接上文《深度學習主機環境配置: Ubuntu16.04+Nvidia GTX 1080+CUDA8.0》，我們繼續來安裝 TensorFlow，使其支持GeForce GTX 1080顯卡。
1 下載和安裝cuDNN
cuDNN全稱 CUDA Deep Neural Network library，是NVIDIA專門針對深度神經網絡設計的一套GPU計算加速庫，被廣泛用於各種深度學習框架，例如Caffe, TensorFlow, Theano, Torch, CNTK等。
The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.
Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe, TensorFlow, Theano, Torch, and CNTK. See supported frameworks for more details.
首先需要下載cuDNN，直接從Nvidia官方下載鏈接選擇一個版本，不過下載cuDNN前同樣需要登錄甚至填寫一個簡單的調查問卷: https://developer.nvidia.com/rdp/cudnn-download，這里選擇的是支持CUDA8.0的cuDNN v5版本，而支持CUDA8的5.1版本雖然顯示在下載選擇項里，但是提示：cuDNN 5.1 RC for CUDA 8RC will be available soon - please check back again.
安裝cuDNN比較簡單，解壓后把相應的文件拷貝到對應的CUDA目錄下即可：
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
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
2 通過源代碼方式編譯安裝TensorFlow GPU版本
TensorFlow的CPU版本安裝比較簡單，在Ubuntu 環境下通過PIP方式安裝即可，具體請參考TensorFlow官方安裝文檔。這里通過源代碼編譯安裝TensorFlow 0.9版本，使其支持相應的GPU：GTX1080。
1) Python相關環境准備
這里使用的是Python2.7版本，在Ubuntu16.04下安裝相關依賴:
sudo apt-get install python-pip
sudo apt-get install python-numpy swig python-dev python-wheel
2）安裝Google構建工具Bazel
Bazel是Google官方開源的一個構建工具，用來配合Google的軟件開發模式，有以下幾個特點：
多語言支持：Bazel支持Java，Objective-C和C++，可以擴展來支持任意的編程語言
高級別的構建語言：工程是通過BUILD語言來描述的。BUILD語言以簡潔的文本格式，描述了由多個小的互相關聯的庫、二進制程序和測試程序來組成的一個項目。而與之相比，Make這類的工具需要描述各個單獨的文件和編譯的命令
多平台支持：同一套工具和同樣的BUILD文件可以用來構建不同架構和不同平台的軟件。在Google，我們使用Bazel來構建在我們數據中心系統中運行的服務器端程序和在手機上運行的客戶端應用程序。
重現性[Reproducibility]：在BUILD文件中，每個庫，測試程序，二進制文件必須明確完整地指定直接依賴。當修改源代碼文件后，Bazel使用這個依賴信息就可以知道哪些必須重新構建，哪些任務可以並行執行。這意味者所有的構建都是增量形式的並能夠每次都生成相同的結果。
伸縮性[Scalability]：Bazel可以處理巨大的構建；在Google，一個服務器端程序超過100k的源碼是常有的事情，如果沒有文件被改動，構建過程需要大約200ms
從Bazel github上最新的Linux relase版本：
下載完畢后執行：
./bazel-0.3.0-installer-linux-x86_64.sh --user
提示錯誤：
Java not found, please install the corresponding package
See http://bazel.io/docs/install.html for more information on
應該是沒有安裝Java環境的問題，bazel需要Java JDK 8，在ubuntu16.04直接apt-get安裝即可：
sudo apt-get install default-jre
sudo apt-get install default-jdk
安裝完畢后，再次執行Bazel安裝腳本：
Bazel installer
---------------# Release 0.3.0 (2016-06-10)
Baseline: a9301fa
Cherry picks:
+ ff30a73: Turn --legacy_external_runfiles back on by default
+ aeee3b8: Fix delete[] warning on fsevents.ccIncompatible changes:
- The --cwarn command line option is not supported anymore. Use
--copt instead.New features:
- On OSX, --watchfs now uses FsEvents to be notified of changes
from the filesystem (previously, this flag had no effect on OS X).
- add support for the '-=', '*=', '/=', and'%=' operators to
skylark. Notably, we do not support '|=' because the semantics
of skylark sets are sufficiently different from python sets.Important changes:
- Use singular form when appropriate in blaze's test result summary
message.
- Added supported for Android NDK revision 11
- --objc_generate_debug_symbols is now deprecated.
- swift_library now generates an Objective-C header for its @objc
interfaces.
- new_objc_provider can now set the USES_SWIFT flag.
- objc_framework now supports dynamic frameworks.
- Symlinks in zip files are now unzipped correctly by http_archive,
download_and_extract, etc.
- swift_library is now able to import framework rules such as
objc_framework.
- Adds "jre_deps" attribute to j2objc_library.
- Release apple_binary rule, for creating multi-architecture
("fat") objc/cc binaries and libraries, targeting ios platforms.
- Aspects documentation added.
- The --ues_isystem_for_includes command line option is not
supported anymore.
- global function 'provider' is removed from .bzl files. Providers
can only be accessed through fields in a 'target' object.## Build informations
- [Build log](http://ci.bazel.io/job/Bazel/JAVA_VERSION=1.8,PLATFORM_NAME=linux-x86_64/595/)
- [Commit](https://github.com/bazelbuild/bazel/commit/e671d29)
Uncompressing......Extracting Bazel installation...
.Bazel is now installed!
Make sure you have "/home/textminer/bin" in your path. You can also activate bash
completion by adding the following line to your ~/.bashrc:
source /home/textminer/.bazel/bin/bazel-complete.bashSee http://bazel.io/docs/getting-started.html to start a new project!
