Using AWS SageMaker, Kubernetes, and PipelineAI for High Performance, Hybrid-Cloud Distributed TensorFlow Model Training and Serving with GPUs
Using AWS SageMaker, Kubernetes, and PipelineAI for High Performance, Hybrid-Cloud Distributed TensorFlow Model Training and Serving with GPUs

Abstract: 

In this talk, I will demonstrate how to train, optimize, and serve distributed machine learning models across various environments including the following:

1) Local Laptop
2) Kubernetes Cluster (Running Anywhere)
3) AWS's New SageMaker Service (Announced Last Week @ Re-invent)

I'll also present some post-training model-optimization techniques to improve model serving performance for TensorFlow running on GPUs. These techniques include 16-bit model training, neural network layer fusing, and 8-bit weight quantization.

Lastly, I'll discuss alternate runtimes for TensorFlow on GPUs including and TensorFlow Lite and Nvidia's TensorRT.

Bio: 

Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, ""High Performance TensorFlow in Production.""

Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.

Open Data Science

 

 

 

Open Data Science
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Cambridge, MA 02142
info@odsc.com

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