Ubuntu上安装TensorFlow

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环境

  • 系统: Ubuntu 16.04.4 LTS
  • 内核: 4.13.0-36-generic
  • CUDA: 9.0.176
  • 显卡: 940mx
  • 显卡驱动: 384.13
  • GCC: 5.4.0
  • python: 2.7.12

Dependences

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sudo apt-get install python-pip python-dev python3-pip python3-dev cuds-command-line-tools

sudo pip install testresources enum34 mock
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sudo edit ~/.bashrc

添加:

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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64

刷新:

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source ~/.bashrc

安装CUDA 9.0

官网下载CUDA cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb。

注意，使用.run文件安装cuda的话，在安装TensorRT时会发生错误！

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sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
sudo apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

重启，配置环境：

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sudo edit ~/.bashrc

添加：

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export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

刷新：

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source ~/.bashrc

重启：

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nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuba compilation tools, release 9.0, V9.0.176
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dpkg-query -W | grep cuda-cubla

cuda-cublas-9-0	9.0.176-1
cuda-cublas-dev-9-0	9.0.176-1
Building Samples (optional)
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cd /NVIDIA_CUDA-9.1_Samples/1_Utilities/deviceQuery
make
./deviceQuery 

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce 940MX"
  CUDA Driver Version / Runtime Version          9.0 / 9.0
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 2003 Mbytes (2100232192 bytes)
  ( 3) Multiprocessors, (128) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            1242 MHz (1.24 GHz)
  Memory Clock rate:                             1001 Mhz
  Memory Bus Width:                              64-bit
  L2 Cache Size:                                 1048576 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
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 2 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS

cuDNN

下载cuDNN v7.0.5 (Dec 5, 2017), for CUDA 9.0:

  • cuDNN v7.0.5 Runtime Library for Ubuntu16.04 (Deb)
  • cuDNN v7.0.5 Developer Library for Ubuntu16.04 (Deb)
  • cuDNN v7.0.5 Code Samples and User Guide for Ubuntu16.04 (Deb)

Navigate to your directory containing cuDNN Deb file:

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sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb 
sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.1.4.18-1+cuda9.0_amd64.deb
Verifying
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cp -r /usr/src/cudnn_samples_v7/ $HOME
cd $HOME/cudnn_samples_v7/mnistCUDNN
make clean && make
./mnistCUDNN
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cudnnGetVersion() : 7005 , CUDNN_VERSION from cudnn.h : 7005 (7.0.5)
Host compiler version : GCC 5.4.0
There are 1 CUDA capable devices on your machine :
device 0 : sms  3  Capabilities 5.0, SmClock 1241.5 Mhz, MemSize (Mb) 2002, MemClock 1001.0 Mhz, Ecc=0, boardGroupID=0
Using device 0

...

Test passed!

NVIDIA TensorRT 3.0.4(optional)

官网下载nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb1。

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sudo dpkg -i nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt

sudo apt-get install python-libnvinfer-doc python-libnvinfer python-libnvinfer-dev swig3.0  # python 2.7

sudo apt-get install python3-libnvinfer-doc # python 3.5
Verifying
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dpkg -l | grep TensorRT

ii  libnvinfer-dev                                              4.0.4-1+cuda9.0                                             amd64        TensorRT development libraries and headers
ii  libnvinfer-samples                                          4.0.4-1+cuda9.0                                             amd64        TensorRT samples and documentation
ii  libnvinfer4                                                 4.0.4-1+cuda9.0                                             amd64        TensorRT runtime libraries
ii  python-libnvinfer                                           4.0.4-1+cuda9.0                                             amd64        Python bindings for TensorRT
ii  python-libnvinfer-dev                                       4.0.4-1+cuda9.0                                             amd64        Python development package for TensorRT
ii  python-libnvinfer-doc                                       4.0.4-1+cuda9.0                                             amd64        Documention and samples of python bindings for TensorRT
ii  python3-libnvinfer                                          4.0.4-1+cuda9.0                                             amd64        Python 3 bindings for TensorRT
ii  python3-libnvinfer-dev                                      4.0.4-1+cuda9.0                                             amd64        Python 3 development package for TensorRT
ii  python3-libnvinfer-doc                                      4.0.4-1+cuda9.0                                             amd64        Documention and samples of python bindings for TensorRT
ii  tensor

安装TensorFlow 1.8 2

For python 2.7

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sudo apt-get install python-pip python-dev

pip install pip==9.0 # Don't use pip > 9.0 !!!!
sudo pip  install --upgrade https://download.tensorflow.google.cn/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl
sudo pip install --upgrade pip

For python 3.5

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sudo apt-get install python3-pip python3-dev

sudo pip3 install --upgrade https://download.tensorflow.google.cn/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl

卸载指令: sudo pip uninstall tensorflow or sudo pip3 uninstall tensor flow

Verifying
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python

写入:

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# Python
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

config = tf.ConfigProto(allow_soft_placement=True)

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)

config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

hello = tf.constant('Hello, TensorFlow!')
print(sess.run(hello))

输出:

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Hello, TensorFlow!

  1. https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html↩︎

  2. https://www.tensorflow.org/install/install_linux?hl=zh-cn#InstallingNativePip↩︎