Amazon SageMaker · 2020. 10. 17. · © 2019, Amazon Web Services, Inc. or its Affiliates. All...

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Solutions Architect, Amazon Web Services Japan.

2019.6.18 Shoko Utsunomiya

Amazon SageMaker

Amazon SageMaker


自己紹介

宇都宮聖子, Ph.D.

• 機械学習ソリューションアーキテクト

• 機械学習サービスを担当

• 前々職は量子情報の研究者

• 前職は自動車OEMで自動運転開発

• 担当領域

• 自動運転、AIヘルスケア、AI ゲーム

• 好きなサービス

• Amazon SageMaker


• SageMaker

• SageMaker

•

•

• SageMaker

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

様々な業界で使われる機械学習

メディア(エンターテイメン

ト)

流通・小売ヘルスケア，ライフサイエンス

金融（サービス，取引）

自動車，製造


典型的な機械学習のワークフロー

データ前処理

モデルの開発

モデルの学習モデルの評価

本番環境へのデプロイ

監視・評価データ収集


典型的な機械学習のワークフロー

データ前処理

モデルの開発

モデルの学習モデルの評価

本番環境へのデプロイ

監視・評価データ収集

できるだけ高速に負担なくこのサイクルを回したい


データ取得：高クオリティのラベル付きデータを低コストで

データの前処理：適切なデータ前処理のパイプライン

開発環境：すべてのエンジニアへ継続的なML環境提供を低コストで

学習環境：大量データに対するスケーラブルな学習環境

ジョブ管理：学習ジョブとモデルの管理，トレーサビリティ

モデル構築：研究開発の最新の成果をいち早くアップデート

運用：実運用からのフィードバックをよりクイックに、よりたくさん

推論・デプロイ：エンドポイントのホスティングを楽にモデル開発から運用開始のタイムラグを減らしたい

セキュリティ：会社や法律に準拠した運用環境の提供

機械学習のワークフローでよくある課題


機械学習における “Undifferentiated Heavy Lifting”

開発環境構築

• 必要なリソースの見積もりと購入の決断

• 開発チームで均一な開発環境構築

• フレームワークのインストール，バージョン管理

機械学習モデルの学習

• CPU/GPU など用途にあったハードウェア環境提供

• スケーラブルな分散学習構築と広帯域な通信環境

運用

• 推論環境の準備とモデルのホスティング

• 機械学習と異なるスキルセットが求められる

Photo by Victor Freitas on Unsplash

https://unsplash.com/photos/WvDYdXDzkhs?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText

https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText


AWS の提供する機械学習スタック

App developers with

little knowledge of ML

ML developers and

data scientists

ML researchers and

academics

Amazon SageMaker

Ground Truth

Algorithms

Notebooks

Marketplace

UnsupervisedLearning

SupervisedLearning

ReinforcementLearning

Optimization(Neo)

Training

Hosting

Deployment

Frameworks Interfaces Infrastructure

Amazon Rekognition

Image

AmazonPolly

AmazonTranscribe

AmazonTranslate

AmazonComprehend

AmazonLex

AmazonRekognition

Video

Vision Speech Language Chatbots

AmazonForecast

Forecasting

AmazonTextract

AmazonPersonalize

Recommendations

AmazonEC2 P3 & P3DN

AmazonEC2 C5

FPGAs AWSGreengrass

AmazonElastic

Inference

AmazonInferentia

Labeling Model development Training HostingML SERVICES

ML FRAMEWORKS & INFRASTRUCTURE

AI SERVICES


AWS の提供する機械学習スタック

ML developers and

data scientists

ML researchers and

academics

Amazon SageMaker

Ground Truth

Algorithms

Notebooks

Marketplace

UnsupervisedLearning

SupervisedLearning

ReinforcementLearning

Optimization(Neo)

Training

Hosting

Deployment

Frameworks Interfaces Infrastructure

AmazonEC2 P3 & P3DN

AmazonEC2 C5

FPGAs AWSGreengrass

AmazonElastic

Inference

AmazonInferentia

Labeling Model development Training HostingML SERVICES

ML FRAMEWORKS & INFRASTRUCTURE

マネージドサービスを活用しビジネスの価値にフォーカス

Amazon SageMaker

API

13

SDK

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

Amazon SageMaker Notebook instance

❖ Jupyter ❖ JupyterLab

•

• Git

• SageMaker

SageMaker Python SDK

https://github.com/aws/sagemaker-python-sdk


:

