Using Cognitive Toolkit (CNTK) and TensorFlow with Kubernetes clusters

Description

Deep learning has fueled the emergence of many practical applications and experiences and has played a central role in making many recent breakthroughs possible—from speech recognition that’s reached human parity in word recognition during conversations to neural networks that are accelerating the creation of highly precise land cover datasets to predicting vision impairment, regression rates, and eye diseases, among others.

The Microsoft Cognitive Toolkit (CNTK) is the behind-the-scenes magic that makes it possible to train deep neural networks that address a very diverse set of needs, such as in the scenarios above. CNTK lets anyone develop and train their deep learning model at massive scale. Successful deep learning projects also require a few critical infrastructure ingredients—specifically, an infrastructure that enables teams to perform rapid experimentation and scale elastically based on the deep learning training requirements.

Meanwhile, container technologies have been maturing (and more enterprises are using containers in their IT environments). Containers enable organizations to simplify the development and deployment of applications in various environments (e.g., on-premises, public cloud, hybrid cloud, etc.). Various container orchestration and management technologies are now available, including Docker Swarm, Kubernetes, and Mesosphere Marathon.

Kubernetes is an open source technology that makes it easier to automate deployment, scaling, and management of containerized applications. The ability to use GPUs with Kubernetes allows the clusters to facilitate running frequent experimentations, using it for high-performing serving and autoscaling of deep learning models and much more. Join Wee Hyong and Danielle Dean as they walk you through using Kubernetes clusters for deep learning.