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Accelerating Apache MXNet Models on Apple Platforms Using Core ML - MCL311 - re:Invent 2017

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Accelerating Apache MXNet Models on Apple Platforms Using Core ML - MCL311 - re:Invent 2017

Running deep learning models on devices at the edge is one of the hottest trends in AI today. This workshop provides a tutorial on developing and training deep learning models with Apache MXNet and walks you through how to easily bring them into the Apple ecosystem of products. You will learn how to convert MXNet models easily and efficiently to formats that can be integrated into iOS/macOS applications. To participate in this workshop, attendees will require an Apple MacBook running the latest OS (10.13). An iPhone running iOS 11+ or higher to run Core ML and Apache MXNet is optional.

Running deep learning models on devices at the edge is one of the hottest trends in AI today. This workshop provides a tutorial on developing and training deep learning models with Apache MXNet and walks you through how to easily bring them into the Apple ecosystem of products. You will learn how to convert MXNet models easily and efficiently to formats that can be integrated into iOS/macOS applications. To participate in this workshop, attendees will require an Apple MacBook running the latest OS (10.13). An iPhone running iOS 11+ or higher to run Core ML and Apache MXNet is optional.

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Accelerating Apache MXNet Models on Apple Platforms Using Core ML - MCL311 - re:Invent 2017

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Accelerating Apache MXNet Models on Apple Platforms Using Core ML A m a z o n A I N o v e m b e r 2 9 , 2 0 1 7 M C L 3 1 1 AWS re:INVENT
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda • Prerequisite: Mac laptop, Xcode 9, (optional) iPhone • Motivation • What is MXNet • What is CoreML • Workshop—train MXNet model, convert to Core ML model, and test on iPhone
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Bay Beach Coast Outdoors Sea Water Palm_tree Plant Tree Summer Landscape Nature Hotel 99.18% 99.18% 99.18% 99.18% 99.18% 99.18% 99.21% 99.21% 99.21% 58.3% 51.84% 51.84% 51.24% Category Confidence Motivation: Object Detection
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Motivation: Face Comparison
  5. 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Motivation: Speech Recognition Market grew by > 20%. { { { { { ˈtwɛn.ti pɚ.ˈsɛnt ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ ˈðæn Market grew by more than twenty percent
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AI services AI platform AI engines Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon Machine Learning Amazon Elastic MapReduce Spark & SparkML More to come in 2017 Apache MXNet Caffe Theano KerasTorch CNTKTensorFlow P Instances Amazon ECS AWS Lambda AWS Greengrass FPGA Amazon EMR/Spark More to come in 2017 Hardware Amazon AI
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Apache MXNet Programmable Portable High performance Near-linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages Most open Best on AWS Optimized for deep learning on AWS Accepted into the Apache Incubator
  8. 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Strategy | Apache MXNet Integrate with AWS services Bring scalable deep learning to AWS services such as Amazon EMR, AWS Lambda, and Amazon ECS Foundation for AI services AmazonAI API Services, Internal AI Research, and Amazon Core AI Development Leverage the community Community brings velocity and innovation with no single project owner or controller
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Collaborations and Community Fourth DL framework in popularity (outpacing Torch, CNTK, and Theano) Diverse community (Spans industry and academia) 0 20,000 40,000 60,000 1 3 5 7 9 11 13 15 *As of 3/30/17 0 100 200 1 3 5 7 *As of 2/11/17
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning Framework Comparison Apache MXNet TensorFlow Cognitive Toolkit Industry Owner N/A – Apache Community Google Microsoft Programmability Imperative and Declarative Declarative only Declarative only Language Support R, Python, Scala, Julia, Cpp. Javascript, Go, Matlab and more… Python, Cpp. Experimental Go and Java Python, Cpp, Brainscript Code Length| AlexNet (Python) 44 sloc 107 sloc using TF.Slim 214 sloc Memory Footprint (LSTM) 2.6GB 7.2GB N/A *sloc – source lines of code
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 0 4 8 12 16 1 2 3 4 5 Ideal Inception v3 Resnet Alexnet 91% Efficiency Multi-GPU Scaling With MXNet
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Ideal Inception v3 Resnet Alexnet 88% Efficiency 0 64 128 192 256 1 2 3 4 5 6 7 8 9 Multi-Machine Scaling With MXNet
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Core ML Core ML is a new foundational machine learning framework used across Apple products, including Siri, Camera, and QuickType
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Core ML
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Conversion Workflow Xcode Your appModel source e.g. MXNet
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. XCode Model View
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Workshop Overview
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Workshop Overview SqueezeNet Model ImageNet data Predicted ImageNet label Fine tuning DeepDogNet Model Cat vs Dog data Convert Your App
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Mobile Hub integration Authenticate users Analyze user behavior Store and share media Synchronize data More …. Track retention Conversational bots Amazon LexAWS Mobile SDKs AWS Mobile Hub
  21. 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Device Farm AWS Device Farm remote access AWS Device Farm automated testing Physical devices Jenkins Plugin aws-device-farm
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. http://bit.ly/2ilp9bI
  23. 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank you! Please remember to complete your survey!

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