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

�

� @

�

( )

����

Nt ) �2 J

- AG Tz g gac

X omu• 1 CC lM o

• 3 C 23 e kfr i �

• 3 C . C s pmw

• 4 � ygzSIc b

( MK nP

!

http://hunkim.github.io/ml/

F R A M E W O R K S A N D I N T E R F A C E S

����������� ����

P3

P3 Instance Deep Learning AMI

Frameworks

PLATFORM SERVICES

VIS ION LANGUAGE VR/ IR

APPLICATION SERVICE

AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk

A W S D E E P LE A R N IN G A M I

Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano

IN S T A N C E S

GPU (G2/P2/P3) CPU (C5)

NVIDIATesla V100 GPU

5,120 Tensor cores 1 Petaflop

128GB of memory NVLink 2.0

14X faster than P2

!

()

-

J N)( (( �

-

J N)( (( �

K-Means ClusteringPrincipal Component AnalysisNeural Topic ModellingFactorization MachinesLinear Learner - Regression

XGBoostLatent Dirichlet AllocationImage ClassificationSeq2SeqLinear Learner - Classification

ALGORITHMS

Apache MXNetTensorFlow

Caffe2, CNTK, PyTorch, Torch

FRAMEWORKS

/ . ..

••

,

: / / .

C A D

,65�.88 �387 9�,41 e�t g

2 8 H DM55 �2 8 H u

EWLp a

L yE n L r

2 8 t g,65� in 2 8 H S E,65A C IE L r

2�+2� � � � 2 2 �

JE C

Discrete Classification,Regression

Linear Learner Supervised

XGBoost Algorithm Supervised

Discrete Recommendations Factorization Machines Supervised

Image Classification Image Classification Algorithm Supervised, CNN

Neural Machine Translation Sequence to Sequence Supervised, seq2seq

Time-series Prediction DeepAR Supervised, RNN

Discrete Groupings K-Means Algorithm Unsupervised

Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised

Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised

Neural Topic Model (NTM) Unsupervised, Neural Network Based

-

J N)( (( �

-

HJ

�

N

�

�

K-Means Clustering

Principal Component Analysis

Neural Topic Modelling

Factorization Machines

Linear Learner - Regression

XGBoost

Latent Dirichlet Allocation

Image Classification

Seq2Seq

Linear Learner - Classification

BUILT ALGORITHMS

Caffe2, CNTK, PyTorch, Torch

IM Estimators in Spark

DEEP LEARNINGFRAMEWORKS

Bring Your Own Script (IM builds the Container)

BRING YOUR OWN MODEL

MLTraining code

Fetch Training dataSave Model Artifacts

Amazon ECR

Save Inference Image

Amazon S3

�

https://nucleusresearch.com/research/single/guidebook-tensorflow-aws/

In analyzing the experiences of researchers supporting more than 388unique projects, Nucleus found that 88 percent of cloud-based TensorFlow projects are running on Amazon Web Services (AWS).

“

�� ������from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(entry_point='tf-train.py’, role='SageMakerRole', training_steps=10000, evaluation_steps=100, train_instance_count=1, train_instance_type='ml.p2.xlarge’)

tf_estimator.fit('s3://bucket/path/to/training/data’)

from sagemaker.mxnet import MXNet

mxnet_estimator = MXNet(＂mx-train.py",train_instance_type="ml.p2.xlarge",train_instance_count=1)

mxnet_estimator.fit("s3://my_bucket/my_training_data/")

-

HJ

�

N

�

-

HJ

� �N

�� �������predictor = tf_estimator.deploy(

initial_instance_count=1,instance_type='ml.c4.xlarge')

predictor = mxnet_estimator.deploy(deploy_instance_type="ml.p2.xlarge", min_instances=1,

https://runtime.sagemaker.us-east-1.amazonaws.com/endpoints/model-name/invocations

�

• BK � �A ID A• � � � �A � �I

�� �������

SageMaker Notebooks

Training Algorithm

SageMakerTraining

Amazon ECR

Code Commit

Code Pipeline

SageMakerHosting

Coco dataset

AWS Lambda

APIGateway

Build Train

Deploy

static website hosted on S3

Inference requests

Amazon S3

Amazon Cloudfront

Web assets onCloudfront

� -� �

•• (6 �- ma wC t

k zm h

• , s Sc A 5 5�5� r

• t h v• 6 t ma ,

r n t v

• ) 5 2 ue h t �Ntl i Ntl �

t h s M v

• r oR r g n g

sagemaker = boto3.client(service_name='sagemaker')

sagemaker.create_training_job(**training_params)

create_model_response = sage.create_model(ModelName = model_name,ExecutionRoleArn = role,PrimaryContainer = primary_container)

endpoint_config_response = sage.create_endpoint_config(EndpointConfigName = endpoint_config_name,ProductionVariants=[{

'InstanceType':'ml.m4.xlarge','InitialInstanceCount':1,'ModelName':model_name,'VariantName':'AllTraffic'}])

endpoint_response = sagemaker.create_endpoint('EndpointName': endpoint_name,'EndpointConfigName': endpoint_config_name

����������

2

.