然后在 ~/.bashrc中追加：
source /home/textminer/.bazel/bin/bazel-complete.bash
export PATH=$PATH:/home/textminer/.bazel/bin
追加的第一行的原因在這里：
Bazel comes with a bash completion script. To install it:
Build it with Bazel: bazel build //scripts:bazel-complete.bash.
Copy the script bazel-bin/scripts/bazel-complete.bash to your completion folder (/etc/bash_completion.d directory under Ubuntu). If you don't have a completion folder, you can copy it wherever suits you and simply insert source /path/to/bazel-complete.bash in your ~/.bashrc file (under OS X, put it in your ~/.bash_profile file).
最后執行
至此，Bazel安裝完畢。
3） 編譯安裝TensorFlow:
首先從github上克隆TensorFlow最新的代碼：
代碼下載完畢之后，進入tensorflow主目錄，執行：
會有一系列提示：
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] y
Google Cloud Platform support will be enabled for TensorFlow
ERROR: It appears that the development version of libcurl is not available. Please install the libcurl3-dev package.
第二項"是否選擇Google雲平台的支持"選擇y之后出現了一個錯誤，需要libcurl，用apt-get安裝，當然，基於國內的網絡現狀，這一項也可以選擇no:
安裝完畢之后重新執行
除了兩處選擇yes or no 的地方外，其他地方一路回車:
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] y
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 Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:
Please specify the location where CUDA toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /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: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]:
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
最后就是通過Bazel進行編譯安裝了:
這個過程中需要通過git下載和編譯google protobuf 和 boringssl:
INFO: Cloning https://github.com/google/protobuf: Receiving objects
INFO: Cloning https://github.com/google/boringssl.git: Receiving objects
....
不過第一次安裝的時候遇到報錯：
configure: error: zlib not installed
Target //tensorflow/cc:tutorials_example_trainer failed to build
google了一下，需要安裝zlib1g-dev:
之后重新編譯安裝TensorFlow就沒有問題了，不過需要等待一段時間：
編譯TensorFlow成功結束的時候，提示如下：
......
Target //tensorflow/cc:tutorials_example_trainer up-to-date:
bazel-bin/tensorflow/cc/tutorials_example_trainer
INFO: Elapsed time: 897.845s, Critical Path: 533.72s
執行一下TensorFlow官方文檔里的例子，看看能否成功調用GTX 1080：
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 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
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) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.65GiB
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
000003/000006 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000006/000007 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000009/000006 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000009/000004 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000000/000005 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
000000/000004 lambda = 1.841570 x = [0.669396 0.742906] y = [3.493999 -0.669396]
......
沒有問題，說明這種通過源代碼編譯TensorFlow使其支持GPU的方式已經成功了。再在Python中調用一下TensorFlow:
提示錯誤：
ImportError: cannot import name pywrap_tensorflow
雖然我們通過源代碼安裝編譯的TensorFlow可用，但是Python版本並沒有ready，所以繼續：
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
sudo pip install /tmp/tensorflow_pkg/tensorflow-0.9.0-py2-none-any.whl
Requirement already satisfied (use --upgrade to upgrade): setuptools in /usr/lib/python2.7/dist-packages (from protobuf==3.0.0b2->tensorflow==0.9.0)
Installing collected packages: six, funcsigs, pbr, mock, protobuf, tensorflow
Successfully installed funcsigs-1.0.2 mock-2.0.0 pbr-1.10.0 protobuf-3.0.0b2 six-1.10.0 tensorflow-0.9.0
我們再次打開ipython，試一下tensorflow官方樣例:
Type "copyright", "credits" or "license" for more information.
IPython 2.4.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import tensorflow as tf
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 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
In [2]: import numpy as np
In [3]: x_data = np.random.rand(100).astype(np.float32)
In [4]: y_data = x_data * 0.1 + 0.3
In [5]: W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
In [6]: b = tf.Variable(tf.zeros([1]))
In [7]: y = W * x_data + b
In [8]: loss = tf.reduce_mean(tf.square(y - y_data))
In [9]: optimizer = tf.train.GradientDescentOptimizer(0.5)
In [10]: train = optimizer.minimize(loss)
In [11]: init = tf.initialize_all_variables()
In [12]: sess = tf.Session()
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) 1.835
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.65GiB
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)
In [13]: sess.run(init)
In [14]: for step in range(201):
....: sess.run(train)
....: if step % 20 == 0:
....: print(step, sess.run(W), sess.run(b))
....:
(0, array([-0.10331395], dtype=float32), array([ 0.62236434], dtype=float32))
(20, array([ 0.03067014], dtype=float32), array([ 0.3403711], dtype=float32))
(40, array([ 0.08353967], dtype=float32), array([ 0.30958495], dtype=float32))
(60, array([ 0.09609199], dtype=float32), array([ 0.30227566], dtype=float32))
(80, array([ 0.09907217], dtype=float32), array([ 0.3005403], dtype=float32))
(100, array([ 0.09977971], dtype=float32), array([ 0.30012828], dtype=float32))
(120, array([ 0.0999477], dtype=float32), array([ 0.30003047], dtype=float32))
(140, array([ 0.0999876], dtype=float32), array([ 0.30000722], dtype=float32))
(160, array([ 0.09999706], dtype=float32), array([ 0.30000171], dtype=float32))
(180, array([ 0.09999929], dtype=float32), array([ 0.30000043], dtype=float32))
(200, array([ 0.09999985], dtype=float32), array([ 0.3000001], dtype=float32))
終於OK了，之后就可以盡情享用基於GTX 1080 GPU版的TensorFlow了。