Nvidia recently announced a new, open-source PyTorch extension that helps users improve the performance of deep learning training on Nvidia’s Volta GPUs. The key improvement that APEX brings to deep learning is that it enables engineers to use mixed precision arithmetic to improve training speed while still maintaining accuracy and stability of training algorithms. The extension requires PyTorch 0.4, Python 3, and Nvidia’s CUDA 9 library. Additional information is available on the Nvidia site.
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About David Calloway
Hi! I'm David Calloway, the author of this blog on deep learning and artificial intelligence. I first started working with neural networks in the mid-80's, before the "dark winter" of neural networking technologies. I graduated from the U.S. Air Force Academy in 1979 with B.S. degrees in Physics and Electrical Engineering. In 1982, I received an MS degree in Electrical Engineering from Purdue University where I worked on early attempts at speech recognition. In 2005, I obtained another M.S. degree, this time in Biology from the University of Central Florida.
My interest in neural networks and deep learning was rekindled recently, when I got involved in a project at Nova Technologies where I am using deep learning and TensorFlow to recognize and classify objects from satellite imagery.
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