Tech N' Talk Summary
In this Tech N' Talk, Gokila Dorai (FSU) and Eleanor Mehlenbacher (Red Hat) share their best practices for deploying TensorFlow on OpenShift and demo doing linear regression analysis using Jupyter Notebook as a jumping off point. This collaboration on TensorFlow started as part of the OpenShift Hackathon for Humanity that the Red Hat Consulting team hosted and mentored participants in the hands-on portion of the Open Source track at the 2017 Grace Hopper Celebration in Orlando, Florida.
TensorFlow is helping to accelerate machine learning research and make technology work better for everyone. There has been an amazing amount of activity around the project: More than 480 people have contributed directly to TensorFlow and we are pleased to have Gokila Dorai as our guest to talk about her work testing and deploying TensorFlow on OpenShift.
TensorFlow is an open source software library for numerical computation using data flow graphs. Its flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. Making TensorFlow available for workloads on OpenShift opens up a whole new world of resources to enable machine learning applications on OpenShift.
TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
About Our Guest Speakers:
Gokila Dorai, is currently a Research Assistant Research Assistant at e-Crime Investigative Technologies Lab at Florida State University focusing on leveraging Machine Learning technology on Mobile Forensics applications. She is currently pursuing PhD in Computer Science working on Mobile Forensics.
Eleanor Mehlenbacher, Middleware Consultant at Red Hat Consulting. She is a lead developer on various client-side and internal projects, with a focus on containerized application development and DevOps tools. She works with Red Hat OpenShift, CI/CD, Container, and Ansible teams.
- Slides from the presentation: TensorFlow and Openshift.pptx
Jupyter Notebook Image used in the demo with different custom configurations for Tensorflow
Radanalytics.io Developers Portal community focused on empowering insightful data driven application development on the OpenShift platform.
Tutorial on how to use source-to-image to build and deploy a Tensorflow prediction endpoint on Openshift.
Centos7 TensorFlow Image https://github.com/radanalyticsio/tf-base-notebook
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