小編給大家分享一下怎么在Ubuntu 18.04服務器上安裝TensorFlow,相信大部分人都還不怎么了解,因此分享這篇文章給大家參考一下,希望大家閱讀完這篇文章后大有收獲,下面讓我們一起去了解一下吧!
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我們假設使用64位的操作系統(tǒng),顯卡為GeForce 740m。SSH登錄到服務器,更新和升級:
apt update -y apt upgrade –y
運行這個命令來安裝Python庫:
sudo apt install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy python-six python3-six build-essential python-pip python3-pip python-virtualenv swig python-wheel python3-wheel libcurl3-dev libcupti-dev
繼續(xù)運行
sudo apt install libcurl4-openssl-dev
通過運行,我們可以看到安裝的顯卡硬件:
sudo lshw -C display | grep product
我們需要安裝Nvidia驅動程序。我們可以檢查SSH上的圖形驅動程序:
nvidia-smi
這是Ubuntu的PPA,瀏覽一下:
https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa
nvidia-graphics-drivers-396是最新的,但可能沒有太多測試。我們可以添加 nvidia-graphics-drivers-390 PPA 并安裝該應用程序。
sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt update sudo apt upgrade ubuntu-drivers devices sudo ubuntu-drivers autoinstall
如果有意外情況,autoinstall不起作用,則運行:
sudo apt install nvidia-390
現(xiàn)在,再次運行命令:
nvidia-smi
您將得到一個有用的輸出。我們應該保持住這個版本停止升級。
sudo apt-mark hold nvidia-driver-390
安裝 Linux—headers :
sudo apt install linux-headers-$(uname -r)
為了后續(xù)步驟正常進行,我們需要 gcc, g++ 等等:
apt install gcc g++ gcc-6 g++-6 gcc-4.8 g++-4.8 # if gcc-4.8 package not found run # sudo add-apt-repository ppa:ubuntu-toolchain-r/test # sudo apt update # sudo apt install gcc-4.8 g++-4.8
現(xiàn)在我們必須安裝CUDA工具包:
apt install nvidia-cuda-toolkit libcupti-dev
重啟
sudo reboot
安裝CUDA工具包:
https://developer.nvidia.com/cuda-toolkit
運行:
cd Downloads/ sudo sh cuda_9.0.176_384.81_linux.run --override --silent –toolkit
接下來,您需要安裝CUDNN,NCCL。您需要按照PyTorch老方法,使用Nvdia帳戶登錄,這很簡單。您將獲得鏈接:cuDNN v7.1.x Library for Linux。您需要下載deb文件,并將FTP上傳到服務器。URL是:
https://developer.nvidia.com/rdp/cudnn-download
https://developer.nvidia.com/nccl
找到已安裝CUDA的目錄。它正在將文件復制到/usr/local/cuda/。將上述內容移到安裝CUDA的目錄中并運行這些操作(注意版本編號的目錄,以下是格式示例):
tar -xzvf cudnn-9.0-linux-x64-v7.1.tgz sudo cp cuda/include/cudnn.h /usr/local/cuda/include sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
以上將節(jié)省空間,并避免apt警告。打開配置文件,如.bashrc:
nano ~/.bashrc
添加這些:
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda
重新加載:
source ~/.bashrc sudo ldconfig echo $CUDA_HOME
安裝Bazel:
sudo apt install curl echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add - sudo apt update -y sudo apt upgrade -y sudo apt install bazel sudo apt upgrade bazel pip install keras
查看Nvidia版本:
cd ~ git clone https://github.com/tensorflow/tensorflow cd ~/tensorflow # check current revision number from browser git checkout r1.11 cd ~/tensorflow
通過運行創(chuàng)建配置文件:
./configure
您將得到這樣的輸出:
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: Y Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: N Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: N Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: N Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: N Do you wish to build TensorFlow with XLA JIT support? [y/N]: N Do you wish to build TensorFlow with GDR support? [y/N]: N Do you wish to build TensorFlow with VERBS support? [y/N]: N Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: N Do you wish to build TensorFlow with CUDA support? [y/N]: Y Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.0 Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda Do you wish to build TensorFlow with TensorRT support? [y/N]: N Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 5.0] 3.0 Do you want to use clang as CUDA compiler? [y/N]: N Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: /usr/bin/gcc-4.8 Do you wish to build TensorFlow with MPI support? [y/N]: N Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: -march=native Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:N
構建TensorFlow :
最后的步驟:
bazel-bin/tensorflow/tools/pip_package/build_pip_package tensorflow_pkg cd tensorflow_pkg/ sudo pip3 install tensorflow-<name_of_generated_file>.whl
通過切換到另一個目錄并運行python來檢查您的構建是否正常工作:
import tensorflow as tf hello = tf.constant('Hello World!') sess = tf.Session() print(sess.run(hello))
您將得到Hello World!輸出。TensorFlow有以下型號:
https://github.com/tensorflow/models
您可以運行:
git clone https://github.com/tensorflow/models.git cd models/tutorials/image/imagenet python classify_image.py
這是一些基本設置和測試。
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文章標題:怎么在Ubuntu18.04服務器上安裝TensorFlow
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