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
Engineering Manager, Cloudera

@leftnoteasy

Wangda Tan is Product Management Committee (PMC) member of Apache Hadoop and engineering manager of computation platform team at Cloudera. He manages all efforts related to Kubernetes and YARN for both on-cloud and on-prem use cases of Cloudera. His primary interesting areas are YuniKorn scheduler (scheduling containers across YARN and Kubernetes) and Hadoop submarine project (running Deep learning workload across YARN and Kubernetes). He has also led features like resource scheduling, GPU isolation, node labeling, resource preemption etc efforts in the Hadoop YARN community. Before joining Cloudera, he was working at Pivotal, working on integration OpenMPI/GraphLab with Hadoop YARN. Before that, he was working at Alibaba cloud computing, participated in creating a large scale machine learning, matrix and statistics computation platform using Map-Reduce and MPI.

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.