August 2018
Intermediate to advanced
336 pages
5h 9m
Japanese
Content preview from 直感 Deep Learning ―Python×Kerasでアイデアを形にするレシピ
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付録AGPUを考慮した開発環境の構築
大串 正矢●株式会社カブク
本付録は日本語版オリジナルの記事です。ディープラーニングの高速な計算にはGPUが不可欠です。本稿では、GPUを考慮した開発環境の構築について解説します。
A.1 TensorFlowとGPU
ディープラーニングでは行列演算を使用する機会が多く、行列演算の高速化には並列処理が不可欠です。
並列処理においてはグラフィック処理ユニット（graphical processing unit：GPU）はCPUに比べてコア数の多さ、メモリバンド幅の大きさからパフォーマンスが高いため高速な計算には不可欠になっています。
並列化したスレッドと共有メモリとの接続パスがボトルネックになるため、メモリバンド幅は大きいほうが効果的です。
本稿ではNVIDAが提供しているGPU（GeForce GTX 1080）をKeras（バックエンドがTensorFlow）で使用するために必要なライブラリのインストール方法について紹介します。
構築環境はUbuntu 16.04 LTSです。
A.2 具体的なインストール方法：ホスト
必要なパッケージをインストールします。
$ sudo apt-get install -y software-properties-common $ sudo add-apt-repository ppa:graphics-drivers/ppa $ sudo apt-get update $ sudo apt-get install -y nvidia-390 $ sudo apt-get install -y build-essential $ sudo apt-get update $ sudo shutdown -r ...
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