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JA: translation /ja/install #442
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project_path: /_project.yaml | ||
book_path: /_book.yaml | ||
description: <!--no description--> | ||
landing_page: | ||
custom_css_path: /site-assets/css/style.css | ||
nav: both | ||
rows: | ||
- classname: | ||
devsite-landing-row-large-headings | ||
devsite-landing-row-100 | ||
items: | ||
- heading: "TensorFlow 2.0 Alpha is available" | ||
description: > | ||
<p> | ||
Install the preview release of <a href="/alpha">TensorFlow 2.0 Alpha</a>. | ||
See the <a href="./gpu">GPU guide</a> for CUDA®-enabled cards. | ||
</p> | ||
<pre class="prettyprint lang-bsh"> | ||
<code class="devsite-terminal">pip install tensorflow==2.0.0-alpha0</code> | ||
</pre> | ||
|
||
- classname: | ||
devsite-landing-row-large-headings | ||
devsite-landing-row-100 | ||
items: | ||
- heading: "Install TensorFlow" | ||
description: > | ||
<p>TensorFlow is tested and supported on the following 64-bit systems:</p> | ||
<table class="columns"> | ||
<tr><td> | ||
<ul> | ||
<li>Ubuntu 16.04 or later</li> | ||
<li>Windows 7 or later</li> | ||
</ul> | ||
</td><td> | ||
<ul> | ||
<li>macOS 10.12.6 (Sierra) or later (no GPU support)</li> | ||
<li>Raspbian 9.0 or later</li> | ||
</ul> | ||
</td></tr> | ||
</table> | ||
|
||
- classname: > | ||
devsite-landing-row-large-headings | ||
devsite-landing-row-50 | ||
items: | ||
- heading: Download a package | ||
description: > | ||
<p>Install TensorFlow with Python's <em>pip</em> package manager.</p> | ||
<p>Official packages available for Ubuntu, Windows, macOS, and the Raspberry Pi.</p> | ||
<p>See the <a href="./gpu">GPU guide</a> for CUDA®-enabled cards.</p> | ||
|
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buttons: | ||
- label: Read the pip install guide | ||
path: /install/pip | ||
classname: button button-primary | ||
|
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code_block: | | ||
<pre class="prettyprint lang-bsh devsite-disable-click-to-copy"> | ||
# Current stable release for CPU-only | ||
<code class="devsite-terminal">pip install tensorflow</code><br/> | ||
# Preview nightly build for CPU-only (unstable) | ||
<code class="devsite-terminal">pip install tf-nightly</code><br/> | ||
# Install TensorFlow 2.0 Alpha | ||
<code class="devsite-terminal">pip install tensorflow==2.0.0-alpha0</code> | ||
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- classname: | ||
devsite-landing-row-100 | ||
devsite-landing-row-large-headings | ||
items: | ||
- heading: Run a TensorFlow container | ||
description: > | ||
<p> | ||
The <a href="https://hub.docker.com/r/tensorflow/tensorflow/" class="external">TensorFlow | ||
Docker images</a> are already configured to run TensorFlow. A | ||
<a href="https://docs.docker.com/install/" class="external">Docker</a> container runs in a | ||
virtual environment and is the easiest way to set up <a href="/install/gpu">GPU | ||
support</a>. | ||
</p> | ||
<pre class="prettyprint lang-bsh"> | ||
<code class="devsite-terminal">docker pull tensorflow/tensorflow # Download latest image</code><br/> | ||
<code class="devsite-terminal">docker run -it -p 8888:8888 tensorflow/tensorflow # Start a Jupyter notebook server</code> | ||
</pre> | ||
buttons: | ||
- label: Read the Docker install guide | ||
path: /install/docker | ||
classname: button button-primary | ||
|
||
- classname: | ||
devsite-landing-row-large-headings | ||
devsite-landing-row-100 | ||
items: | ||
- heading: Google Colab: An easy way to learn and use TensorFlow | ||
description: > | ||
<p> | ||
No install necessary—run the <a href="/tutorials">TensorFlow tutorials</a> directly in | ||
the browser with <a href="https://colab.research.google.com/notebooks/welcome.ipynb" | ||
class="external">Colaboratory</a>, a Google research project created to help disseminate | ||
machine learning education and research. It's a Jupyter notebook environment that requires | ||
no setup to use and runs entirely in the cloud. | ||
<a href="https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309" class="external">Read the blog post</a>. | ||
</p> | ||
