DEIM Forum 2019 J
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1 DEIM Forum 2019 J {g ,oguchi}@is.ocha.ac.jp, takefusa@nii.ac.jp, hide-nakada@aist.go.jp Apache Spark Ray Ray Apache Apache Kafka Construction and Evaluation of a Scalable Distributed Stream Processing Infrastructure Kasumi KATO, Atsuko TAKEFUSA, Hidemoto NAKADA, and Masato OGUCHI Ochanomizu University Otsuka,Bunkyo-Ku, Tokyo , Japan National Institute of Informatics Hitotsubashi, Chiyoda-ku, Tokyo, , Japan National Institute of Advanced Industrial Science and Technology (AIST) Umezono, Tsukuba, Ibaraki , Japan {g ,oguchi}@is.ocha.ac.jp, takefusa@nii.ac.jp, hide-nakada@aist.go.jp 1. TensorFlow [1] Chainer [2]
![大規模分散プラットフォーム Apache Spark [4] 以降 Spark と呼ぶ と分散実行フレームワーク Ray [5] の分散性能の調査 を行う 実験から Ray による高速かつスケーラブルな分散処 理が可能であることを示す 次に 分散メッセージングシステ ムの Apache Kafka [3] 以降 Kafka と呼ぶ と Ray を用いた 分散ストリーム処理基盤を構築し](/docs-images/99/139998267/images/2-2.jpg "その性能を示す 実験結果 から Kafka と Ray を用いた提案分散ストリーム処理基盤が高 いスケーラビリティを有することを示す 図 3 Spark を用いたマスタ ワーカ処理 2.")
2 表 1 実験に用いた計算機の性能 OS GPU Ubuntu 16.04LTS Intel(R) Xeon(R) GHz 4 core 2 sockets(8 core) NVIDIA GeForce GTX 980 Memory 45 GiB 図1 想定する大規模分散ストリーム処理基盤 図2 実験環境 非常に困難である 本研究では 複数センサから送られる大量の動画像データの 解析を高効率に行うことを目的とし スケーラブルな分散スト リーム処理基盤の構築手法を提案する まず 予備実験として 大規模分散プラットフォーム Apache Spark [4] 以降 Spark と呼ぶ と分散実行フレームワーク Ray [5] の分散性能の調査 を行う 実験から Ray による高速かつスケーラブルな分散処 理が可能であることを示す 次に 分散メッセージングシステ ムの Apache Kafka [3] 以降 Kafka と呼ぶ と Ray を用いた 分散ストリーム処理基盤を構築し その性能を示す 実験結果 から Kafka と Ray を用いた提案分散ストリーム処理基盤が高 いスケーラビリティを有することを示す 図 3 Spark を用いたマスタ ワーカ処理 2. 分散ストリーム処理基盤 本研究では 図 1 のような大規模ストリームデータ処理基盤 を想定している 各一般家庭に設置されたセンサやカメラから クラウドに送信された動画像データは ストリーム処理基盤に よって収集され 分散処理基盤へと渡される 分散処理基盤が データを受け取ると データの解析処理がディープラーニング フレームワークを用いて行われ 結果がサービスマネジメント システムを通してユーザに返される ストリーム処理基盤とし て Kafka 分散処理基盤として Spark もしくは Ray の採用を 検討している 既発表研究 [6] から Kafka クラスタにより大 量のセンサデータに対するスケーラブルなメッセージングが可 能であることを確認している 想定する処理基盤では Kafka クラスタが各家庭のセンサから収集した画像データを Spark ク ラスタもしくは Ray クラスタに送信し クラスタ内の計算ノー ドを用いた画像データの識別処理が行われる 動画像データの 流量に応じて スケーラブルに処理可能な処理基盤の構築を目 指している 3. Spark と Ray の分散処理性能調査 れの場合において 1 つ 30KB ほどの大きさの ImageNet デー タファイルを 個用意し マスタでファイルを読み出して からワーカで画像データの識別処理が行われ 結果がマスタに 返されるまでの時間を計測した 表 1 に実験に用いた計算機の 性能を示す 実験に用いるマスタと全ワーカは同質のノードに なっている 各ノードは図 2 に示すように 1Gbps のネットワー クで接続されている また 実験では Spark v Ray v PyTorch v TensorFlow v を用いた 3. 1 Spark の分散処理性能調査 図 3 に Spark を用いる場合のマスタ ワーカ処理の構成を 示す Spark の分散処理は以下のように行われる 1 マスタ で Python プログラムが実行される 2 マスタが ImageNet データを読み込み RDD に変換する 3 作成した RDD をマ スタからワーカに渡す 4 ワーカにおいて PyTorch を用いた ImageNet データの識別処理が行われる RDD とは耐障害分 散データセットであり Spark アプリケーション内でデータは RDD に変換されて処理される RDD と Spark に定義された メソッドを用いることで Spark は自動的に分散を行う また 各ノードは Spark Standalone Mode で接続されている 予備実験として Spark と Ray の分散処理性能の調査を行う Spark と Ray それぞれについてクラスタを構築し ディープ ラーニングライブラリ PyTorch [7] とそのバックエンドとして TensorFlow を用いた画像の識別処理を行う 実験では デー タセットとして ImageNet [8] を用いる Spark と Ray それぞ ワーカ数を 1 から 5 まで変化させると同時にパーティション 数を 8 から 48 まで 8 刻みで変化させた際の実行時間の計測結 果を図 4 に示す パーティション数とは処理するデータ全体の 分割数であり 一般的に分割数が多いほどタスクが細かく分か
