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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p><code>tensorflow 2</code>的个人安装过程，以及解决出现的一些问题。<br><a id="more"></a></p>
<h2 id="系统要求"><a href="#系统要求" class="headerlink" title="系统要求"></a>系统要求</h2><p>我安装的版本是 <code>tensorflow 2.1.0</code>，这个版本以及可以支持 CPU 和 GPU 的运行与计算。</p>
<ul>
<li>Python 3.5-3.7</li>
<li>pip 19.0 或更高版本（需要 manylinux2010 支持）</li>
<li>Ubuntu 16.04 或更高版本（64 位）</li>
<li>macOS 10.12.6 (Sierra) 或更高版本（64 位）（不支持 GPU）</li>
<li>Windows 7 或更高版本（64 位）（仅支持 Python 3）</li>
<li>Raspbian 9.0 或更高版本</li>
<li>GPU 支持需要使用支持 CUDA® 的卡（适用于 Ubuntu 和 Windows）</li>
</ul>
<h2 id="Windows安装过程"><a href="#Windows安装过程" class="headerlink" title="Windows安装过程"></a>Windows安装过程</h2><p>本人是Windows系统，下面写一下Windows安装的过程。Windows安装需要注意以下几点：</p>
<ol>
<li>安装适用于 Visual Studio 2015、2017 和 2019 的 Microsoft Visual C++ Redistributable。从 <code>TensorFlow 2.1.0</code> 版开始，此软件包需要 <code>msvcp140_1.dll</code> 文件（旧版可再发行软件包可能不提供此文件）。 该可再发行软件包随附在 Visual Studio 2019 中，但可以单独安装。</li>
<li>确保在 Windows 上启用了长路径</li>
<li>安装 64 位适用于 Windows 的 Python 3 版本</li>
</ol>
<h3 id="Microsoft-Visual-C-Redistributable安装"><a href="#Microsoft-Visual-C-Redistributable安装" class="headerlink" title="Microsoft Visual C++ Redistributable安装"></a>Microsoft Visual C++ Redistributable安装</h3><p>如果电脑里安装了Visual Studio 2015, 2017 and 2019，这一步就不用了，如果没有，你那这一步还是好好安装，要不然就会出现<code>ImportError: DLL load failed</code>错误。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span><span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf</span><br><span class="line">Traceback (most recent call last):</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py"</span>, line <span class="number">58</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">from</span> tensorflow.python.pywrap_tensorflow_internal <span class="keyword">import</span> *</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py"</span>, line <span class="number">28</span>, <span class="keyword">in</span> <module></span><br><span class="line"> _pywrap_tensorflow_internal = swig_import_helper()</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py"</span>, line <span class="number">24</span>, <span class="keyword">in</span> swig_import_helper</span><br><span class="line"> _mod = imp.load_module(<span class="string">'_pywrap_tensorflow_internal'</span>, fp, pathname, description)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\imp.py"</span>, line <span class="number">242</span>, <span class="keyword">in</span> load_module</span><br><span class="line"> <span class="keyword">return</span> load_dynamic(name, filename, file)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\imp.py"</span>, line <span class="number">342</span>, <span class="keyword">in</span> load_dynamic</span><br><span class="line"> <span class="keyword">return</span> _load(spec)</span><br><span class="line">ImportError: DLL load failed: The specified module could <span class="keyword">not</span> be found.</span><br><span class="line"></span><br><span class="line">During handling of the above exception, another exception occurred:</span><br><span class="line"></span><br><span class="line">Traceback (most recent call last):</span><br><span class="line"> File <span class="string">"<stdin>"</span>, line <span class="number">1</span>, <span class="keyword">in</span> <module></span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow\__init__.py"</span>, line <span class="number">101</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">from</span> tensorflow_core <span class="keyword">import</span> *</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\__init__.py"</span>, line <span class="number">40</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">from</span> tensorflow.python.tools <span class="keyword">import</span> module_util <span class="keyword">as</span> _module_util</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow\__init__.py"</span>, line <span class="number">50</span>, <span class="keyword">in</span> __getattr__</span><br><span class="line"> module = self._load()</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow\__init__.py"</span>, line <span class="number">44</span>, <span class="keyword">in</span> _load</span><br><span class="line"> module = _importlib.import_module(self.