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Overview
Title: State of the Art Deep Learning on Apache Spark™
Date: 10/31/2018
Time: 9:00 AM PDT
Duration: 60 minutes
summary
Big data and AI are joined at the hip: the best AI applications require massive amounts of constantly updated training data to build state-of-the-art models. Increasingly more Spark users want to integrate Spark with distributed machine learning frameworks built for state-of-the-art training.
Here's the problem: big data frameworks like Spark and distributed deep learning frameworks don’t play well together due to the disparity between how big data jobs are executed and how deep learning jobs are executed.
In this latest Data Science Central webinar, we'll share how Project Hydrogen, a Spark Project Improvement Proposal led by Databricks, is positioned as a potential solution to this dilemma.
We will cover:
- Barrier execution mode for distributed DL training
- Fast data exchange between Spark and DL frameworks, and
- Accelerator-awareness scheduling
Speaker: Xiangrui Meng, Software Engineer - Databricks
Hosted by: Bill Vorhies, Editorial Director - Data Science Central Registering for this DSC event constitutes express written consent that DSC, its sponsors and affiliates, may use the information provided to keep you informed about offers, products and services.
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State of the Art Deep Learning on Apache Spark™
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