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Posted to commits@mxnet.apache.org by sk...@apache.org on 2018/01/11 18:40:18 UTC
[incubator-mxnet-site] branch asf-site updated: Fix gpu install
instructions (#42)
This is an automated email from the ASF dual-hosted git repository.
skm pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 434d67a Fix gpu install instructions (#42)
434d67a is described below
commit 434d67a88cbaa3912322f66313978713bf19ee71
Author: thinksanky <31...@users.noreply.github.com>
AuthorDate: Thu Jan 11 10:40:16 2018 -0800
Fix gpu install instructions (#42)
* fixed the install instructions for Linux GPU
* fixed one missing location in install instructions for Linux GPU
---
install/index.html | 24 ++++++++++++------------
1 file changed, 12 insertions(+), 12 deletions(-)
diff --git a/install/index.html b/install/index.html
index 72d5bf1..ee3e399 100644
--- a/install/index.html
+++ b/install/index.html
@@ -384,8 +384,8 @@ pip install graphviz
<p><strong>Prerequisites</strong></p>
<p>Install the following NVIDIA libraries to setup <em>MXNet</em> with GPU support:</p>
<ol class="simple">
-<li>Install CUDA 8.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-linux/">installation guide</a>.</li>
-<li>Install cuDNN 5 for CUDA 8.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
+<li>Install CUDA 9.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-linux/">installation guide</a>.</li>
+<li>Install cuDNN 7 for CUDA 9.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
</ol>
<p><strong>Note:</strong> Make sure to add CUDA install path to <code class="docutils literal"><span class="pre">LD_LIBRARY_PATH</span></code>.</p>
<p>Example - <em>export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH</em></p>
@@ -397,8 +397,8 @@ $ sudo apt-get install -y wget python
$ wget https://bootstrap.pypa.io/get-pip.py <span class="o">&&</span> sudo python get-pip.py
</pre></div>
</div>
-<p><strong>Step 2</strong> Install <em>MXNet</em> with GPU support using CUDA 8.0</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu80==1.0.0
+<p><strong>Step 2</strong> Install <em>MXNet</em> with GPU support using CUDA 9.0</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu90==1.0.0
</pre></div>
</div>
<p><strong>Step 3</strong> Install <a class="reference external" href="http://www.graphviz.org/">Graphviz</a>. (Optional, needed for graph visualization using <code class="docutils literal"><span class="pre">mxnet.viz</span></code> package).</p>
@@ -408,7 +408,7 @@ pip install graphviz
</div>
<p><strong>Step 4</strong> Validate the installation by running simple MXNet code described <a class="reference external" href="#validate-mxnet-installation">here</a>.</p>
<p><strong>Experimental Choice</strong> If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu80mkl==1.0.0
+<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu90mkl==1.0.0
</pre></div>
</div>
</div><div class="virtualenv"><p><br/></p>
@@ -641,7 +641,7 @@ $ bash install-mxnet-osx-python.sh
</div><!-- END - Mac OS Python CPU Installation Instructions --><!-- START - Mac OS Python GPU Installation Instructions --><div class="macos">
<div class="python">
<div class="gpu"><p>More details and verified installation instructions for macOS, with GPUs, coming soon.</p>
-<p><em>MXNet</em> is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 8.0 and cuDNN 5.0, prior to installing GPU version of <em>MXNet</em>.</p>
+<p><em>MXNet</em> is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 9.0 and cuDNN 7.0, prior to installing GPU version of <em>MXNet</em>.</p>
</div>
</div>
</div><!-- END - Mac OS Python GPU Installation Instructions --><!-- START - Cloud Python Installation Instructions --><div class="cloud"><p>AWS Marketplace distributes AMIs (Amazon Machine Image) with MXNet pre-installed. You can launch an Amazon EC2 instance with one of the below AMIs:</p>
@@ -709,8 +709,8 @@ $ R CMD INSTALL mxnet_current_r.tar.gz
<p><strong>Prerequisites</strong></p>
<p>Install the following NVIDIA libraries to setup <em>MXNet</em> with GPU support:</p>
<ol class="simple">
-<li>Install CUDA 8.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-linux/">installation guide</a>.</li>
-<li>Install cuDNN 5 for CUDA 8.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
+<li>Install CUDA 9.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-linux/">installation guide</a>.</li>
+<li>Install cuDNN 7 for CUDA 9.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
</ol>
<p><strong>Note:</strong> Make sure to add CUDA install path to <code class="docutils literal"><span class="pre">LD_LIBRARY_PATH</span></code>.</p>
<p>Example - <em>export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH</em></p>
@@ -1099,15 +1099,15 @@ b
<p><strong>Prerequisites</strong></p>
<p>Install the following NVIDIA libraries to setup <em>MXNet</em> with GPU support:</p>
<ol class="simple">
-<li>Install CUDA 8.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows">installation guide</a>.</li>
-<li>Install cuDNN 7 for CUDA 8.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
+<li>Install CUDA 9.0 following the NVIDIA’s <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows">installation guide</a>.</li>
+<li>Install cuDNN 7 for CUDA 9.0 following the NVIDIA’s <a class="reference external" href="https://developer.nvidia.com/cudnn">installation guide</a>. You may need to register with NVIDIA for downloading the cuDNN library.</li>
</ol>
<p><strong>Note:</strong> Make sure to add CUDA install path to <code class="docutils literal"><span class="pre">PATH</span></code>.</p>
<div class="pip">
<br/><p><strong>Step 1</strong> Install python.</p>
<p>Recommend install <code class="docutils literal"><span class="pre">Anaconda3</span></code> <a class="reference external" href="https://www.anaconda.com/download/">here</a></p>
-<p><strong>Step 2</strong> Install <em>MXNet</em> with GPU support using CUDA 8.0</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu80==1.0.0
+<p><strong>Step 2</strong> Install <em>MXNet</em> with GPU support using CUDA 9.0</p>
+<div class="highlight-bash"><div class="highlight"><pre><span></span>$ pip install mxnet-cu90==1.0.0
</pre></div>
</div>
</div>
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