Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Wangda Tan

Wangda Tan
Product Management Committee Member | Engineering Manager, Cloudera

@leftnoteasy

Wangda Tan is a product management committee (PMC) member of Apache Hadoop and engineering manager of the computation platform team at Cloudera. He manages all efforts related to Kubernetes and YARN for both on-cloud and on-premises use cases of Cloudera. His primary interesting areas are the YuniKorn scheduler (scheduling containers across YARN and Kubernetes) and the Hadoop submarine project (running a deep learning workload across YARN and Kubernetes). He’s also led features like resource scheduling, GPU isolation, node labeling, resource preemption, etc., efforts in the Hadoop YARN community. Previously, he worked on integration of OpenMPI and GraphLab with Hadoop YARN at Pivotal and participated in creating a large-scale machine learning, matrix, and statistics computation program using MapReduce and MPI and Alibaba.

Sessions

1:10pm–1:50pm Thursday, 09/13/2018
Location: 1A 10 Level: Intermediate
Secondary topics:  Data Platforms, Deep Learning, Model lifecycle management
Wangda Tan (Cloudera)
Average rating: ****.
(4.50, 2 ratings)
In order to train deep learning and machine learning models, you must leverage applications such as TensorFlow, MXNet, Caffe, and XGBoost. Wangda Tan discusses new features in Apache Hadoop 3.x to better support deep learning workloads and demonstrates how to run these applications on YARN. Read more.