3 4 Spark 5 10Gbps Spark 2 r5.4xlarge OS Ubuntu 16.04LTS Intel(R) Xeon(R) Platinum GHz 16 core Memory 128 GiB 3 p2.xlarge OS Ubuntu 16.04LTS Intel(R) Xeon(R) E GHz 4 core GPU NVIDIA K80 Memory 61 GiB 6 Ray Spark 3 AWS Gbps G Spark 3. 2 Ray 6 Ray Ray 1 Python 2 Ray Ray PyTorch ImageNet Spark Ray Spark 5 7 Ray ImageNet Spark Ray ImageNet Spark Ray 2 4 Spark Ray
4 4 Spark Ray # of workers 1 5 Phase (1) (2) (1) (2) Spark Ray Ray Kafka Kafka Ray 1 5 Spark 40 4 Spark Ray Spark RDD 1 Ray Spark 4. Ray Kafka 3 Ray Ray Kafka 8 Kafka Ray Kafka Broker Kafka Producer Ray Kafka Consumer 9 Kafka Ray 1Kafka Python 2 Producer Broker 3 Broker Ray Consumer 4Consumer PyTorch TensorFlow Kafka v Kafka Producer 1 Ray Consumer Chen [9] Kafka Producer-Consumer Spark Streaming OpenCV HDFS HBase CNN 3D CNN CNN TensorFlow 6. Spark Ray ImageNet PyTorch
5 Ray Kafka Ray JSPS JP16K (NEDO) JST CREST JPMJCR1503. [1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Largescale machine learning on heterogeneous systems, 2015, pp [Online]. Available: [2] S. Tokui, K. Oono, S. Hido, and J. Clayton, Chainer: a next-generation open source framework for deep learning, in In Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), 2015, 6 pages. [3] Apache kafka, [4] Apache Spark, [5] P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liax, E. Liang, W. Paul, M. I. Jordan, I. Stoica, Ray: A Distributed Framework for Emerging AI Applications, Available: [6], Kafka, 10 (DEIM2018), I3-3, 018. [7] A. Paszke, S. Gross, S. Chintala, G. Chanan,Pytorh: Tensors and dynamic neural networks in python with strong gpu acceleration, Available: [8] J. Deng, W. Dong, R. Socher, K. Li, L. Fei-Fei,ImageNet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, Available: [9] H. Chen, F. Luo, L. Zhao, and Y. Li, Design and Implementation of Real-Time Video Big Data Platform Based on Spark Streaming, International Conference on Computer Science and Application Engineering (CSAE), 2017.
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九州大学学術情報リポジトリ Kyushu University Institutional Repository 回帰分析による CGM 動画再生回数推定 柴田, 知親九州大学工学部電気情報工学科 伊東, 栄典九州大学情報基盤研究開発センター : 准教授 http://hdl.handle.net/2324/1912137 出版情報 :2018-03-05. 電子情報通信学会データ工学研究専門委員会バージョン
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