|
||
- background: grey | ||
heading: Build your first ML app | ||
description: Create and deploy TensorFlow models on web and mobile. | ||
items: | ||
- classname: tfo-landing-row-item-inset-white | ||
heading: Web developers | ||
path: https://js.tensorflow.org | ||
description: > | ||
TensorFlow.js is a WebGL accelerated, JavaScript library to train and | ||
deploy ML models in the browser and for Node.js. | ||
- classname: tfo-landing-row-item-inset-white | ||
heading: Mobile developers | ||
path: /lite/ | ||
description: > | ||
TensorFlow Lite is a lightweight solution for mobile and embedded devices. |
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toc: | ||
- title: TensorFlowのインストール | ||
path: /install/ | ||
- heading: パッケージ | ||
- title: pip | ||
path: /install/pip | ||
- title: Docker | ||
path: /install/docker | ||
- heading: 追加のセットアップ | ||
- title: GPUサポート | ||
path: /install/gpu | ||
- title: 問題 | ||
path: /install/errors | ||
- heading: ソースからのビルド | ||
- title: Linux / macOS | ||
path: /install/source | ||
- title: Windows | ||
path: /install/source_windows | ||
- title: Raspberry Pi | ||
path: /install/source_rpi | ||
- heading: 言語バインディング | ||
- title: Java | ||
path: /install/lang_java | ||
- title: C | ||
path: /install/lang_c | ||
- title: Go | ||
path: /install/lang_go |
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# Docker | ||
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||
[Docker](https://docs.docker.com/install/){:.external}は、*container*を使用して、 | ||
TensorFlowのインストールをシステムの他の部分から分離された仮想環境を作成します。 | ||
TensorFlowのプログラムは、この*仮想環境内*で実行され、ホストマシンとリソースを共有できます。 | ||
(ディレクトリーへのアクセス、GPUの使用、インターネットへの接続など) | ||
[TensorFlowのDockerイメージ](https://hub.docker.com/r/tensorflow/tensorflow/){:.external} | ||
は各リリースでテストされています。 | ||
|
||
Dockerは、LinuxでTensorFlowの[GPUサポート](./gpu.md)を有効にする最も簡単な方法です。 | ||
*ホスト*マシンに必要なのは | ||
[NVIDIA® GPドライバー](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver){:.external}だけです。 | ||
(*NVIDIA® CUDA® Toolk*をインストールする必要はありません) | ||
|
||
|
||
## TensorFlow Dockerの必要条件 | ||
|
||
1. [Dockerのインストール](https://docs.docker.com/install/){:.external}をローカル*ホスト*マシンで実行してください | ||
2. LinuxでGPUサポートするには、[nvidia-dockerのインストール](https://github.com/NVIDIA/nvidia-docker){:.external}を実行してください | ||
|
||
注: `sudo`なしで`docker`コマンドを実行するには、`docker`グループを作成してあなたのユーザーを追加してください。 | ||
詳しくは | ||
[Linux用のインストール後の手順](https://docs.docker.com/install/linux/linux-postinstall/){:.external}を参照してください。 | ||
|
||
|
||
## TensorFlowのDockerイメージをダウンロードする | ||
|
||
公式のTensorFlowのDockerイメージは、 | ||
[tensorflow/tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/){:.external} | ||
Docker Hubリポジトリにあります。 | ||
イメージリリースは、次の形式で[タグ付け](https://hub.docker.com/r/tensorflow/tensorflow/tags/){:.external}されています: | ||
|
||
| タグ | 説明 | | ||
| --- | --- | | ||
| `latest` | 最新リリースのTensorFlowのCPUバイナリイメージ。デフォルト。 | | ||
| `nightly` | TensorFlowのnightlyビルドのイメージ。(不安定) | | ||
| *`version`* | TensorFlowのバイナリイメージの*version*を指定する。例: *1.12.0* | | ||
| `devel` | TensorFlowのnightlyビルドの`master`開発環境。TensorFlowのソースコードを含む。 | | ||
|
||
各ベーズ*tag*は、機能を追加または変更する亜種があります: | ||
|
||
| タグ亜種 | 説明 | | ||
| --- | --- | | ||
| *`tag`*`-gpu` | GPUサポートの指定された*tag*リリース。 ([以下参照](#gpu_support)) | | ||
| *`tag`*`-py3` | Python3サポートの指定された*tag*リリース。 | | ||
| *`tag`*`-jupyter` | Jupyter付きの指定された*tag*リリース (TensorFlowのチュートリアルnotebooks含む) | | ||
|
||
一度に複数の亜種を使用できます。例えば、 | ||
以下はTensorFlowリリースイメージをご使用のマシンにダウンロードします: | ||
|
||
<pre class="devsite-click-to-copy prettyprint lang-bsh"> | ||
<code class="devsite-terminal">docker pull tensorflow/tensorflow # latest stable release</code> | ||
<code class="devsite-terminal">docker pull tensorflow/tensorflow:devel-gpu # nightly dev release w/ GPU support</code> | ||
<code class="devsite-terminal">docker pull tensorflow/tensorflow:latest-gpu-jupyter # latest release w/ GPU support and Jupyter</code> | ||
</pre> | ||
|
||
|
||
## TensorFlowのDockerコンテナを起動する | ||
|
||
TensorFlowの設定コンテナを起動するには、次のコマンド形式を使用します: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy"> | ||
docker run [-it] [--rm] [-p <em>hostPort</em>:<em>containerPort</em>] tensorflow/tensorflow[:<em>tag</em>] [<em>command</em>] | ||
</pre> | ||
