-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathzai-ubuntu-1804xia-an-zhuang-cuda91.html
executable file
·96 lines (80 loc) · 7.49 KB
/
zai-ubuntu-1804xia-an-zhuang-cuda91.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
<!DOCTYPE html>
<html lang="zh">
<head>
<meta charset="utf-8" />
<title>在Ubuntu 18.04下安装CUDA9.1</title>
<link rel="stylesheet" href="https://bridgeqiao.github.io/blog/theme/css/main.css" />
<!--[if IE]>
<script src="http://html5shiv.googlecode.com/svn/trunk/html5.js"></script>
<![endif]-->
</head>
<body id="index" class="home">
<header id="banner" class="body">
<h1><a href="https://bridgeqiao.github.io/blog/">Bridge Qiao's Blog </a></h1>
<nav><ul>
<li><a href="https://bridgeqiao.github.io/blog/category/computer-vision.html">Computer vision</a></li>
<li><a href="https://bridgeqiao.github.io/blog/category/fun.html">fun</a></li>
<li class="active"><a href="https://bridgeqiao.github.io/blog/category/linux.html">linux</a></li>
<li><a href="https://bridgeqiao.github.io/blog/category/machine-learning.html">Machine Learning</a></li>
<li><a href="https://bridgeqiao.github.io/blog/category/matlab.html">Matlab</a></li>
<li><a href="https://bridgeqiao.github.io/blog/category/suan-fa-ti.html">算法题</a></li>
</ul>
</nav>
</header><!-- /#banner -->
<section id="content" class="body">
<article>
<header>
<h1 class="entry-title">
<a href="https://bridgeqiao.github.io/blog/zai-ubuntu-1804xia-an-zhuang-cuda91.html" rel="bookmark"
title="Permalink to 在Ubuntu 18.04下安装CUDA9.1">在Ubuntu 18.04下安装CUDA9.1</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<span>二 24 七月 2018</span>
</footer><!-- /.post-info --> <p>由于有台服务器一直闲置在那里，所以给配了个tensorflow-gpu的环境。电脑用的ubuntu18.04，而到现在为止，官方最新的tensorflow-gpu是不支持cuda9.1的，至于为什么要装9.1，是因为用的ubuntu内置的包，下面简要说下安装的过程：</p>
<h3>安装NVIDIA驱动</h3>
<div class="highlight"><pre><span></span>sudo apt install nvidia-driver-390
</pre></div>
<h3>安装cuda</h3>
<div class="highlight"><pre><span></span>sudo apt install nvidia-cuda-toolkit
</pre></div>
<h3>安装cudnn 7.1</h3>
<p>这个是在官网直接下载的，网速不好的话可以在百度云找到相应的文件，然后通过pandownload之类的工具下载。</p>
<h3>安装tensorflow</h3>
<p>由于官方还不支持cuda9.1，所以找的别人编译好的wheel文件，网上有很多，这里提供个我找的<a href="https://github.com/mind/wheels/releases">网站</a>，选择相应的即可，MKL是Intel提供的加速工具，到目前为止还不太稳定，如果没安装的话，选择no MKL的包。</p>
<h3>任意测试下tensorflow是否安装成功</h3>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">'sparse_categorical_crossentropy'</span><span class="p">,</span>
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
<p>这是tensorflow目前最新的测试代码。</p>
</div><!-- /.entry-content -->
</article>
</section>
<section id="extras" class="body">
<div class="blogroll">
<h2>blogroll</h2>
<ul>
<li><a href="http://getpelican.com/">Pelican</a></li>
<li><a href="http://python.org/">Python.org</a></li>
<li><a href="https://github.com">Github</a></li>
</ul>
</div><!-- /.blogroll -->
</section><!-- /#extras -->
<footer id="contentinfo" class="body">
<p>Powered by <a href="http://getpelican.com/">Pelican</a>. Theme <a href="https://github.com/blueicefield/pelican-blueidea/">blueidea</a>, inspired by the default theme.</p>
</footer><!-- /#contentinfo -->
</body>
</html>