tensorflow bazel安装
- 作者: 像个蝴蝶
- 来源: 51数据库
- 2020-10-05
普通电脑PC怎样跑TensorFlow的GPU模式
在Mac电脑上面编译GPU版本TensorFlow的方法
基本使用
使用 TensorFlow, 你必须明白 TensorFlow:
使用图 (graph) 来表示计算任务.
在被称之为 会话 (Session) 的上下文 (context) 中执行图.
使用 tensor 表示数据.
通过 变量 (Variable) 维护状态.
使用 feed 和 fetch 可以为任意的操作(arbitrary operation) 赋值或者从其中获取数据.
1、先查看是否是带cuda的gpu，检测方法：https://developer.nvidia.com/cuda-gpus，下载软件进行测试。
2、如果测试带cuda的gpu，并且计算能力大于3.5，就可以使用了。
首先需要看你的PC配置是否够，TF的GPU模式只支持N卡，然后计算能力高于3.0，具体可以查：
安装教程可以参考：
Ubuntu16.04上gtx1080的cuda安装
July 17 2016
目前tensorflow是一个非常流行的深度学习计算框架，常规硬件及系统的安装方法官方的doc已经说的很清楚了，但是 因为系统是ubuntu16.04,显卡是GTX1080,所以不可避免的要折腾起来。在上一篇已经在16.04上安装好了驱动。接下来其实 重点安装的是CUDA和cuDNN.
首先说为什么要安装CUDA和cuDNN，关于采用GPU计算比CPU有速度有多少提升的benchmark找找就有，这次重点是怎么让tensorflow充分用的 上GTX1080能力。具体的就是如何把支持GTX1080的CUDA和cuDNN装起来，然后让tensorflow认识我们新装的CUDA和cuDNN。
首先总体说下安装步骤：
1 首先要注册NVIDIA developer的帐号，分别下载CUDA和cuDNN
2 确认准备gcc版本，安装依赖库sudo apt-get install freegl
3 安装CUDA
4 解压cuDNN
5 clone tensorflow源码，configure配置
6 编译安装
7 最后一哆嗦,测试！
准备工作
在正式开始前，需要做几个准备工作，主要是大概先看下文档
cuda FAQ
tensorflow 的安装文档
cuda-gpu的支持列表/计算能力/FAQ
cudnn 5.1有多牛
cuda tookit下载页面
CUDA_Installation_Guide_Linux.pdf
cudnn User Guide
文档看过之后接下来就是实际动手的过程：
1 注册NVIDIA developer的帐号，分别下载CUDA和cuDNN
1.1 下载CUDA 打开cuda toolkit下载页面,GTX1080 要用的是CUDA 8。先点击JOIN,注册帐号。 完了后，再回到cuda toolkit下载页面。选择 linux, x86-64, ubuntu, 16.04, runfile(local)
1.2 下载cuDNN 进入cudnn的下载页,一堆调查,日志写时下载的是[Download cuDNN v5 (May 27, 2016), for CUDA 8.0 RC],点开选linux,不出意外的话这个就是下载地址.
2 确认GCC版本，安装依赖库
确认本机gcc版本,16.04默认的是gcc 5,这里安装需要的最高是gcc 4.9。接下来就安装配置gcc 4.9.
2.1 安装gcc 4.9，并修改系统默认为4.9
sudo apt-get install gcc-4.9 gcc-4.9 g++-4.9 g++-4.9
gcc --version
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 10
sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
sudo update-alternatives --set cc /usr/bin/gcc
sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
sudo update-alternatives --set c++ /usr/bin/g++
gcc --version
2.2 一个小依赖
sudo apt-get install freegl
3 安装CUDA
需要注意的是这个地方有个选择安装低版本驱动的地方，选n 大致的安装流程如下：
3.1 安装CUDA
chmod ?+x /cuda_8.0.27_linux.run
./cuda_8.0.27_linux.run
....
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/h ]: /home/h/Documents/cuda_samples
....
3.2 写入环境变量
vim ~/.bashrc
#添加下面变量
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
3.3 安装好后简单验证
a. 进入刚配置时指定的cuda sample所在文件夹,NVIDIA_CUDA-8.0_Samples/
b. cd 0_Simple/asyncAPI;sudo make
c. NVIDIA_CUDA-8.0_Samples/0_Simple/asyncAPI$ ./asyncAPI [./asyncAPI] - Starting… GPU Device 0: 逗GeForce GTX 1080地 with compute capability 6.1 CUDA device [GeForce GTX 1080] time spent executing by the GPU: 10.94 time spent by CPU in CUDA calls: 0.19 CPU executed 50591 iterations while waiting for GPU to finish
4 安装cuDNN
h@h:~/Downloads$ tar xvzf cudnn-8.0-linux-x64-v5.0-ga.tgz
cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.5
cuda/lib64/libcudnn.so.5.0.5
cuda/lib64/libcudnn_static.a
h@h:~/Downloads$ sudo cp -R cuda/lib64 /usr/local/cuda/lib64
h@h:~/Downloads$ sudo mkdir -p /usr/local/cuda/include
h@h:~/Downloads/cuda$ sudo cp include/cudnn.h /usr/local/cuda/include/
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
5 clone, configure tensorflow
5.1 clone源码
$ git clone
5.2 configure配置
整个配置流程应该跟下面的基本一样的
h@h:~/Downloads/tensorflow$ cd ./tensorflow/
h@h:~/Downloads/tensorflow$ ./configure
Please specify the location of python. [Default is /usr/bin/python]:
***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 GPU support? [y/N] y***
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:
**Please specify the location where CUDA ?toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-8.0 **
**Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.0.5**
**Please specify the location where cuDNN 5.0.5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-8.0]: /usr/local/cuda**
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at:
**Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]: 6.1**
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
6 编译安装
6.1 编译工具Bazel安装配置?
