This talk will cover how to accelerate deep learning with GPUs. GPUs have an architecture that is well-adapted to speeding up the massive parallel array calculations at the heart of deep learning. Today, manufacturers like NVIDIA are releasing GPUs with deep learning-specific features to further speed up model training and improve the throughput of deployed models. Installing and deploying GPU accelerated code can be challenging, so Anaconda has curated popular deep learning frameworks and packed them with GPU acceleration in the Anaconda Distribution. There they can be combined with Python packages like Pandas, Dask, and Jupyter to power data science experiments and production deployments.
Required audience experience
Attendees should be proficient in Python and have a general understanding of deep learning deployment requirements
Objective of the talk
This session will cover how to install and deploy GPU accelerated code using Anaconda. Attendees will learn how to combine the most popular deep learning frameworks with Python packages such as Pandas, Dask, and Jupyter, to power data science experiments and production deployments.