__name__)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\importlib\__init__.py"</span>, line <span class="number">127</span>, <span class="keyword">in</span> import_module</span><br><span class="line"> <span class="keyword">return</span> _bootstrap._gcd_import(name[level:], package, level)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\__init__.py"</span>, line <span class="number">49</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">from</span> tensorflow.python <span class="keyword">import</span> pywrap_tensorflow</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py"</span>, line <span class="number">74</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">raise</span> ImportError(msg)</span><br><span class="line">ImportError: Traceback (most recent call last):</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py"</span>, line <span class="number">58</span>, <span class="keyword">in</span> <module></span><br><span class="line"> <span class="keyword">from</span> tensorflow.python.pywrap_tensorflow_internal <span class="keyword">import</span> *</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py"</span>, line <span class="number">28</span>, <span class="keyword">in</span> <module></span><br><span class="line"> _pywrap_tensorflow_internal = swig_import_helper()</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py"</span>, line <span class="number">24</span>, <span class="keyword">in</span> swig_import_helper</span><br><span class="line"> _mod = imp.load_module(<span class="string">'_pywrap_tensorflow_internal'</span>, fp, pathname, description)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\imp.py"</span>, line <span class="number">242</span>, <span class="keyword">in</span> load_module</span><br><span class="line"> <span class="keyword">return</span> load_dynamic(name, filename, file)</span><br><span class="line"> File <span class="string">"C:\Development\Python\Python37\lib\imp.py"</span>, line <span class="number">342</span>, <span class="keyword">in</span> load_dynamic</span><br><span class="line"> <span class="keyword">return</span> _load(spec)</span><br><span class="line">ImportError: DLL load failed: The specified module could <span class="keyword">not</span> be found.</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">Failed to load the native TensorFlow runtime.</span><br><span class="line"></span><br><span class="line">See https://www.tensorflow.org/install/errors</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> some common reasons <span class="keyword">and</span> solutions. Include the entire stack trace</span><br><span class="line">above this error message when asking <span class="keyword">for</span> help.</span><br></pre></td></tr></table></figure></p>
<p>解决方案就是去<a href="https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads" target="_blank" rel="noopener">Microsoft Visual C++下载</a>页面，下载x64: <a href="https://aka.ms/vs/16/release/vc_redist.x64.exe" target="_blank" rel="noopener">vc_redist.x64.exe</a>， 完成安装。因为你的电脑必须要是64位的，才能安装64位的 <code>Python 3</code>，才能满足 <code>tensorflow 2.1.0</code>的安装要求。</p>
<h3 id="长路径"><a href="#长路径" class="headerlink" title="长路径"></a>长路径</h3><p>长路径好像不启用，也没什么问题，但保险起见，还是打开吧，反正也挺容易的。</p>
<ol>
<li>按 <code>win+R</code>，输入 <code>gpedit.msc</code>，打开本地策略组编辑器</li>
<li>依次选择 <code>计算机配置</code> > <code>管理模板</code> > <code>系统</code> > <code>文件系统</code></li>