先看一眼文档?然后就执行下面的流程:
#安装java 1.8
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer
#安装好后车参考下
java -version
#添加源
echo "deb [arch=amd64] stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl | sudo apt-key add -
#下载
sudo apt-get update && sudo apt-get install bazel
#升级
sudo apt-get upgrade bazel
6.2 编译tensorflow的pip版本并安装
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# The name of the .whl file will depend on your platform.
#注意编译完成后生成的文件名字和官方doc里面的是不一定一致的
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.*-linux_x86_64.whl
i6700k 32g编译时间:
只编译代码不带pip INFO: Elapsed time: 967.271s, Critical Path: 538.38s
bazel-bin/tensorflow/tools/pip_package/build_pip_package INFO: Elapsed time: 65.183s, Critical Path: 48.58
7 最后测试
前面都整完了，现在该测试了，注意前面有两个动态链接库的位置，cuDNN在/usr/local/cuda/lib64， 而cuda在/usr/local/cuda-8.0/lib64，所以这个时候的bashrc应该这么写：
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
写完后,
source ~/.bashrc
cd tensorflow/tensorflow/models/image/mnist
python convolutional.py
成功的话会出现流畅的跑动:
h@h:~/Downloads/tensorflow/tensorflow/models/image/mnist$ python convolutional.py
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so.5.0.5 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8475
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.41GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: ? Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
Initialized!
Step 0 (epoch 0.00), 8.4 ms
Minibatch loss: 12.054, learning rate: 0.010000
Minibatch error: 90.6%
Validation error: 84.6%
......
Minibatch error: 0.0%
Validation error: 0.7%
Step 8500 (epoch 9.89), 4.7 ms
Minibatch loss: 1.601, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Test error: 0.8%
在Mac电脑上面编译GPU版本TensorFlow的方法
基本使用
使用 TensorFlow, 你必须明白 TensorFlow:
使用图 (graph) 来表示计算任务.
在被称之为 会话 (Session) 的上下文 (context) 中执行图.
使用 tensor 表示数据.
通过 变量 (Variable) 维护状态.
使用 feed 和 fetch 可以为任意的操作(arbitrary operation) 赋值或者从其中获取数据.
1、先查看是否是带cuda的gpu，检测方法：https://developer.nvidia.com/cuda-gpus，下载软件进行测试。
2、如果测试带cuda的gpu，并且计算能力大于3.5，就可以使用了。
首先需要看你的PC配置是否够，TF的GPU模式只支持N卡，然后计算能力高于3.0，具体可以查：
安装教程可以参考：
Ubuntu16.04上gtx1080的cuda安装
July 17 2016
目前tensorflow是一个非常流行的深度学习计算框架，常规硬件及系统的安装方法官方的doc已经说的很清楚了，但是 因为系统是ubuntu16.04,显卡是GTX1080,所以不可避免的要折腾起来。在上一篇已经在16.04上安装好了驱动。接下来其实 重点安装的是CUDA和cuDNN.
首先说为什么要安装CUDA和cuDNN，关于采用GPU计算比CPU有速度有多少提升的benchmark找找就有，这次重点是怎么让tensorflow充分用的 上GTX1080能力。具体的就是如何把支持GTX1080的CUDA和cuDNN装起来，然后让tensorflow认识我们新装的CUDA和cuDNN。
首先总体说下安装步骤：
1 首先要注册NVIDIA developer的帐号，分别下载CUDA和cuDNN
2 确认准备gcc版本，安装依赖库sudo apt-get install freegl
3 安装CUDA
4 解压cuDNN
5 clone tensorflow源码，configure配置
6 编译安装
7 最后一哆嗦,测试！
准备工作
在正式开始前，需要做几个准备工作，主要是大概先看下文档
cuda FAQ
tensorflow 的安装文档
cuda-gpu的支持列表/计算能力/FAQ
cudnn 5.1有多牛
cuda tookit下载页面
CUDA_Installation_Guide_Linux.pdf
cudnn User Guide
文档看过之后接下来就是实际动手的过程：
1 注册NVIDIA developer的帐号，分别下载CUDA和cuDNN
1.1 下载CUDA 打开cuda toolkit下载页面,GTX1080 要用的是CUDA 8。先点击JOIN,注册帐号。 完了后，再回到cuda toolkit下载页面。选择 linux, x86-64, ubuntu, 16.04, runfile(local)
1.2 下载cuDNN 进入cudnn的下载页,一堆调查,日志写时下载的是[Download cuDNN v5 (May 27, 2016), for CUDA 8.0 RC],点开选linux,不出意外的话这个就是下载地址.
