1c8b: 回顧與概述
這篇文章詳細介紹了Tensorflow的安裝和校驗安裝是否成功的教程，涵蓋了在Ubuntu 16.04環境下GPU支持安裝和非GPU支持的安裝以及校驗。
系統概覽：
- Ubuntu 16.04 64位
- NVIDIA GTX 770M
內容概覽：
- 根據NVIDIA官方文檔安裝CUDA-Toolkit
- 校驗CUDA安裝是否成功
- 根據Tensorflow官方文檔安裝Tensorflow（GPU支持）
- 校驗Tensorflow安裝是否成功
原版文檔很長，這篇文章給使用Ubuntu的朋友們提供一下便利！
1c8c: Tensorflow入門資源推薦
我的第一份Tensorflow入門，給大家介紹一下，是Youtube里周莫煩的Tensorflow基礎教程，翻墻點擊<a target="_blank">這里</a>帶你去看！
下面好評很多，很基礎，現在20集的樣子，大家可以訂閱，并且希望他能持續更新。
1c8d: Tensorflow安裝
首先請翻墻；
點擊進入Tensofrflow官網Linux安裝頁。進入頁面之后就會看到安裝選擇，可以安裝非GPU支持的TF（Tensorflow以下簡稱TF）和GPU支持的TF，我們這邊選擇安裝GPU支持的TF；
-
檢查系統軟件硬件是否符合NVIDIA要求；
完整的安裝前檢查如下：
- 檢查系統GPU是否處于CUDA支持列表
通過 lspci | grep -i nvidia 來查看系統GPU型號；如果沒有輸入，請先運行update-pciids，然后再次運行上一個命令；
并且到 CUDA支持的GPU列表 查看系統GPU是否處于支持列表；
- 檢查當前Linux版本處于CUDA支持列表
通過 uname -m && cat /etc/*release 來查看Linux版本；
</br>
<center>CUDA-8支持的Linux版本
</br>
Cuda-8-Supported Linux Dists
- 檢查系統安裝了gcc
通過 gcc --version 來查看系統是否安裝gcc；如果報錯，請安裝相應的開發工具包；
- 檢查系統是否安裝內核頭文件，以及必要的開發工具包；
通過 sudo apt-get install linux-headers-$(uname -r) 安裝；
其他Linux發行版本，安裝方法詳見CUDA安裝文檔 第2.4章節；
- 如果上述條件符合，轉到第4步，否則，轉到第8步安裝
非GPU支持的Tensorflow；
需要安裝CUDA-TOOLKIT來支持GPU。官方提供了NVIDIA的官方文檔；
下載NVIDIA最新的CUDA-TOOLKIT；在頁面最下方，以此點擊
Linux->x86_64->Ubuntu->16.04->deb(local); 之后點擊Download開始下載；-
下載完成之后，在終端執行以下命令，安裝CUDA：
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda -
安裝CUDA依賴庫：
sudo apt-get install libcupti-dev -
通過官方推薦的
virtualenv方式來安裝TF：完整安裝步驟如下：-
安裝pip，virtualenv，以及python開發包
sudo apt-get install python-pip python-dev python-virtualenv -
創建virtualenv環境
virtualenv --system-site-packages <tensorflow> 上述命令講創建系統默認python版本的虛擬環境 如果需要指定python版本，務必添加--python選項 virtualenv --system-site-packages --python=<path-to-python-executable> <tensorflow> -
激活虛擬環境
$ source ~/tensorflow/bin/activate # bash, sh, ksh, or zsh $ source ~/tensorflow/bin/activate.csh # csh or tcsh 使用如下相應的命令安裝
TF
(tensorflow)$ pip install --upgrade tensorflow # for Python 2.7 (tensorflow)$ pip3 install --upgrade tensorflow # for Python 3.n (tensorflow)$ pip install --upgrade tensorflow-gpu # for Python 2.7 and GPU (tensorflow)$ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU- 如果上面一步失敗，可能由于使用的pip版本低于了8.1，使用如下命令安裝
pip install --upgrade pip # 升級pip，然后重試上一步驟 如果有權限錯誤，請使用 sudo -H pip install --upgrade pip # 升級pip，然后重試上一步驟
-
至此，CUDA和TF都安裝完成；
1c8e: 校驗CUDA和TF安裝
-
校驗CUDA安裝
1) 重啟一下系統，讓NVIDIA GPU加載剛剛安裝的驅動，重啟完成之后運行cat /proc/driver/nvidia/version 如果有如下顯示，說明GPU驅動加載成功： NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.26 Thu Dec 8 18:36:43 PST 2016 GCC version: gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4)- 配置環境變量
# cuda env 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}} export LPATH=/usr/lib/nvidia-375:$LPATH export LIBRARY_PATH=/usr/lib/nvidia-375:$LIBRARY_PATH export CUDA_HOME=/usr/local/cuda-8.0務必配置這些環境變量，否則在接下來編譯sample的時候會遇到如下錯誤：
Makefile:346: recipe for target 'cudaDecodeGL' failed- 安裝CUDA樣例程序
cuda-install-samples-8.0.sh <dir> 該命令已經在系統環境變量中，直接使用，dir為自定義目錄； 執行完該命令之后，如果成功，會在dir中生成一個 NVIDIA_CUDA-8.0_Samples 目錄- 編譯樣例程序，校驗CUDA安裝
