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Makoto Shimura, Solutions Architect 2019/01/15

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学習に使うコードを記述 大量の GPU 大量のCPU や GPU 小規模データで動作確認 大規模データの処理 継続的なデプロイ 試行錯誤の繰り返し 様々なデバイスで動作

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エンジニアがプロダク データサイエンティストが開発環境で作業 ション環境に構築 開発と学習を同じ 1 台のインスタンスで実施 API サーバにデプロイ であれば GPU インスタンスを使用 エッジデバイスで動作

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• API • •

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S3 S3

* IAM

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* JupyterLab JupyterLab

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SageMaker Examples

SageMaker Python Sdk

chainer_single_machine_cifer10.ipynb Use Create copy

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Sagemaker Chainer

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SDK github *

Chainer estimator.fit()

estimator.deploy()

predictor.predict()

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Jupyter Notebook

API

EC2

API AB API

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Jupyter Notebook

API

EC2

API AB API

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Office Network AWS Cloud

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Office Network AWS Cloud

2. 1.

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Office Network AWS Cloud

1. SDK 2. 4.

5.

3.

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Office Network AWS Cloud

1. SDK

3.

2.

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Office Network AWS Cloud 3. 1. SDK

4. S3 S3

2.

5.

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• github SDK • SDK github

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fit() Chainer S3

deploy()

predict()

transformer.transform() S3 S3

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SageMaker argparse

model_fn() SageMaker

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-

-

Tensorflow ( ) Deep Learning Chainer PyTorch MXNet ( Keras)

scikit-learn

Coach Ray

-

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Tensorflow ( Keras) Deep Learning Chainer PyTorch MXNet ( Keras)

scikit-learn

Coach Ray

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• Tensorflow, Chainer, PyTorch, MXNet, scikit-learn SDK • Tensorflow MXNet Keras • main github • github

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ECR ID

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• Redshift, Athena, EMR

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• →

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• • /opt/ml/input/config/resourceConfig.json SageMaker

• SDK estimator.fit(wait=False)

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• SageMaker •

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

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pull • SageMaker • local

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• Accuracy Loss • Linage

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2 • 1 •

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• • • • •

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Text Embedding

Client Numeric vector

Category Classification

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• Elastic Inference CPU EC2 GPU ML

• CPU GPU • 75% • • AWS enhanced versions of TensorFlow • Apache MXNet (including ONNX)

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• SageMaker SDK

• AWS SDK SageMaker API

SageMaker SDK AWS SDK

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1: SageMaker

開発 学習

2: GPU AWS

推論

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• Step Functions SageMaker AWS Glue Lambda • Glue – SageMaker – –

AWS Step Functions workflow

CloudWatch Events ETL

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