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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 Deep Learning 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
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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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API
EC2
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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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model_fn() SageMaker
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Tensorflow ( Keras) 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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ECR ID
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•
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• SDK estimator.fit(wait=False)
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Client Numeric vector
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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
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1: SageMaker
開発 学習
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推論
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AWS Step Functions workflow
CloudWatch Events ETL
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