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Deep Learning at the Edge

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Julien SIMON
Julien SIMON

Talk @ Papis.io, London, 05/04/2018

Technologie
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© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon Deep Learning at the Edge
Deep Learning at the Edge 1. Flexible experimentation in the Cloud. 2. Scalable training in the Cloud. 3. Good prediction performance at the Edge. 4. Simple deployment of code and model
Flexible experimentation in the Cloud • Apache MXNet: Python, R, Perl, Matlab, Scala, C++. • Gluon • Imperative programming aka ‘define-by-run’. • Inspect, debug and modify models during training. • Extensive model zoo • Pre-trained computer vision models. • MobileNet, SqueezeNet for resource-constrained devices.
Scalable training in the Cloud Amazon SageMaker AWS Deep Learning AMI Amazon EC2 c5 p3
Good prediction performance at the Edge • MXNet is written in C++. • Gluon networks can be ‘hybridized’ for additional speed. • Two libraries boost performance on CPU-only devices • Fast implementation of math primitives • Hardware-specific instructions, e.g. Intel AVX or ARM NEON • Intel Math Kernel Library https://software.intel.com/en-us/mkl • NNPACK https://github.com/Maratyszcza/NNPACK • Mixed precision training • Use float16 instead of float32 for weights and activations • Almost 2x reduction in model size, no loss of accuracy, faster inference • https://devblogs.nvidia.com/parallelforall/mixed-precision-training-deep-neural-networks/
Simple deployment of code and model • Train a model in SageMaker (or bring your own). • Write a Lambda function performing prediction. • Add both as resources in your Greengrass group. • Let Greengrass handle deployment and updates. Best when You want the same programming model in the Cloud and at the Edge. Code and models need to be updated, even if network connectivity is infrequent or unreliable. One device in the group should be able to perform prediction on behalf on other devices. Requirements Devices are powerful enough to run Greengrass (XXX HW requirements) Devices are provisioned in AWS IoT (certificate, keys).
ML Inference using AWS Greengrass Deploy model and Lambda separately Send insights Predict and take actions locally on device AWS Cloud
AWS DeepLens Intel Atom CPU Gen9 graphics Ubuntu 16.04 LTS 100 GFLOPS performance Dual band Wi-Fi 8 GB RAM 16 GB Storage (eMMC) 32 GB SD card 4 MP camera with MJPEG H.264 encoding at 1080p resolution 2 USB ports Micro HDMI Audio out AWS Greengrass preconfigured Intel clDNN for Apache MXNet
AWS DeepLens Architecture Video out Data out D E P L O Y P R O J E C T S Manage device Console Project Management I N F E R E N C E AWS Cloud Intel Model Optimizer
AWS DeepLens
Object detection with AWS DeepLens
Resources
Resources https://mxnet.incubator.apache.org http://gluon.mxnet.io https://aws.amazon.com/sagemaker (free tier available) An overview of Amazon SageMaker: https://www.youtube.com/watch?v=ym7NEYEx9x4 https://github.com/awslabs/amazon-sagemaker-examples https://aws.amazon.com/greengrass (free tier available) https://aws.amazon.com/deeplens https://github.com/intel/clDNN https://medium.com/@julsimon You’ll pay for this.
© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Thank you! Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon

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Deep Learning at the Edge

  • 1. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon Deep Learning at the Edge
  • 2. Deep Learning at the Edge 1. Flexible experimentation in the Cloud. 2. Scalable training in the Cloud. 3. Good prediction performance at the Edge. 4. Simple deployment of code and model
  • 3. Flexible experimentation in the Cloud • Apache MXNet: Python, R, Perl, Matlab, Scala, C++. • Gluon • Imperative programming aka ‘define-by-run’. • Inspect, debug and modify models during training. • Extensive model zoo • Pre-trained computer vision models. • MobileNet, SqueezeNet for resource-constrained devices.
  • 4. Scalable training in the Cloud Amazon SageMaker AWS Deep Learning AMI Amazon EC2 c5 p3
  • 5. Good prediction performance at the Edge • MXNet is written in C++. • Gluon networks can be ‘hybridized’ for additional speed. • Two libraries boost performance on CPU-only devices • Fast implementation of math primitives • Hardware-specific instructions, e.g. Intel AVX or ARM NEON • Intel Math Kernel Library https://software.intel.com/en-us/mkl • NNPACK https://github.com/Maratyszcza/NNPACK • Mixed precision training • Use float16 instead of float32 for weights and activations • Almost 2x reduction in model size, no loss of accuracy, faster inference • https://devblogs.nvidia.com/parallelforall/mixed-precision-training-deep-neural-networks/
  • 6. Simple deployment of code and model • Train a model in SageMaker (or bring your own). • Write a Lambda function performing prediction. • Add both as resources in your Greengrass group. • Let Greengrass handle deployment and updates. Best when You want the same programming model in the Cloud and at the Edge. Code and models need to be updated, even if network connectivity is infrequent or unreliable. One device in the group should be able to perform prediction on behalf on other devices. Requirements Devices are powerful enough to run Greengrass (XXX HW requirements) Devices are provisioned in AWS IoT (certificate, keys).
  • 7. ML Inference using AWS Greengrass Deploy model and Lambda separately Send insights Predict and take actions locally on device AWS Cloud
  • 8. AWS DeepLens Intel Atom CPU Gen9 graphics Ubuntu 16.04 LTS 100 GFLOPS performance Dual band Wi-Fi 8 GB RAM 16 GB Storage (eMMC) 32 GB SD card 4 MP camera with MJPEG H.264 encoding at 1080p resolution 2 USB ports Micro HDMI Audio out AWS Greengrass preconfigured Intel clDNN for Apache MXNet
  • 9. AWS DeepLens Architecture Video out Data out D E P L O Y P R O J E C T S Manage device Console Project Management I N F E R E N C E AWS Cloud Intel Model Optimizer
  • 11. Object detection with AWS DeepLens
  • 13. Resources https://mxnet.incubator.apache.org http://gluon.mxnet.io https://aws.amazon.com/sagemaker (free tier available) An overview of Amazon SageMaker: https://www.youtube.com/watch?v=ym7NEYEx9x4 https://github.com/awslabs/amazon-sagemaker-examples https://aws.amazon.com/greengrass (free tier available) https://aws.amazon.com/deeplens https://github.com/intel/clDNN https://medium.com/@julsimon You’ll pay for this.
  • 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Thank you! Julien Simon, Principal Evangelist, AI & Machine Learning @julsimon