Deep Dive into Google TPU, TFRecord, Dataset API, Kafka, Math Behind Neural Nets
Details
PLEASE RSVP HERE TO MAKE SURE YOU GET A SPOT:
https://www.eventbrite.com/e/deep-dive-into-google-tpu-tfrecord-dataset-api-kafka-math-behind-neural-nets-tickets-44304797843
THIS EVENT WILL BE RECORDED AND POSTED TO THE FOLLOWING:
- https://pipeline.ai
- https://youtube.pipeline.ai
- https://slideshare.pipeline.ai
- https://quickstart.pipeline.ai
- http://community.pipeline.ai
Agenda
Talk 0: Meetup Updates and Announcements (by Chris Fregly, Founder @ PipelineAI)
Talk 1: Comparing Spark and TensorFlow Model Training and Serving Pipelines including the Estimator API, Dataset API, TFRecord File Format, GPUs, and TPUs (by Chris Fregly, Founder @ PipelineAI)
https://www.linkedin.com/in/cfregly/
Related Links
- https://docs.google.com/presentation/d/16kHNtQslt-yuJ3w8GIx-eEH6t_AvFeQOchqGRFpAD7U/
- https://developers.googleblog.com/2017/12/creating-custom-estimators-in-tensorflow.html
- Latest (Official) MNIST using customer estimator:
https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py
Talk 2: Deep Dive into Google's TPUs including their new Pipeline Profiling Tools within TensorBoard (by Romit Singhai, Applied AI Engineer @ GE)
https://www.linkedin.com/in/romitsinghai/
Romit will share his experience working with Google's new TPUs on various datasets, training algorithms, and device-placement strategies.
He will highlight the differences between the standard CPU/GPU Estimator API - and the new TPU Estimator API. These are important differences, so pay close attention!
In addition, Romit and I discovered some new TensorBoard profiling features that analyze your ENTIRE TensorFlow pipeline including data ingestion and ETL to CPU, GPU, and TPU utilization and graph/operator optimization.
Note: These profiling tools are exactly what we've always from Spark-based ETL pipelines, but we've never seen them on the market - not at this level of system detail and optimization.
Lastly, Romit will perform a live demo of TPU training, profiling, and optimizing - complete with source code and runtime configuration.
Talk 3: The Math Behind Neural Networks by Francesco Mosconi, PhD (https://www.linkedin.com/in/framosconis/)
Francesco is Founder and Data Scientist @ CATALIT Data Science - a Deep Learning and Advanced Analytics Consultancy/Training Company based in San Francisco.
Francesco is also Founder of the popular Data Weekends: https://www.dataweekends.com/
Related Links
- https://pipeline.ai
- https://youtube.pipeline.ai
- https://slideshare.pipeline.ai
- https://quickstart.pipeline.ai
- http://community.pipeline.ai
- https://www.neuraldesigner.com/blog/5_algorithms_to_train_a_neural_network
- http://algorithms-tour.stitchfix.com/
PLEASE RSVP HERE TO MAKE SURE YOU GET A SPOT:
https://www.eventbrite.com/e/deep-dive-into-google-tpu-tfrecord-dataset-api-kafka-math-behind-neural-nets-tickets-44304797843