|
||
詳細については、[docker run reference](https://docs.docker.com/engine/reference/run/){:.external}を参照してください。 | ||
|
||
### CPUのみのイメージを使った例 | ||
|
||
`latest`タグ付きイメージを使ってTensorFlowのインストールを確認しましょう。 | ||
Dockerは、初めて実行する際に新しいTensorFlowのイメージをダウンロードします: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy prettyprint lang-bsh"> | ||
docker run -it --rm tensorflow/tensorflow \ | ||
python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))" | ||
</pre> | ||
|
||
成功: TensorFlowがインストールできました。[チュートリアル](../tutorials)を読んで始めましょう。 | ||
|
||
もう少しTensorFlowのDockerレシピをデモをしましょう。 | ||
TensorFlowで設定されたコンテナ内で `bash`シェルセッションを開始します: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy"> | ||
docker run -it tensorflow/tensorflow bash | ||
</pre> | ||
|
||
コンテナ内で、`python`セッションを開始してTensorFlowをインポートすることができます。 | ||
|
||
コンテナ内の*ホスト*マシン上で開発されたTensorFlowプログラムを実行するには、 | ||
ホストディレクトリをマウントしてコンテナの作業ディレクトリを変更します。 | ||
(`-v hostDir:containerDir -w workDir`): | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy prettyprint lang-bsh"> | ||
docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/tensorflow python ./script.py | ||
</pre> | ||
|
||
コンテナ内で作成されたファイルがホストに公開されると、アクセス権限の問題が発生する可能性があります。 | ||
通常はホストシステム上のファイルを編集するのがベストです。 | ||
|
||
Python3サポート付きのTensorFlowのnightlyビルドを使用した | ||
[Jupyter Notebook](https://jupyter.org/){:.external}サーバーを起動してください: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy"> | ||
docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter | ||
</pre> | ||
|
||
手順に従い、ご使用のホストのWebブラウザ内で以下のURLを開きます: | ||
`http://127.0.0.1:8888/?token=...` | ||
|
||
|
||
## GPUサポート | ||
|
||
Dockerは、GPUでTensorFlowを実行する最も簡単な方法です。 | ||
*ホスト*マシンに必要なのは | ||
[NVIDIA® GPUドライバー](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver){:.external}だけです。 | ||
(*NVIDIA® CUDA® Toolk*をインストールする必要はありません) | ||
|
||
NVIDIA® GPUをサポートするDockerコンテナを起動するには、 | ||
[nvidia-docker](https://github.com/NVIDIA/nvidia-docker){:.external}をインストールしてください。 | ||
`nvidia-docker`はLinuxでのみ利用可能です。詳細は、 | ||
[プラットフォームサポートFAQ](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#platform-support){:.external} | ||
を参照してください。 | ||
|
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GPUが利用可能か確認します: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy"> | ||
lspci | grep -i nvidia | ||
</pre> | ||
|
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`nvidia-docker`がインストールされたことを確認してください: | ||
|
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<pre class="devsite-terminal devsite-click-to-copy"> | ||
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi | ||
</pre> | ||
|
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注: `nvidia-docker` v1は`nvidia-docker`エイリアスを使います。v2は `docker --runtime=nvidia`を使用します。 | ||
|
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### GPUが有効なイメージを使った例 | ||
|
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GPU対応のTensorFlowのイメージをダウンロードして実行します(数分かかる場合があります)。 | ||
|
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<pre class="devsite-terminal devsite-click-to-copy prettyprint lang-bsh"> | ||
docker run --runtime=nvidia -it --rm tensorflow/tensorflow:latest-gpu \ | ||
python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))" | ||
</pre> | ||
|
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GPU対応のイメージの設定にはしばらく時間がかかる場合があります。 | ||
GPUベースのスクリプトを繰り返し実行する場合は、`docker exec`を使ってコンテナを再利用できます。 | ||
|
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コンテナ内で`bash`シェルセッションを開始するために、最新のTensorFlowのGPUイメージを使用してください: | ||
|
||
<pre class="devsite-terminal devsite-click-to-copy"> | ||
docker run --runtime=nvidia -it tensorflow/tensorflow:latest-gpu bash | ||
</pre> | ||
|
||
成功: TensorFlowがインストールできました。[チュートリアル](../tutorials)を読んで始めましょう。 |
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「...レシピを使ったデモをしましょう」 will be better.
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Thanks for your comment, mattn. Which of the following sentences is better?
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Original sentense use
demonstrate
so I prefer the second. (but this is just my optinion. Using "を" twice bothers me) :)There was a problem hiding this comment.
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Got it!