Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Deep Learning withTensorFlow and
Apache MXNet onAmazonSageMaker
Julien Simon
Global Evangelist, AI & Machine Learning
@julsimon

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
M L F R A M E W O R K S &
I N F R A S T R U C T U R E
The Amazon ML Stack: Broadest & Deepest Set of Capabilities
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Models without training data (REINFORCEMENT LEARNING)
Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
NEW NEWNEW
NEW
NEW
NEWNEW
NEW
NEW

AWS is framework agnostic
Choose from popular frameworks
Run them fully managed Or run them yourself

AmazonSageMaker:
Build,Train,and Deploy MLModels atScale
1
2
3

© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
AWS Deep Learning AMIs
Preconfigured environments to build Deep Learning applications
Conda AMI
For developers who want pre-
installed pip packages of DL
frameworks in separate virtual
environments.
Base AMI
For developers who want a clean
slate to set up private DL engine
repositories or custom builds of DL
engines.
AMI with source code
For developers who want
preinstalled DL frameworks and their
source code in a shared Python
environment.

TensorFlow
• Open source software library for Machine Learning
• MainAPI in Python, experimental support for other languages
• Built-in support for many network architectures: FC, CNN, LSTM, etc.
• Support for symbolic execution, as well as imperative execution since v1.7
(aka “eager execution”)
• Complemented by the Keras high-levelAPI

AWS: The platform of choice to run TensorFlow
85% of all
TensorFlow
workloads in the
cloud runs on AWS

Training a ResNet-50 benchmark with the synthetic ImageNet dataset using
our optimized build ofTensorFlow 1.11 on a c5.18xlarge instance type is 11x
faster than training on the stock binaries.
https://aws.amazon.com/about-aws/whats-new/2018/10/chainer4-4_theano_1-0-2_launch_deep_learning_ami/
October 2018
OptimizingTensorFlow forAmazon EC2C5instances

OptimizingTensorFlow forAmazon EC2P3 instances
65%
30m
90%
14m

TensorFlowonAmazonSageMaker:afirst-classcitizen
• Built-in containers for training and prediction.
• Code available on Github: https://github.com/aws/sagemaker-tensorflow-containers
• Build it, run it on your own machine, customize it, push it to Amazon ECR, etc.
• Supported versions: 1.4.1, 1.5.0, 1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0, 1.13.0
• Advanced features
• Local mode: train on the notebook instance for faster experimentation
• Script mode: use the sameTensorFlow code as on your local machine (1.11.0 and up)
• Distributed training: zero setup!
• Pipe mode: stream large datasets directly fromAmazon S3
• TensorBoard: visualize the progress of your training jobs
• Keras support (tf.keras.* and keras.*)

ApacheMXNet
• Open source software library for Deep Learning
• Natively implemented in C++
• Built-in support for many network architectures: FC, CNN, LSTM, etc.
• SymbolicAPI: Python, Scala, Clojure, R, Julia, Perl, Java (inference only)
• ImperativeAPI: Gluon (Python), with computer vision and natural language
processing toolkits

Apache MXNet: deep learning for enterprise
developers
2x faster

ApacheMXNetonAmazonSageMaker:afirst-classcitizen
• Built-in containers for training and prediction.
• Code available on Github: https://github.com/aws/sagemaker-mxnet-container
• Build it, run it on your own machine, customize it, push it to Amazon ECR, etc.
• Supported versions: 0.12.1, 1.0.0, 1.1.0, 1.2.1, 1.3.0
• Advanced features
• Local mode: train on the notebook instance for faster experimentation
• Script mode: use the sameTensorFlow as on your local machine
• Distributed training: zero setup!
• Pipe mode: stream large datasets directly fromAmazon S3
• Keras support (tf.keras.* and keras.*)

TRACK WAFFLE FRY FRESHNESS
Identify fries that have exceeded
the hold time
USES DEEP LEARNING
Computer vision model for object
detection and tracking
BUILT THE MODEL USING MXNet
A team of enterprise developers with
no ML expertise
Apache MXNet customer example: Chick-fil-A
https://www.youtube.com/watch?v=dKcyAjCtXqc

Inference
(Prediction)
90%
Training
10%

Hardwareoptimization isextremelycomplex

AmazonSageMakerNeo
Trainonce,runanywherewith2xtheperformance
K E Y F E A T U R E S
Open-source Neo-AI runtime and compiler
under the Apache software license;
1/10th the size of original frameworks
github.com/neo-ai

Are youmaking themostof your infrastructure?

Amazon ElasticInference
Reducedeeplearninginferencecostsupto75%
K E Y F E A T U R E S
Integrated with
Amazon EC2 and
Amazon SageMaker
Support forTensorFlow and
Apache MXNet
Single and
mixed-precision
operations

© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Getting started
http://aws.amazon.com/free
https://ml.aws
https://aws.amazon.com/sagemaker
https://github.com/aws/sagemaker-python-sdk
https://github.com/awslabs/amazon-sagemaker-examples
https://medium.com/@julsimon
https://gitlab.com/juliensimon/dlnotebooks

Deep Learning with TensorFlow and Apache MXNet on Amazon SageMaker (March 2019)

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