In this workshop, you gain hands-on experience with Amazon SageMaker, which offers the highest performing GPU-based training instances in the cloud for efficient model training and cost-effective model inference hosting. We first introduce the most common machine vision scenarios. Then, we share the deep learning application development lifecycle. Finally, we demonstrate how to accelerate the process by leveraging the power of Amazon EC2 P3 GPU instances.

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Optimize Deep Learning Training and Inferencing
Using GPU and Amazon SageMaker
Wenming Ye
Sr. Solutions Architect (AI/ML)
Amazon Web Services
C M P 3 0 2

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Agenda
• Lab logistics
• Use your own AWS account ID, or pair up
• Apply AWS credit to your account
• Deep learning for computer vision
• Lab 1: Image classification
• Lab 2: Object detection

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Scenarios and deep learning architecture
• Architecture
• Convolutional neural network (CNN)
• Scenarios
• Image classification
• Object detection
• Image segmentation
• Visual search
• Generative adversarial networks (GANs) for item generation

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Deep learning in computer vision
.02
.85
p(cat)
p(dog)
Convolutional neural
network
Layer 1 Layer 2 Output
Explore spatial information with convolution layers
Img src: https://leonardoaraujosantos.gitbooks.io/artificial-inteligence

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http://scs.ryerson.ca/~aharley/vis/conv

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Object detection

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Single shot detector

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Image segmentation

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KITTI

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Visual search

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Architecture

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Pipeline stages
Image query processing
Data normalization or augmentation
Embedding
DNN model(s)
KNN + ranking
Post-processing, deduplication

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Demo: Amazon shopping

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Domains

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Embedding = Learned representation space

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Demo notebook + TensorBoard
https://github.com/miroenev/teach_DL/blob/master/data_buckets_fashion_mnist_cnn_ae.ipynb

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GAN overview

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GANs @ celebrity faces

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Helping ShopBop design new shoes using progressive
GANs

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• Up to eight NVIDIA Tesla V100 GPUs
• 1 PetaFLOPs of computational performance – Up
to 14x better than P2
• 300 GB/s GPU-to-GPU communication (NVLink)
– 9x better than P2
• 16/32 GB GPU memory with 900 GB/sec peak
GPU memory bandwidth
O n e o f t h e f a s t e s t , m o s t p o w e r f u l G P U i n s t a n c e s i n t h e c l o u d
Amazon EC2 P3 and P3dn instances

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Use cases for P3 instances
Machine learning & AI High performance computing
Natural language
processing
Image and video
recognition
Autonomous vehicle
systems
Recommendation systems
Computational fluid
dynamics
Financial and data analytics
Weather
simulation
Computational chemistry

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A fully managed servicethat enables data scientists and developersto quickly and easily
buildmodels based on machine learning models into production as smart applications.
Amazon SageMaker

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One-click training for
ML, DL, and custom
algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without engineering
effort
Fully managed
hosting at scale
BuildPre-built notebook
instances
Deploy
Train
Amazon SageMaker

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Amazon SageMaker: Launch customers

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Amazon SageMaker
1 2 3 4
I I I I
Notebook instances Algorithms ML training service ML hosting service

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Labs
1. Image classification
2. Object detection
URL: https://bit.ly/2TNdEI0

33. Thank you!
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Wenming Ye
@wenmingye