training vs Inference – Memory Consumption by Neural Networks Frankdenneman Follow Frank Denneman is a Chief Technologist at VMware, primarily focusing on Machine Learning technology.
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