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Amazon SageMaker

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Amazon SageMaker

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Jayson Hsieh, Solutions Architect 2019/11/08 Amazon SageMaker
  2. 2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 機器學習系統的常見問題
  3. 3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 典型的機器學習流程 推論推論 ⼤量的CPU或GPU 持續部署 適⽤於各種設備 ⼤量的 GPU 處理大規模數據 重複試錯 學習 編寫機器學習程式 少量資料檢查 Model 可 ⽤性 開發
  4. 4. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 典型的機器學習流程 推論推論 ⼯程師構建⽣產環境 部署到 API 服務器 與邊緣設備⼀起使⽤ 學習開發 在開發環境中⼯作的資料科學家 在同⼀ EC2 上開發和學習 使⽤ GPU EC2 進⾏深度學習
  5. 5. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 典型的機器學習流程 推論推論 ⼯程師構建⽣產環境 部署到 API 服務器 與邊緣設備⼀起使⽤ 學習開發 在開發環境中⼯作的資料科學家 在同⼀ EC2 上開發和學習 使⽤ GPU EC2 進⾏深度學習 開發與學習 • 難以建立的環境 • 難以並行運行多個學習作業 • 使用多台機器難以實現分佈式學習 • 難以管理學習成果 推論 • API 伺服器的構建和維護難以進行推理 • 難以部署在邊緣設備上 • 建立批處理推理機制很麻煩
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Dan R. Mbanga – Business Development Manager, Amazon AI David Arpin – AI Platforms Selection Leader, Amazon AI End-to-End Managed ML Platform
  7. 7. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker 概要
  8. 8. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker • 解決機器學習系統中常見問題並使資料科學家和工程師能夠快速轉變流程的 服務 • 除了使機器學習基礎架構的構建和操作自動化之外,還提供了各種其他功能。 • 在13個地區部署服務
  9. 9. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker Architecture AWS CloudOffice Network Jupyter Notebook Training Inference client SageMaker Service
  10. 10. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker 開發流程 AWS CloudOffice Network Jupyter Notebook Client 1. 啟動筆記本並從瀏覽器訪問 2. 編寫機器學習程式碼並保存在本地 SageMaker Service
  11. 11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker Service Amazon SageMaker 訓練流程 AWS CloudOffice Network JupyterClient 1.通過SDK 開展學習工作 4. 保存訓練好的模型 Training 2. 啟動訓練EC2 3.在容器上帶有學習數據 加載程式碼並執行學習 5. 訓練完成後, 實例也會被刪除
  12. 12. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker Service Amazon SageMaker API Inference AWS CloudOffice Network JupyterClient 1.通過SDK創建推斷端點 Inference 2.推論實例開始 3.將模型加載到容器上當做端點
  13. 13. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker Service Amazon SageMaker Inference Batch Inference AWS CloudOffice Network JupyterClient 1.通過SDK批處理 創建推理作業 Inference 2.推論實例開始 3.模型在容器上 4.來自S3的推斷數據 讀取結果並將其輸出到S3 5.推理完成後刪除實例
  14. 14. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker Training & Evaluating
  15. 15. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Model Training Options
  16. 16. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Built-in Algorithm • Linear Learner • Factorization Machines • XGBoost • Image Classification • seq2seq • K-means • k-NN • Object2Vec • Semantic Segmentation • PCA • LDA • Neural Topic Model • DeepAR Forecasting • BlazingText (word2vec) • Random Cut Forest • Object Detection • IP Insights https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
  17. 17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Evaluating the model
  18. 18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker SDK 開發流程
  19. 19. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker Python SDK & Examples • SDK代碼和文檔可在github上找到 • 許多使用 SDK 的筆記本樣本都發佈在github上 https://github.com/aws/sagemaker-python-sdk https://github.com/awslabs/amazon-sagemaker-examples
  20. 20. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark SageMaker SDK 基本結構 創建 estimator 利用 Chainer 選擇訓練用機型及 指定本地開發的腳本 當執行fit()時,將啟動指定的 EC2,讀 取準備好的Chainer容器,並使用S3數據 執行學習作業 學習之後,執行deploy()方法創建一個 API 端點。 推斷實際上可以通過predict ()執行 Batch 處理推斷由Transformer.transform ()執行。 從S3讀取目標數據並將推理文 件輸出到S3
  21. 21. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2019, Amazon Web Services, Inc. or its Affiliates. Demo

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