Name: [Palo Alto] TensorFlow +Spark +Neural Nets +Deep Learning +Nvidia CUDA +OpenDeep
Start: 2016-04-07T17:30:00-07:00
End: 2016-04-07T20:00:00-07:00
Location: Insight Data Science

*** Note the Earlier Start Time: 5:30pm ***

This event will be recorded for off-line viewing.

Parking and Caltrain Details

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There is a free parking structure with plenty of parking available across the street at 261 Sheridan Ave (https://www.google.com/maps/place/261+Sheridan+Ave,+Palo+Alto,+CA+94306/data=!4m2!3m1!1s0x808fbaf01defdecf:0x65a6ca10f23a68ba?sa=X&ved=0ahUKEwjc-OePtfrLAhUS5GMKHXILDEQQ8gEIHDAA). There is also overflow parking lots two blocks away at the intersection of Birch St. and Sherman Ave (https://www.google.com/maps/place/2498+Birch+St,+Palo+Alto,+CA+94306/@37.4268638,-122.1434993,19z/data=!3m1!4b1!4m2!3m1!1s0x808fbae576f28143:0x90816dc74c26e245).

For those taking the CalTrain, we are a 5-minute walk from the California Ave stop.

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Agenda

5:30pm: Arrive and Mingle

6:00pm: Introductions and Exciting Announcements

6:30pm: Talk 1

Modularity in Neural Nets and Resulting Design Choices in Open Deep (http://www.opendeep.org/)
Markus Beissinger (https://www.linkedin.com/in/mbeissinger), Founder of Vitruvian Science Labs (http://vitruvianscience.com/)

7:00pm: Talk 2

DeepLearning4J (http://deeplearning4j.org/): CUDA-based Deep Learning Java Library for Spark
Adam Gibson (https://www.linkedin.com/in/agibsonccc), Founder of Skymind (http://skymind.io)

7:30pm: Talk 3

Spark Project Tungsten + GPUs (http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark): Exploiting GPUs in Spark

Kazuaki Ishizaki (https://www.linkedin.com/in/kazuaki-ishizaki-63744113), Research Engineer, IBM Research - Tokyo

8:00pm: Talk 4

Nvidia CUDA (https://developer.nvidia.com/cuda-zone) + GPUs + Spark: Extending Spark Operators for Distributed Spark Matrix Multiplication in new, Row-grouped CSR Format (http://arxiv.org/pdf/1012.2270.pdf) for sparse matrices

Maxim Naumov (https://www.linkedin.com/in/maxim-naumov-86133250), Sr. Research Scientist @ Nvidia (http://nvidia.com/)

8:30pm: Talk 5

TensorFlow (https://www.tensorflow.org/) + Scikit-learn (http://scikit-learn.org/stable/) + TensorFlow Serving (https://www.tensorflow.org/versions/master/tutorials/tfserve/index.html) + GPUs
Chris Fregly (http://linkedin.com/in/cfregly), Principal Data Solutions Engineer, IBM Spark Tech Center (http://spark.tc)

9:00pm: Go Home and Spend Time With Your Family!

Related Links

http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark

https://developer.nvidia.com/cuda-zone

http://www.opendeep.org/

https://databricks.com/blog/2016/01/25/deep-learning-with-spark-and-tensorflow.html (https://github.com/amplab/SparkNet/)

https://github.com/amplab/SparkNet

http://arxiv.org/pdf/1511.06051v3.pdf

http://deeplearning4j.org/

http://ampcamp.berkeley.edu/5/exercises/image-classification-with-pipelines.html

https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala

https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala

http://www.datascienceassn.org/content/spark-gets-gpu-lab

http://www.datascienceassn.org/content/single-gpu-powered-node-4x-faster-50-node-spark-cluster

https://issues.apache.org/jira/browse/SPARK-3785

https://issues.apache.org/jira/browse/SPARK-3434

https://groups.google.com/forum/#!topic/scalanlp-discuss/ZZJbXtwL19c

https://github.com/BIDData

David Hall: https://www.linkedin.com/profile/view?id=ADEAAABq7i4B_GWYFbBSB6KIL2DEMYCGfiHUCOo

Tungsten + GPUs + UnsafeColumn: http://www.slideshare.net/SparkSummit/exploiting-gpus-for-columnar-datafrrames-by-kiran-lonikar

Wikipedia (for laughs):

https://en.wikipedia.org/wiki/Deep_learning

Chris Fregly

Adam Gibson

David E Drummond

Kazuaki Ishizaki

AI Performance Engineering Meetup (San Francisco, Global)

Technology

Predictive Analytics

High Scalability Computing

Artificial Intelligence Programming

Artificial Intelligence Applications

Apache Spark

Data Science

Machine Learning

TensorFlow

Big Data

Deep Learning

Neural Networks

Artificial Intelligence

Natural Language Processing

Kubernetes

PyTorch