<li>在 <code>文件系统</code> 界面下，选择 <code>启用 Win32 长路径</code>，双击打开，选择<code>已启用</code>，然后确定关闭。</li>
</ol>
<h3 id="64位-Python-3"><a href="#64位-Python-3" class="headerlink" title="64位 Python 3"></a>64位 <code>Python 3</code></h3><p>一定要安装64位的 <code>Python 3</code>。查看 <code>Python 3</code> 版本方式有很多，下面写两个最简单的。</p>
<ol>
<li><p>打开 <code>cmd</code> 命令行， 输入 <code>python -VV</code>。<code>python -V</code> 是简略的版本信息，<code>python -VV</code> 信息更加详细，包括位数信息，我的64位版本信息如下：</p>
<figure class="highlight cmd"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">Python <span class="number">3</span>.<span class="number">7</span>.<span class="number">5</span> (tags/v3.<span class="number">7</span>.<span class="number">5</span>:<span class="number">5</span>c02a39a0b, Oct <span class="number">15</span> <span class="number">2019</span>, <span class="number">00</span>:<span class="number">11</span>:<span class="number">34</span>) [MSC v.<span class="number">1916</span> <span class="number">64</span> bit (AMD64)]</span><br></pre></td></tr></table></figure>
</li>
<li><p>打开python命令行，最开始出现的消息里面就有位数信息。</p>
<figure class="highlight cmd"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="function">C:\<span class="title">Users</span>\*******><span class="title">python</span></span></span><br><span class="line"><span class="function"><span class="title">Python</span> 3.7.5 (<span class="title">tags</span>/<span class="title">v3</span>.7.5:5<span class="title">c02a39a0b</span>, <span class="title">Oct</span> 15 2019, 00:11:34) [<span class="title">MSC</span> <span class="title">v</span>.1916 64 <span class="title">bit</span> (<span class="title">AMD64</span>)] <span class="title">on</span> <span class="title">win32</span></span></span><br><span class="line"><span class="function"><span class="title">Type</span> "<span class="title">help</span>", "<span class="title">copyright</span>", "<span class="title">credits</span>" <span class="title">or</span> "<span class="title">license</span>" <span class="title">for</span> <span class="title">more</span> <span class="title">information</span>.</span></span><br><span class="line"><span class="function">>>></span></span><br></pre></td></tr></table></figure>
</li>
</ol>
<p>在 Windows 上，仅支持在 <code>Python 3</code> 64位版本下， 安装 <code>TensorFlow 2.1.0</code>，如果是 <code>Python 2</code> 版本， 在 Windows 上是实现不了的。</p>
<h3 id="安装-TensorFlow-2-1-0"><a href="#安装-TensorFlow-2-1-0" class="headerlink" title="安装 TensorFlow 2.1.0"></a>安装 <code>TensorFlow 2.1.0</code></h3><p>安装使用 <code>pip</code> 工具，19.0 或者更高版本。如果在虚拟环境里安装，注意先要更新 <code>pip</code>，然后在进行 <code>pip</code> 的安装 ( <code>python -m pip install --upgrade pip</code> )。<br><figure class="highlight cmd"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install tensorflow</span><br></pre></td></tr></table></figure></p>
<p>由于安装包比较大，推荐使用国内镜像，在命令行后加 <code>-i [https://xxxxx]</code> 即可，下面是使用清华镜像源，你也可以永久修改默认源的使用，这里就不介绍了。<br><figure class="highlight cmd"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple</span><br></pre></td></tr></table></figure></p>
<h2 id="GPU-安装支持"><a href="#GPU-安装支持" class="headerlink" title="GPU 安装支持"></a>GPU 安装支持</h2><p>如果你进行了以上安装，不出意外的话，<code>tensorflow</code> 应该是安装成功了，但你在使用时可能会看到以下警告，因为现在的 <code>tensorflow</code> 只能使用 CPU，无法使用 GPU。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="number">2020</span>-xx-xx xx:xx:xx.xxxxxx: W tensorflow/stream_executor/platform/default/dso_loader.cc:<span class="number">55</span>] Could <span class="keyword">not</span> load dynamic library <span class="string">'cudart64_101.dll'</span>; dlerror: cudart64_101.dll <span class="keyword">not</span> found</span><br><span class="line"></span><br><span class="line"><span class="number">2020</span>-xx-xx xx:xx:xx.xxxxxx: I tensorflow/stream_executor/cuda/cudart_stub.cc:<span class="number">29</span>] Ignore above cudart dlerror <span class="keyword">if</span> you do <span class="keyword">not</span> have a GPU set up on your machine.</span><br></pre></td></tr></table></figure></p>
