Developers spend much time and effort delivering machine learning (ML) models that can make fast and accurate predictions in real time. These models become even more critical for edge devices where memory and processing power are constrained. Amazon SageMaker Neo enables developers to run and develop models in the most optimized way. With Neo, developers can train ML models once and run them anywhere in the cloud and at the edge. In this chalk talk, we dive deep into Neo and show you how this capability of Amazon SageMaker automatically optimizes models built on TensorFlow, Apache MXNet, PYTorch, and ONNX. 

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Train once, deploy anywhere on the cloud
and at the edge with Neo
Vebhhav Singh
Sr. Solutions Architect
Amazon Web Services
A I M 3 0 1

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Amazon AI/ML Stack
M L S E R V I C E S
M L F R A M E W O R K S
A I S E R V I C E S
(ML researchers
and academics)
(ML developers
and data scientists)
A m a z o n
S a g e M a k e r
G R O U N D T R U T H A L G O R I T H M S
N O T E B O O K S
M A R K E T P L A C E
U N S U P E R V I S E D
L E A R N I N G
S U P E R V I S E D
L E A R N I N G
R E I N F O R C E M E N T
L E A R N I N G
O P T I M I Z A T I O N
( N E O )
T R A I N I N G
H O S T I N G
D E P L O Y M E N T
Vision Speech Language Chatbots &
Contact Centers
Verticals
A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
R e k o g n i t i o n
V i d e o
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n d A m a z o n
L e x
A m a z o n
F o r e c a s t
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
(Application
Developers)

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Deployment of ML models is complex
Repeat for every model and every change in the model
BYO
AWS
Build with your
own algorithms
Build with built-in algorithms
from AWS
Train with
TensorFlow,
MXNet,
or PyTorch
Optimize
your models
Deploy to
the cloud
Deploy to
the edge
A B
A/B test

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The majority of the cost and
complexity of ML in
production is due to inference
Inference (prediction)
90%
Training
10%

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Problem: Not all models are skinny
Models that are accurate tend to
be big and slow
Models are chained to the
framework in which they were
trainedS
L
M
Accuracy
Performance

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Problem: Not all targets are easy
Need enormous expertise …
Application development: Cloud-native or embedded system
Machine learning: Model training and parameter tuning
Performance tuning: Troubleshooting and optimization
Frameworks: TensorFlow or MXNet or PyTorch or Chainer
Hardware: Cloud server or edge device
Computer architecture: x86 or RISC or GPU or FPGA or ASIC
…and endless time

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Problem: Not every path is a catwalk

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What if you could train your model once and run it
anywhere, in the cloud or at the edge, with twice the
speed and no loss in accuracy?

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Amazon
SageMaker Neo

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Increase inference performance by 2x
Reduce runtime footprint by 100x

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Amazon SageMaker Neo
K E Y F E A T U R E S
Machine learning compiler for CPU or GPU
Compact open source runtime
https://aws.amazon.com/sagemaker/neo

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Neo bridges Amazon SageMaker & AWS IoT Greengrass
Data labeling &
pre-built
notebooks
for common
problems
Model and
algorithm
marketplace &
Built-in, high-
performance
algorithms
One-click
training on the
highest
performing
infrastructure
One-click model
optimization
One-click
deployment
Improves performance on
selected hardware
Extends AWS IoT
services onto your
devices
NeoAmazon SageMaker AWS IoT Greengrass
ML models train once run anywhere

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Neo delivers compilation as a service
Parses
model
Optimizes
tensors
Generates
code
Optimizes
graph
Convert a TensorFlow,
MXNet, PyTorch, or
XGBoost model into a
common format
Detect patterns in the
ML model structure to
reduce the execution
time
Detect patterns in the shape
of input data to allocate
memory efficiently
Use a low-level compiler to
generate machine code for
each target
No additional cost for Amazon SageMaker users

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Neo delivers compilation as a service
Parses
model
Optimizes
tensors
Generates
code
Optimizes
graph
Graph pruning
Layer fusion
Constant folding
Layout transforms
Nested Parallelism
Tiling
Tensorization
Auto-tuning
Uses Treelite and Apache TVM for model optimization

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Neo delivers a compact runtime
Dispatches
model
Partitions
graph
Matches model with
execution backend
Sends subgraph to
suitable accelerator
Framework Size
MXNet 450 MB
TensorFlow 660 MB
PyTorch 1000 MB
Neo 1 MB
Open-source software enables device-specific customization

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Steps for sample deployment
• Get an existing model
• Compile model for new hardware
• Copy the artifacts to new hardware
• Deploy the Neo runtime
• Run the inference

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Neo: Open-source project

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Q&A

20. Thank you!
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