接到了一個需求缭保，需要驗證一下在深度操作系統(tǒng)中是否適配以下軟件：
- python2.7
- python3.5
- gnuradio 最新版
- tensorflow 1.7以上版本
- cuda 9.1版本適配
- cndnn 7.1版本適配
- QT 5.0版本適配
首先砾莱，操作系統(tǒng)已經(jīng)默認(rèn)裝了python2.7和python3.5 肮塞。通過命令行就可以查看：
deepin@deepin-PC:~$ apt-cache policy python2.7 python3.5
python2.7:
已安裝：2.7.13-2
候選： 2.7.13-2
版本列表：
*** 2.7.13-2 500
500 http://pools.corp.deepin.com/professional elephant/main amd64 Packages
100 /var/lib/dpkg/status
python3.5:
已安裝：3.5.3-1
候選： 3.5.3-1
版本列表：
*** 3.5.3-1 500
500 http://pools.corp.deepin.com/professional elephant/main amd64 Packages
100 /var/lib/dpkg/status
驗證gnuradio的最新版
安裝依賴:
sudo apt-get install git cmake libboost-all-dev swig3.0 libasound2-dev pyqt4-dev-tools libqwt-dev libzmq3-dev libusb-1.0-0-dev doxygen python-mako bison flex libevent-dev libssl-dev python-twisted autoconf -y
下載最新源碼
git clone https://github.com/gnuradio/gnuradio.git
編譯安裝
cd gnuradio
mkdir build
cd build
cmake -DENABLE_INTERNAL_VOLK=OFF ../
make
make test
sudo make install
驗證 tensorflow 1.7以上版本：
多次執(zhí)行sudo pip install tensorflow ，因為執(zhí)行的時候，會下載好幾個包，但是下載會失敗多次，所以多執(zhí)行幾遍善玫。
安裝完成之后，執(zhí)行命令驗證tensorflow 是否能夠正確運行：
deepin@deepin-PC:~/cudnn_samples_v7/mnistCUDNN$ python
Python 2.7.13 (default, Jan 19 2017, 14:48:08)
[GCC 6.3.0 20170118] on linux2
Type "help", "copyright", "credits" or "license" for more information.
import tensorflow as tf
hello = tf.constant('Hello,TensorFlow!')
sess = tf.Session()
2018-10-23 10:49:04.013088: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
print sess.run(hello)
Hello,TensorFlow!
使用命令pip3 show tensorflow密强，查看tensorflow版本
deepin@deepin-PC:~/Downloads$ pip show tensorflow
Name: tensorflow
Version: 1.11.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: opensource@google.com
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: protobuf, grpcio, termcolor, numpy, six, mock, tensorboard, backports.weakref, absl-py, wheel, keras-applications, keras-preprocessing, gast, enum34, astor, setuptools
cuda 9.1版本適配
在連接中下載修改repo的deb包：
https://developer.nvidia.com/cuda-91-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1704&target_type=debnetwork
安裝步驟：
sudo dpkg -i cuda-repo-ubuntu1704_9.1.85-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1704/x86_64/7fa2af80.pub`
sudo apt-get update
sudo apt-get install cuda
cudnn7.1安裝步驟：
官網(wǎng)下載包cudnn-9.1-linux-x64-v7.1.tgz 茅郎，libcudnn7_7.1.3.16-1+cuda9.1_amd64.deb ，libcudnn7-dev_7.1.3.16-1+cuda9.1_amd64.deb 或渤，libcudnn7-doc_7.1.3.16-1+cuda9.1_amd64.deb ：
https://developer.nvidia.com/rdp/cudnn-archive
執(zhí)行命令：
tar -xzvf cudnn-9.1-linux-x64-v7.1.tgz
cd cudnn
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/
安裝deb包：
sudo dpkg -i libcudnn7_7.1.3.16-1+cuda9.1_amd64.deb
sudo dpkg -i libcudnn7-dev_7.1.3.16-1+cuda9.1_amd64.deb
sudo dpkg -i libcudnn7-doc_7.1.3.16-1+cuda9.1_amd64.deb
QT5.0安裝：
下載地址
http://download.qt.io/archive/qt/5.0/5.0.0/
chmod +x qt-linux-opensource-5.0.0-x86_64-offline.run
./qt-linux-opensource-5.0.0-x86_64-offline.run
執(zhí)行安裝成功系冗。