編譯之前首先保證第2)步中的環境變量設置無誤，并且第1)步中，GPU驅動版本顯示正常 進入 NVIDIA_CUDA-8.0_Samples 目錄，執行 make 編譯成功之后輸入如下： /usr/local/cuda-8.0/bin/nvcc -ccbin g++ -I../../common/inc -I../common/UtilNPP -I../common/FreeImage/include -m64 -gencode arch=compute_20,code=compute_20 -o jpegNPP.o -c jpegNPP.cpp nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). /usr/local/cuda-8.0/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_20,code=compute_20 -o jpegNPP jpegNPP.o -L../common/FreeImage/lib -L../common/FreeImage/lib/linux -L../common/FreeImage/lib/linux/x86_64 -lnppi -lnppc -lfreeimage nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). mkdir -p ../../bin/x86_64/linux/release cp jpegNPP ../../bin/x86_64/linux/release make[1]: Leaving directory '/home/yuanzimiao/Downloads/NVIDIA_CUDA-8.0_Samples/7_CUDALibraries/jpegNPP' Finished building CUDA samples 注：CUDA版本可以使用 nvcc -V 來查看- 運行樣例程序
進入 bin 目錄 運行 ./deviceQuery 如果CUDA安裝及配置無誤，輸出如下： ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "GeForce GTX 770M" CUDA Driver Version / Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 3.0 Total amount of global memory: 3017 MBytes (3163357184 bytes) ( 5) Multiprocessors, (192) CUDA Cores/MP: 960 CUDA Cores GPU Max Clock rate: 797 MHz (0.80 GHz) Memory Clock rate: 2004 Mhz Memory Bus Width: 192-bit L2 Cache Size: 393216 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 1 copy engine(s) Run time limit on kernels: Yes Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX 770M Result = PASS Result = Pass，校驗通過 運行： ./bandwidthTest 來測試系統和CUDA組建的通信無誤，正常結果輸出如下： [CUDA Bandwidth Test] - Starting... Running on... Device 0: GeForce GTX 770M Quick Mode Host to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) 33554432 11534.4 Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) 33554432 11768.0 Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) 33554432 72735.8 Result = PASS NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. 輸出Result = Pass 校驗通過
注：運行期間如果遇到錯誤，可能是系統SELINUX狀態為開啟，或者NVIDIA必要文件缺失，詳見官方文檔 第6.2.2.3章節
至此，CUDA安裝校驗完成
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校驗Tensorflow安裝
- 激活虛擬環境，然后運行
$ python >>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() >>> print(sess.run(hello)) 如果輸出 Hello, TensorFlow! 說明TF安裝正常
Edit 1
帶GPU支持TF在import tensorflow的時候出現了錯誤日志：
>>> import tensorflow as t
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library libcudnn.so.5. LD_LIBRARY_PATH: /usr/local/cuda-8.0/lib64/
I tensorflow/stream_executor/cuda/cuda_dnn.cc:3517] Unable to load cuDNN DSO
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
Couldn't open CUDA library libcudnn.so.5. LD_LIBRARY_PATH: /usr/local/cuda-8.0/lib64/
Google得知是因為CUDNN包沒有安裝；
點擊這里進入下載頁面；
下載CUDNN需要注冊NVIDIA帳號，有些問題要答，隨便勾選，隨便寫點什么就可以了；
完成注冊之后，就可以下載了；
下載完成之后，運行
sudo tar -xvf cudnn-8.0-linux-x64-v5.1-rc.tgz -C /usr/local
這里假設/usr/local是cuda的安裝目錄
然后再次import：
>>> import tensorflow as t
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] 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_device.cc:885] Found device 0 with properties:
name: GeForce GTX 770M
major: 3 minor: 0 memoryClockRate (GHz) 0.797
pciBusID 0000:01:00.0
Total memory: 2.95GiB
Free memory: 2.52GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 770M, pci bus id: 0000:01:00.0)
Those are simply warnings. They are just informing you if you build TensorFlow from source it can be faster on your machine. Those instructions are not enabled by default on the builds available I think to be compatible with more CPUs as possible.
If you have any other doubts regarding this please feel free to ask, otherwise this can be closed.
所有的庫都加載成功了；上面輸出的帶W的警告，查詢后是因為沒有通過源代碼編譯TF，有些CPU的參數沒有開啟，因此CPU不支持一系列協議。不過，這只會影響CPU的計算速度，并不影響GPU的計算。
