获取TensorFlow源代码
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git clone --recurse-submodules https:
//github
.com
/tensorflow/tensorflow
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使用 --recurse-submodules
选项来获取 TensorFlow 需要依赖的 protobuf 库文件。
安装 Bazel
遵从以下 指令 来安装 bazel 依赖。bazel 安装文件:下载地址
bazel 缺省需要使用JDK1.8,如你使用JDK1.7,请下载相应的安装包。
安装 Bazel 其他所需依赖:
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sudo
apt-get
install
pkg-config zip g++ zlib1g-dev unzip
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执行如下命令来安装Bazel:
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chmod
+x PATH_TO_INSTALL.SH
.
/PATH_TO_INSTALL
.SH --user
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记住把 PATH_TO_INSTALL.SH 替换为你下载的Bazel安装文件名,如:
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.
/bazel-0
.1.4-installer-linux-x86_64.sh --user
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安装其他依赖
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sudo
apt-get
install
python-numpy swig python-dev
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配置安装
运行 tensorflow 根目录下的 configure
脚本。这个脚本会要求你输入 python 解释器的安装路径,并允许你可选择安装CUDA库。
如果不安装CUDA,则这一步主要是定位python和numpy头文件所在位置:
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.
/configure
Please specify the location of python. [Default is
/usr/bin/python
]:
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如果要安装CUDA,则除了指定 python 外,还需指定 CUDA 安装位置:
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.
/configure
Please specify the location of python. [Default is
/usr/bin/python
]:
Do you wish to build TensorFlow with GPU support? [y
/N
] y
GPU support will be enabled
for
TensorFlow
Please specify the location where CUDA 7.0 toolkit is installed. Refer to
README.md
for
more
details. [default is:
/usr/local/cuda
]:
/usr/local/cuda
Please specify the location where the cuDNN v2 library is installed. Refer to
README.md
for
more
details. [default is:
/usr/local/cuda
]:
/usr/local/cuda
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished
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构建支持GPU的Tensorflow
在tensorflow 根目录下执行如下命令:
$ bazel build -c opt --config=cuda --spawn_strategy=standalone //tensorflow/cc:tutorials_example_trainer
$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
# Lots of output. This tutorial iteratively calculates the major eigenvalue of
# a 2x2 matrix, on GPU. The last few lines look like this.
000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
Note that "--config=cuda" is needed to enable the GPU support.