�1 3

.

- �

•• w I n �

l 9 � l NFl

l T

• C x N S• , �C e ,• 03 2 � x

oMs r C• N S• C 8

-

• S• n aN eb

D bFDo

• o A b• 13 � �, � K, D

• 34 137 4 �o T l rC

• A•

�

••••

1

4.75

8.5

12.25

16

1 4.75 8.5 12.25 16

Spee

d up

(x)

# GPUs

Resnet 152

Inceptin V3

Alexnet

Ideal

��� ������ ���

P2.16xlarge (8 Nvidia Tesla K80 - 16 GPUs)Synchronous SGD (Stochastic Gradient Descent)

91% Efficiency

88% Efficiency

16x P2.16xlarge by AWS CloudFormationMounted on Amazon EFS

# GPUs

## train data

num_gpus = 4gpus = [mx.gpu(i) for i in range(num_gpus)]model = mx.model.FeedForward(

ctx = gpus,symbol = softmax,num_round = 20,learning_rate = 0.01,momentum = 0.9,wd = 0.00001)

model.fit(X = train, eval_data = val,batch_end_callback = mx.callback.Speedometer(batch_size=batch_size))

��� ������ ���

기반예제 � �

B : A I A AA• ( � A B

• . DD � A � DD � A B A• A � A � IBD A � AD� D � AD• -A � D � AD � D :� � D

• BB A• -. �D A: � D � :• /D � �BD D � A• - � AD� C � D :• A D�• D � � �A � � D �)A B D� A � A �)..

http://mxnet.io/https://github.com/dmlc/mxnethttp://incubator.apache.org/projects/mxnet.html

http://gluon.mxnet.io

-

H• ,X P b fd S

• ( C X g NTMI ce

• ) A ) A� A �A K a W

• A �,C � � C a

� / -

•• X

MN

• e G 3• N S

• / �e i

• M G•

���� ��������� ���������

We plan to use Amazon SageMaker to train modelsagainst petabytes of Earth observation imagery datasetsusing hosted Jupyter notebooks, so DigitalGlobe'sGeospatial Big Data Platform (GBDX) users can just push abutton, create a model, and deploy it all within onescalable distributed environment at scale.

- Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe

EC

: A C

�

�

�

“With Amazon SageMaker, we can accelerate our Artificial Intelligenceinitiatives at scale by building and deploying our algorithms on theplatform. We will create novel large-scale machine learning and AIalgorithms and deploy them on this platform to solve complexproblems that can power prosperity for our customers."

- Ashok Srivastava, Chief Data Officer, Intuit

$$$$

$$$

$$

$

Minutes Hours Days Weeks Months

Single Machine

Distributed, withStrong Machines

���������

$$$$

$$$

$$

$

Minutes Hours Days Weeks Months

EC2 + AMI

Amazon SageMaker

On-premise

���������

. 2 �) ( 1

G) : - h U 9 8 ).0 h U G) : - h U

8 )/ h U 9 ) 8 .- h U 8 )/ h U

D) 8 ( )- h U D* ) 8 )/ h U D) 8 ( )- h U

wU ( od kx (. pg gz kx ( pg gz

m 553 �g n ( 42U -62

o e i (- 42U (- 42U

3A• S apg g1 G) : cm ) h• 1� 8 �cm h• g 1 8 �cm () h

� T s u 753�l ) (/ )n ril t 1 GGD 1 8 8 8 B 9 8 8 D 9 B

. � 6 3

70�r z d h ( ,w B ) = J �6J =G n�h ay * o B B:G = s �h ( o

B ) = J � n *53 )53d 2 : D�8* �(53 g � c U ( (

w n a w p Q

( , B ) = J /.)

) B B:G = ( () ) )

. B ) = J , *,,

tuxp

� (�1� 53 � (�1� 53 . (,,,,

�(�1� 53 �(�1� 53 53 ( ( /

2 : D�8*�p *53 /

�iS m k z e U 84� ) (/ )x l0 0 : : : D A : = :A=G G D

����

NK : - : / /

D• : : - : / : / /

B D � : . -: : - : / / /: . :

• / : - : : : / / / /:

• � � : - : : / / :.