Deep learning is useful for enterprises tasks such as speech recognition, image classification, AI chatbots, and machine translation, just to name a few. 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, such as first-class GPU support, container-DNS support, scheduling improvements, and more. These improvements make running distributed deep learning and machine learning applications on YARN as simple as running them locally, which allows machine learning engineers to focus on algorithms instead of worrying about the underlying infrastructure. Wangda then demonstrates how to run these applications on YARN.
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 areas of interest 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.
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