2 确认GCC版本，安装依赖库
确认本机gcc版本,16.04默认的是gcc 5,这里安装需要的最高是gcc 4.9。接下来就安装配置gcc 4.9.
2.1 安装gcc 4.9，并修改系统默认为4.9
sudo apt-get install gcc-4.9 gcc-4.9 g++-4.9 g++-4.9
gcc --version
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 10
sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30
sudo update-alternatives --set cc /usr/bin/gcc
sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30
sudo update-alternatives --set c++ /usr/bin/g++
gcc --version
2.2 一个小依赖
sudo apt-get install freegl
3 安装CUDA
需要注意的是这个地方有个选择安装低版本驱动的地方，选n 大致的安装流程如下：
3.1 安装CUDA
chmod ?+x /cuda_8.0.27_linux.run
./cuda_8.0.27_linux.run
....
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/h ]: /home/h/Documents/cuda_samples
....
3.2 写入环境变量
vim ~/.bashrc
#添加下面变量
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
3.3 安装好后简单验证
a. 进入刚配置时指定的cuda sample所在文件夹,NVIDIA_CUDA-8.0_Samples/
b. cd 0_Simple/asyncAPI;sudo make
c. NVIDIA_CUDA-8.0_Samples/0_Simple/asyncAPI$ ./asyncAPI [./asyncAPI] - Starting… GPU Device 0: 逗GeForce GTX 1080地 with compute capability 6.1 CUDA device [GeForce GTX 1080] time spent executing by the GPU: 10.94 time spent by CPU in CUDA calls: 0.19 CPU executed 50591 iterations while waiting for GPU to finish
4 安装cuDNN
h@h:~/Downloads$ tar xvzf cudnn-8.0-linux-x64-v5.0-ga.tgz
cuda/include/cudnn.h
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.5
cuda/lib64/libcudnn.so.5.0.5
cuda/lib64/libcudnn_static.a
h@h:~/Downloads$ sudo cp -R cuda/lib64 /usr/local/cuda/lib64
h@h:~/Downloads$ sudo mkdir -p /usr/local/cuda/include
h@h:~/Downloads/cuda$ sudo cp include/cudnn.h /usr/local/cuda/include/
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
5 clone, configure tensorflow
5.1 clone源码
$ git clone
5.2 configure配置
整个配置流程应该跟下面的基本一样的
h@h:~/Downloads/tensorflow$ cd ./tensorflow/
h@h:~/Downloads/tensorflow$ ./configure
Please specify the location of python. [Default is /usr/bin/python]:
***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 GPU support? [y/N] y***
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:
**Please specify the location where CUDA ?toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-8.0 **
**Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.0.5**
**Please specify the location where cuDNN 5.0.5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-8.0]: /usr/local/cuda**
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at:
**Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]: 6.1**
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
6 编译安装
6.1 编译工具Bazel安装配置?
先看一眼文档?然后就执行下面的流程:
#安装java 1.8
sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer
#安装好后车参考下
java -version
#添加源
echo "deb [arch=amd64] stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl | sudo apt-key add -
#下载
sudo apt-get update && sudo apt-get install bazel
#升级
sudo apt-get upgrade bazel
6.2 编译tensorflow的pip版本并安装
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# The name of the .whl file will depend on your platform.
#注意编译完成后生成的文件名字和官方doc里面的是不一定一致的
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.*-linux_x86_64.whl
i6700k 32g编译时间:
只编译代码不带pip INFO: Elapsed time: 967.271s, Critical Path: 538.38s
bazel-bin/tensorflow/tools/pip_package/build_pip_package INFO: Elapsed time: 65.183s, Critical Path: 48.58
7 最后测试
前面都整完了，现在该测试了，注意前面有两个动态链接库的位置，cuDNN在/usr/local/cuda/lib64， 而cuda在/usr/local/cuda-8.0/lib64，所以这个时候的bashrc应该这么写：
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
写完后,
source ~/.bashrc
cd tensorflow/tensorflow/models/image/mnist
python convolutional.py
成功的话会出现流畅的跑动:
h@h:~/Downloads/tensorflow/tensorflow/models/image/mnist$ python convolutional.py
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so.5.0.5 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8475
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.41GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: ? Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
Initialized!
Step 0 (epoch 0.00), 8.4 ms
Minibatch loss: 12.054, learning rate: 0.010000
Minibatch error: 90.6%
Validation error: 84.6%
......
Minibatch error: 0.0%
Validation error: 0.7%
Step 8500 (epoch 9.89), 4.7 ms
Minibatch loss: 1.601, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Test error: 0.8%
推荐阅读