• API

•

•

AWS Summit NY spot

: API

•

API1

• GPU

Elastic Inference

•

•

• A/B

A/B

2:

SageMaker

3: GPU

AWS

1:

SageMaker


• SageMaker

• SageMaker

•

•

• SageMaker

SageMaker

( )

• Amazon S3

•

• API

( )

• Amazon ECR (Elastic

Container Registry)

• (TensorFlow )

…

•

SageMaker

•

S3

/opt/ml/input/data/dog1.jpgdog2.jpg

image/jpeg

application/

json

Amazon SageMaker

( )

Amazon SageMaker

AWS CloudOffice

NetworkSageMaker Service

1.

AWS CloudOffice


2.

AWS CloudOffice


3. S3

AWS CloudOffice


4.

AWS CloudOffice


5.

AWS CloudOffice


6.

AWS CloudOffice



• SageMaker

• SageMaker

•

•

• SageMaker

( )

S3

• 16TB

• Pandas

• EMR

S3

FILE (Default):

…

PIPE :

•

• TensorFlow, MXNet

Linear Learner

XGBoost XGBoost,(eXtreme Gradient Boosting)

PCA (Principal

Component Analysis)

k-means K

k-NN K

Factorization Machines

Random Cut Forest robust random cut tree

LDA (Latent Dirichlet Allocation)

~ 機械学習モデル ~SageMaker

※ LDAのオリジナルは教師なし

https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html


Image classification ResNet

Object Detection SSD (Single Shot multibox

Detector)

Semantic

Segmentation

FCN, PSP, DeepLabV3 (ResNet50, ResNet101)

seq2seq Deep LSTM

Neural Topic Model NTM, LDA

Blazing text Word2Vec

Text Classification

Object2Vec Word2Vec

DeepAR Forecasting Autoregressive RNN

IP Insights NN (IP entity ) IP

~ ディープラーニングモデル ~SageMaker



Image Classification

ILSVRC 2015 ResNet

AWS

( )

https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/image-classification.html

dog cat

AWS Marketplace for Machine Learning

•

200

• SageMaker

•


• AWS

SageMaker

200

Amazon SageMaker

ok

AWS Marketplace


MLマーケットプレイスアルゴリズムの利用（学習）

Marketplace上でアルゴリズムの選択

SageMaker上でアルゴリズム登録

トレーニングジョブの作成・実行


MLマーケットプレイスモデルの利用（推論）

Marketplace 上でモデルの選択

SageMaker 上でのモデルパッケージ登録

エンドポイントの作成


SageMaker SDK AWS

CLI

AWS

ML

•

•

•

•

container

• S3 fit

SageMaker

( )

•

• Docker github

• 2018 re:Invent

SageMaker container

Deep learning TensorFlow Legacy mode: 1.4.1, 1.5.0, 1.6.0, 1.7.0, 1.8.0, 1.9.0,

1.10.0

Script mode: 1.11.0, 1.12.0

Chainer 4.0.0, 4.1.0, 5.0.0

PyTorch 0.4.0, 1.0.0

MXNet 1.3.0, 1.2.1, 1.1.0, 0.12.1

ML scikit-learn 0.20.0

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker

TensorFlow: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/tensorflow

Chainer: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/chainer

PyTorch: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch

MXNet: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/mxnet

Sklearn: https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/sklearn

※ 2019 2 13•

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/tensorflow

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/chainer

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/mxnet

https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/sklearn

SageMaker

•

•

• PC ( )

•

• GPU Elastic Inference

• , A/B

SageMaker SDK Estimator

Chainer Estimator

fit()

Chainer

S3

deploy()

predict()

transformer.transform()

S3

S3

Chainer main

OK

SageMaker

argparse

model_fn()

SageMaker

• API

• ( : TensorFlow Horovod )

estimator = TensorFlow(entry_point='train.py’,

train_instance_type=‘ml.p3.2xlarge’,

train_instance_count=2,…)

opt = tf.train.AdagradOptimizer(0.01 * hvd.size())

opt = hvd.DistributedOptimizer(opt)Optimizer

• Deep Learning

•

• Dropout

•

• …

•

(HPO)

