Guidebook – Deep learning on AWS

To understand the state of deep learning adoption and usage today, as well as how it has changed since last year’s report (Nucleus Research s180 – Guidebook: TensorFlow on AWS –November 2018), Nucleus conducted interviews and analyzed the experiences across 316 unique projects. For the second time in two years, the number of deep learning projects in production more than doubled. We found that 96 percent of deep learning is running in a cloud environment, with TensorFlow being the most popular framework, being used in 74 percent of deep learning projects. PyTorch was also used in 43 percent of projects (please note that most projects leverage multiple frameworks; MXNet, Keras, and Caffe2 also appeared, virtually always in conjunction with TensorFlow, PyTorch, or both), a significant increase in adoption from last year. Of the 316 total projects, only 9 percent were built with a singular framework. Most notably, of the cloud-hosted deep learning projects, 89 percent are running on AWS; a key driver of this is the breadth of framework choices on AWS along with its own continued investment in existing and new services.