Build Deep Learning Applications Using PyTorch and Amazon SageMaker (AIM432-R1) - AWS re:Invent 2018

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This document discusses building deep learning applications using PyTorch and Amazon SageMaker. It provides an overview of PyTorch and its key features and capabilities for deep learning. It also describes Amazon SageMaker as a fully managed service for hosting, deploying, and managing machine learning models at scale. It presents an example of bringing a PyTorch model to SageMaker using a PyTorch container and discussing related solution architectures and breakout sessions.

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Build Deep Learning Applications Using
PyTorch and Amazon SageMaker
Dylan Tong
Solutions Architect
Amazon Web Services
A I M 4 3 2
Vikram
Gangulavoipalyam
Solutions Architect
Amazon Web Services
Yash Pant
Solutions Architect
Amazon Web Services

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
PyTorch
“An open source deep-learning platform that provides a seamless path from prototype to production.”

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Key Features and Capabilities

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Why PyTorch

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Bring Your Own Script: PyTorch Container

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Shared weights
Similarity(v1,v2)
Adam  min: contrastive loss(similarity(v1,v2),labels)
v1
v2
CNN
CNN
ResNet-152 (transfer learning)
Source: https://arxiv.org/abs/1311.2901
The CNN extracts features from images, which allows us to map images through training into a vector space that represents the similarity
between images.

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Advance analytics

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Solution architecture

© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Related breakouts
Monday, Nov 26
AIM402-R Deep Learning Applications Using PyTorch, Featuring Facebook
10:00 AM – 11:00 AM | Lando 4202
Tuesday, Nov 27
AIM402-R Deep Learning Applications Using PyTorch, Featuring Facebook
4:45 PM – 5:45 PM | Aria West, Level 3, Juniper 4

Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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