1
1

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?

More than 5 years have passed since last update.

Build of TensorFlow

Last updated at Posted at 2016-10-09

TensorFlowの内部構造を知りたいと思ったのでまずはbuildしてみたというお話。基本的にここに書いてある通りに進めれば良いのだけど、protobufのところだけ例外があったのでメモしておく。

環境

  • Ubuntu 16.04 on VMware Workstation
    • 2 vCPUs
    • 8 GB Memory (4 GBだと途中でbuildがとまった..)
  • 以下、Python3前提

準備

TensorFlowのbuild toolにBazelを使っているのでインストールしておく。

$ sudo apt install openjdk-8-jdk
$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | \
sudo tee /etc/apt/sources.list.d/bazel.list
$ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | \
sudo apt-key add -
$ sudo apt install bazel
$ sudo apt install swig

numpyはpip3でinstall済みと仮定してある。python3-devとかpython3-wheelもapt install済み。

TensorFlowのBuild

git pull済みとする。

$ git log -1
commit 3d35376a66cde4f3e614c746d3c8708d15caa1b5
Merge: aaeb50c 23c1156
Author: Vincent Vanhoucke <vanhoucke@google.com>
Date:   Sun Oct 2 15:11:18 2016 -0700

    Merge pull request #4716 from ultraklon/patch-1

    small typo fix

$ ./configure
~/tensorflow ~/tensorflow
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] N
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] N
No Hadoop File System support will be enabled for TensorFlow
Found possible Python library paths:
  /usr/local/lib/python2.7/dist-packages
  /usr/lib/python2.7/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib/python2.7/dist-packages]
/usr/local/lib/python3.5/dist-packages
Do you wish to build TensorFlow with GPU support? [y/N] N
No GPU support will be enabled for TensorFlow
Configuration finished
Extracting Bazel installation...
.
INFO: Starting clean (this may take a while). Consider using --expunge_async if the clean takes more than several minutes.
.
INFO: All external dependencies fetched successfully.

$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package

Core i5-4570で大体30分程度かかった。次にdeployだが、pipのパッケージを作るのではなく、ここの記述に従って、今後あれこれしやすいようにする。

$ mkdir _python_build
$ cd _python_build
$ ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/* .
$ ln -s ../tensorflow/tools/pip_package/* .
$ sudo python3 setup.py develop
[sudo] password for ohara:
running develop
running egg_info
creating tensorflow.egg-info
writing dependency_links to tensorflow.egg-info/dependency_links.txt
writing entry points to tensorflow.egg-info/entry_points.txt
writing tensorflow.egg-info/PKG-INFO
writing top-level names to tensorflow.egg-info/top_level.txt
writing requirements to tensorflow.egg-info/requires.txt
writing manifest file 'tensorflow.egg-info/SOURCES.txt'
reading manifest file 'tensorflow.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensorflow.egg-info/SOURCES.txt'
running build_ext
Creating /usr/local/lib/python3.5/dist-packages/tensorflow.egg-link (link to .)
Adding tensorflow 0.10.0 to easy-install.pth file
Installing tensorboard script to /usr/local/bin

Installed /home/ohara/tensorflow/_python_build
Processing dependencies for tensorflow==0.10.0
Searching for protobuf==3.1.0
Reading https://pypi.python.org/simple/protobuf/
No local packages or working download links found for protobuf==3.1.0
error: Could not find suitable distribution for Requirement.parse('protobuf==3.1.0')

あ、はい、、ってなったのでprotobufをbuildする。

$ sudo apt-get install autoconf automake libtool curl make g++ unzip
$ git clone https://github.com/google/protobuf.git
$ cd protobuf
$ git checkout -B my-v3.1.0 v3.1.0
$ ./autogen.sh
$ CXXFLAGS="-fPIC -g -O2" ./configure
$ make
$ sudo pip3 uninstall protobuf
$ sudo pip3 install dist/protobuf-3.1.0-cp35-cp35m-linux_x86_64.whl

もういちどdeployしてみる。

$ sudo python3 setup.py develop
[sudo] password for ohara:
running develop
running egg_info
writing dependency_links to tensorflow.egg-info/dependency_links.txt
writing top-level names to tensorflow.egg-info/top_level.txt
writing entry points to tensorflow.egg-info/entry_points.txt
writing requirements to tensorflow.egg-info/requires.txt
writing tensorflow.egg-info/PKG-INFO
reading manifest file 'tensorflow.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'tensorflow.egg-info/SOURCES.txt'
running build_ext
Creating /usr/local/lib/python3.5/dist-packages/tensorflow.egg-link (link to .)
tensorflow 0.10.0 is already the active version in easy-install.pth
Installing tensorboard script to /usr/local/bin

Installed /home/ohara/tensorflow/_python_build
Processing dependencies for tensorflow==0.10.0
Searching for wheel==0.29.0
Best match: wheel 0.29.0
Adding wheel 0.29.0 to easy-install.pth file
Installing wheel script to /usr/local/bin

Using /usr/lib/python3/dist-packages
Searching for protobuf==3.1.0
Best match: protobuf 3.1.0
Adding protobuf 3.1.0 to easy-install.pth file

Using /usr/local/lib/python3.5/dist-packages
Searching for six==1.10.0
Best match: six 1.10.0
Adding six 1.10.0 to easy-install.pth file

Using /usr/lib/python3/dist-packages
Searching for numpy==1.11.1
Best match: numpy 1.11.1
Adding numpy 1.11.1 to easy-install.pth file

Using /usr/local/lib/python3.5/dist-packages
Searching for setuptools==28.2.0
Best match: setuptools 28.2.0
Adding setuptools 28.2.0 to easy-install.pth file
Installing easy_install-3.5 script to /usr/local/bin
Installing easy_install script to /usr/local/bin

Using /usr/local/lib/python3.5/dist-packages
Finished processing dependencies for tensorflow==0.10.0

今度は大丈夫そう。/usr/local/lib/python3.5/dist-packages/tensorflow.egg-linkから/home/ohara/tensorflow/_python_buildへのリンクがはられている。

$ pip3 list|grep tensorflow
tensorflow (0.10.0, /home/ohara/tensorflow/_python_build)

次はTensorFlowがLinear Algebra Libraryとして採用しているEigenを調べたい。TensorFlowはSMP CPUに対応しているのだけどそのScalability向上についてはEigenに強く依存している。

1
1
0

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
  3. You can use dark theme
What you can do with signing up
1
1

Delete article

Deleted articles cannot be recovered.

Draft of this article would be also deleted.

Are you sure you want to delete this article?