ROCm and Hopsworks for end-to-end deep learning pipelines
Who is this presentation for?
- Data scientists, data architects, data engineers, and CTOs
The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning.
Jim Dowling outlines how Hopsworks, an open source platform for machine learning infrastructure, enables the training and operation of deep learning models on ROCm in horizontally scalable end-to-end machine learning pipelines. He presents performance benchmarks for ROCm on new GPU hardware (AMD MI50, MI60 GPUs) and shows you how Hopsworks can enable distributed deep learning with both ROCm and Cuda on both TensorFlow and PyTorch. You’ll see a live demonstration of training and inference for an end-to-end machine learning pipeline written in a number of Jupyter notebooks orchestrated by Airflow.
Prerequisite knowledgeA basic understanding of Python and either TensorFlow or PyTorch programs
What you'll learn
- Learn that ROCm is a viable, high-performance platform for deep learning and Hopsworks enables the development and operation of horizontally scalable deep learning pipelines using ROCm
Jim Dowling is the CEO of Logical Clocks, an associate professor at KTH Royal Institute of Technology in Stockholm, and lead architect of Hopsworks, an open source data and AI platform. He’s a regular speaker at big data industry conferences. He holds a PhD in distributed systems from Trinity College Dublin.
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