•

https://github.com/aws/sagemaker-python-sdk#sagemaker-automatic-model-tuning

https://aws.amazon.com/jp/blogs/news/amazon-sagemaker-automatic-model-tuning-becomes-

more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/

https://github.com/aws/sagemaker-python-sdk#sagemaker-automatic-model-tuning

https://aws.amazon.com/jp/blogs/news/amazon-sagemaker-automatic-model-tuning-becomes-more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/

• Docker SageMaker

•

• train_instance_type=‘local’ SageMaker Python SDK

Mac Book

SageMaker

•

•

• PC ( )

•

• GPU Elastic Inference

• , A/B

• deploy() API

• Web API URL URL

•

( )

• リアルタイム推論が必要でない場合、推論エンドポイントを維持するとコストがかかる

• バッチ推論では、推論が必要なときに、エンドポイントを作成し、推論後のエンドポイント削除を自動で行う

EP EP

EP:

Elastic Inference

• GPU

• CPU / EC2

GPU

EIA (Elastic Inference Accelerator)

3

• eia1.medium: 8TFLOPS

• eia1.large: 16TFLOPS

• eia1.xlarge: 32TFLOPS

•

•

• SageMakerVariantInvocationsPer

Instance (1 1

)

•

https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html#endpoint-auto-scaling-add-policy

https://docs.aws.amazon.com/ja_jp/autoscaling/application/userguide/application-auto-scaling-target-tracking.html



A/B

• A/B

•

•

•

•

https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_runtime_InvokeEndpoint.html

https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_runtime_InvokeEndpoint.html


• SageMaker

• SageMaker

•

•

• SageMaker


• 2018 re:Invent

•


Git

• Git

• Git


Git Integration

• SageMaker Git

clone

• JupyterLab Git extension


• 2018 re:Invent

•


• CloudWatch Metrics

•

( )


(Beta)

•

•


• 2018 re:Invent

•


Amazon SageMaker Neo

• Tensorflow PyTorch EC2 Greengrass

• Deep Learning 500MB-1GB

Amazon SageMaker Neo Runtime は 1MB

• Apache Software License OSS


SageMaker Python SDK Neo

mnist_estimator = TensorFlow(

entry_point='mnist.py', role=role, framework_version='1.11.0’,

training_steps=1000, evaluation_steps=100,

train_instance_count=2, train_instance_type='ml.c4.xlarge’)

mnist_estimator.fit(inputs)

optimized_estimator = mnist_estimator.compile_model(

target_instance_family='ml_c5', input_shape={'data':[1, 784]},

output_path=output_path, framework='tensorflow’,

framework_version='1.11.0’)

optimized_predictor = optimized_estimator.deploy(

initial_instance_count = 1, instance_type = 'ml.c5.4xlarge')

Amazon SageMaker


• 2018 re:Invent

•


AWS Step Functions API Connectors

• Step Functions

AWS

〜〜

• DynamoDB: item

item

• AWS Batch:

• Amazon ECS/Fargate: ECS Fargate

• Amazon SNS: SNS

• Amazon SQS:

• AWS Glue:

• Amazon SageMaker:


Apache Airflow

• Airflow 1.10.1 SageMaker Operator

• Airflow SageMaker

train_config = training_config(…)

trans_config

=transform_config_from_estimator(…

)

train_op =

SageMakerTrainingOperator(…)

transform_op =

SageMakerTransformOperator(…)

transform_op.set_upstream(train_op)


• (SSE-KMS)

•

•

•

•

• Cloudtrail

• PCI DSS HIPPA


• SageMaker S3 S3 VPC

• S3

• S3

• SageMaker API PrivateLink

• SageMaker Notebook Endpoint

• SageMaker Service API

• SageMaker Runtime API


• SageMaker

• SageMaker

•

•

• SageMaker


•

•


https://mlloft4.splashthat.com/

https://mlloft4.splashthat.com/


Reference

SageMaker Example Notebooks

https://github.com/awslabs/amazon-sagemaker-examples

SageMaker SDK


(Doc : https://readthedocs.org/projects/sagemaker/)

SageMaker

https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/whatis.html

https://github.com/awslabs/amazon-sagemaker-examples


https://readthedocs.org/projects/sagemaker/

https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/whatis.html

Amazon SageMaker · 2020. 10. 17. · © 2019, Amazon Web Services, Inc. or its Affiliates. All...

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Transcript of Amazon SageMaker · 2020. 10. 17. · © 2019, Amazon Web Services, Inc. or its Affiliates. All...