<p>首先查看你的 GPU 是否支持 CUDA，点击<a href="http://developer.nvidia.com/cuda-gpus" target="_blank" rel="noopener">查询页面</a>查找自己的显卡是否在列表内。或者使用”显卡名称 + specification“进行搜索，直接查看自己的显卡信息。</p>
<p>显卡支持后，如果要使用 GPU，还需要安装下面几个软件：</p>
<ul>
<li>NVIDIA Graphic Drivers (一般都有)</li>
<li><a href="https://developer.nvidia.com/cuda-toolkit-archive" target="_blank" rel="noopener">NVIDIA CUDA Toolkit</a>, <a href="http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html" target="_blank" rel="noopener">CUDA 安装文档</a></li>
<li><a href="https://developer.nvidia.com/rdp/cudnn-download" target="_blank" rel="noopener">cuDNN SDK</a>, <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows" target="_blank" rel="noopener">cuDNN 安装文档</a></li>
</ul>
<p>CUDA 选择 10.1 版本，cuDNN 选择配套版本。安装完成后，<code>import tensorflow</code> 不报错，无警告，应该就成功了。</p>
<h2 id="检查使用情况"><a href="#检查使用情况" class="headerlink" title="检查使用情况"></a>检查使用情况</h2><p>最后，在看看自己设备上的使用信息。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> tensorflow.python.client <span class="keyword">import</span> device_lib</span><br><span class="line">print(device_lib.list_local_devices())</span><br><span class="line"></span><br><span class="line">[name: <span class="string">"/device:CPU:0"</span></span><br><span class="line">device_type: <span class="string">"CPU"</span></span><br><span class="line">memory_limit: <span class="number">268435456</span></span><br><span class="line">locality {</span><br><span class="line">}</span><br><span class="line">incarnation: <span class="number">11567553738839476818</span></span><br><span class="line">, name: <span class="string">"/device:GPU:0"</span></span><br><span class="line">device_type: <span class="string">"GPU"</span></span><br><span class="line">memory_limit: <span class="number">6661821563</span></span><br><span class="line">locality {</span><br><span class="line"> bus_id: <span class="number">1</span></span><br><span class="line"> links {</span><br><span class="line"> }</span><br><span class="line">}</span><br><span class="line">incarnation: <span class="number">17494855292993829603</span></span><br><span class="line">physical_device_desc: <span class="string">"device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1"</span></span><br><span class="line">]</span><br></pre></td></tr></table></figure></p>
<p>至此，结束，感觉挺麻烦的。</p>
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<div style="text-align:center;color: #ccc;font-size:14px;">-------------本文结束<i class="fa fa-paw"></i>感谢您的阅读-------------</div>
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<div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#系统要求"><span class="nav-number">1.</span> <span class="nav-text">系统要求</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Windows安装过程"><span class="nav-number">2.</span> <span class="nav-text">Windows安装过程</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#Microsoft-Visual-C-Redistributable安装"><span class="nav-number">2.1.</span> <span class="nav-text">Microsoft Visual C++ Redistributable安装</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#长路径"><span class="nav-number">2.2.</span> <span class="nav-text">长路径</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#64位-Python-3"><span class="nav-number">2.3.</span> <span class="nav-text">64位 Python 3</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#安装-TensorFlow-2-1-0"><span class="nav-number">2.4.</span> <span class="nav-text">安装 TensorFlow 2.1.0</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#GPU-安装支持"><span class="nav-number">3.</span> <span class="nav-text">GPU 安装支持</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#检查使用情况"><span class="nav-number">4.</span> <span class="nav-text">检查使用情况</span></a></li></ol></div>
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