[SF]Real-Time, Streaming, Autonomous, IOT, Object Detection, TensorFlow, AI, GPU
Details
Talk 0: Meetup Announcements and Tech Updates (by Chris Fregly, Founder & Research Engineer @ PipelineAI)
- We hit 9,000 members!!!
https://secure.meetupstatic.com/photos/event/d/0/f/2/event_466553490.jpeg
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PipelineAI NIPS & LA Meetup Talks: https://www.slideshare.net/cfregly/pipelineai-aws-sagemaker-distributed-tensorflow-ai-model-training-and-serving-december-2017-nips-conference-la-big-data-and-python-meetups-83895713
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TensorFlow Announces Eager Execution (like PyTorch): https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html
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TensorFlow Dataset API Emerges: https://www.tensorflow.org/programmers_guide/datasets
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AWS SageMaker and RecordIO
Talk 1: Running Distributed TensorFlow with GPUs on Mesos with DC/OS (by Kevin Klues, Engineering Manager @ Mesosphere)
Running distributed TensorFlow is challenging, especially if you want to train large models on your own infrastructure. In this talk, I will present an open source TensorFlow framework for distributed training on DC/OS. This framework takes the pain out of deploying distributed TensorFlow, so you can spend less time worrying about your deployment strategy and more time building out your model. I will begin with a quick introduction to distributed TensorFlow on DC/OS, followed by a live demo.
Speaker Bio:
Kevin Klues is an Engineering Manager at Mesosphere where he leads the DC/OS Cluster Operations team. Prior to joining Mesosphere, Kevin worked at Google on an experimental operating system for data centers called Akaros. He and a few others founded the Akaros project while working on their Ph.Ds at UC Berkeley. In a past life, Kevin was a lead developer of the TinyOS project, working at Stanford University, the Technical University of Berlin, and the CSIRO in Australia. When not working, you can usually find Kevin on a snowboard or up in the mountains in some capacity or another.
Talk 2: Using the TensorFlow Estimator and Experiment APIs for End-to-End, "Train-to-Serve" Model Training, Optimizing, and Serving GPU-based TensorFlow AI Models from Research to Production using PipelineAI (by Chris Fregly, Founder and Research Engineer @ PipelineAI)
(More details to come...)
Speaker 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.
Talk 3: Using TensorFlow for Deep Learning on Autonomous Vehicles at Kiwi Campus (Christian Garcia, Deep Learning Engineer @ Kiwi)
Summary:
In this talk we will learn about our goals, challenges, and general approach taken at Kiwi Campus to create a fleet of autonomous delivery robots. We will explore some of the basic models and architectures used for solving various aspects of this problem using Deep Learning.
Speaker Bio:
Expert Data Scientist and Developer with background in math and physics. Extremely passionate about programming, deep learning, and deep reinforcement learning.
Talk 4: Kubernetes + GPUs + Distributed TensorFlow + Streaming Data (Dong Meng, Data Scientist and Engineer @ MapR)
Based on this blog post: https://mengdong.github.io/2017/07/15/distributed-tensorflow-with-gpu-on-kubernetes-and-mapr/
Speaker Bio:
Dong is a Data Scientist - and Systems Engineer - with MapR Technologies. He specializes in Kubernetes, GPUs, TensorFlow, and Streaming Data.
