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| <h1 class="post-title">MXNet on the Cloud</h1> |
| <h3></h3></header> |
| |
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| <h3 style="text-transform: capitalize; padding-left:10px">faq</h3> |
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| <li><a href="/versions/master/api/faq/add_op_in_backend">A Beginner's Guide to Implementing Operators in MXNet Backend</a></li> |
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| <li><a href="/versions/master/api/faq/large_tensor_support">Using MXNet with Large Tensor Support</a></li> |
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| <li><a href="/versions/master/api/faq/model_parallel_lstm">Model Parallel</a></li> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --> |
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| <!--- distributed with this work for additional information --> |
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| <!--- specific language governing permissions and limitations --> |
| <!--- under the License. --> |
| |
| <h1 id="mxnet-on-the-cloud">MXNet on the Cloud</h1> |
| |
| <p>Deep learning can require extremely powerful hardware, often for unpredictable durations of time. |
| Moreover, <em>MXNet</em> can benefit from both multiple GPUs and multiple machines. |
| Accordingly, cloud computing, as offered by AWS and others, |
| is especially well suited to training deep learning models. |
| Using AWS, we can rapidly fire up multiple machines with multiple GPUs each at will |
| and maintain the resources for precisely the amount of time needed.</p> |
| |
| <h2 id="set-up-an-aws-gpu-cluster-from-scratch">Set Up an AWS GPU Cluster from Scratch</h2> |
| |
| <p>In this document, we provide a step-by-step guide that will teach you |
| how to set up an AWS cluster with <em>MXNet</em>. We show how to:</p> |
| |
| <ul> |
| <li><a href="#use-pre-installed-ec2-gpu-instance">Use Pre-installed EC2 GPU Instance</a></li> |
| <li><a href="#build-and-run-mxnet-on-a-gpu-instance">Build and run MXNet on a single computer</a></li> |
| <li><a href="#set-up-an-ec2-gpu-cluster-for-distributed-training">Set up an EC2 GPU cluster for distributed training</a></li> |
| </ul> |
| |
| <h3 id="use-pre-installed-ec2-gpu-instance">Use Pre-installed EC2 GPU Instance</h3> |
| <p>The <a href="https://aws.amazon.com/marketplace/search/results?x=0&y=0&searchTerms=Deep+Learning+AMI">Deep Learning AMIs</a> |
| are a series of images supported and maintained by Amazon Web Services for use |
| on Amazon Elastic Compute Cloud (Amazon EC2) and contain the latest MXNet release.</p> |
| |
| <p>Now you can launch <em>MXNet</em> directly on an EC2 GPU instance. |
| You can also use <a href="https://jupyter.org">Jupyter</a> notebook on EC2 machine. |
| Here is a <a href="https://github.com/dmlc/mxnet-notebooks">good tutorial</a> |
| on how to connect to a Jupyter notebook running on an EC2 instance.</p> |
| |
| <h3 id="set-up-an-ec2-gpu-instance-from-scratch">Set Up an EC2 GPU Instance from Scratch</h3> |
| |
| <p><a href="https://aws.amazon.com/marketplace/search/results?x=0&y=0&searchTerms=Deep+Learning+Base+AMI">Deep Learning Base AMIs</a> |
| provide a foundational image with NVIDIA CUDA, cuDNN, GPU drivers, oneDNN, |
| Docker and Nvidia-Docker, etc. for deploying your own custom deep |
| learning environment. You may follow the <a href="https://mxnet.apache.org/get_started/build_from_source">MXNet Build From Source |
| instructions</a> easily on |
| the Deep Learning Base AMIs.</p> |
| |
| <h3 id="set-up-an-ec2-gpu-cluster-for-distributed-training">Set Up an EC2 GPU Cluster for Distributed Training</h3> |
| |
| <p>A cluster consists of multiple computers. |
| You can use one computer with <em>MXNet</em> installed as the root computer for submitting jobs,and then launch several |
| slave computers to run the jobs. For example, launch multiple instances using an |
| AMI with dependencies installed. There are two options:</p> |
| |
| <ul> |
| <li> |
| <p>Make all slaves’ ports accessible (same for the root) by setting type: All TCP, |
| Source: Anywhere in Configure Security Group.</p> |
| </li> |
| <li> |
| <p>Use the same <code class="highlighter-rouge">pem</code> as the root computer to access all slave computers, and |
| then copy the <code class="highlighter-rouge">pem</code> file into the root computer’s <code class="highlighter-rouge">~/.ssh/id_rsa</code>. If you do this, all slave computers can be accessed with SSH from the root.</p> |
| </li> |
| </ul> |
| |
| <p>Now, run the CNN on multiple computers. Assume that we are on a working |
| directory of the root computer, such as <code class="highlighter-rouge">~/train</code>, and MXNet is built as <code class="highlighter-rouge">~/mxnet</code>.</p> |
| |
| <ol> |
| <li>Pack the <em>MXNet</em> Python library into this working directory for easy |
| synchronization:</li> |
| </ol> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="nb">cp</span> <span class="nt">-r</span> ~/mxnet/python/mxnet <span class="nb">.</span> |
| <span class="nb">cp</span> ~/mxnet/lib/libmxnet.so mxnet/ |
| </code></pre></div></div> |
| |
| <p>And then copy the training program:</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code> <span class="nb">cp</span> ~/mxnet/example/image-classification/<span class="k">*</span>.py <span class="nb">.</span> |
| <span class="nb">cp</span> <span class="nt">-r</span> ~/mxnet/example/image-classification/common <span class="nb">.</span> |
| </code></pre></div></div> |
| |
| <ol> |
| <li>Prepare a host file with all slaves private IPs. For example, <code class="highlighter-rouge">cat hosts</code>:</li> |
| </ol> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code> 172.30.0.172 |
| 172.30.0.171 |
| </code></pre></div></div> |
| |
| <ol> |
| <li>Assuming that there are two computers, train the CNN using two workers:</li> |
| </ol> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code> ../../tools/launch.py <span class="nt">-n</span> 2 <span class="nt">-H</span> hosts <span class="nt">--sync-dir</span> /tmp/mxnet python train_mnist.py <span class="nt">--kv-store</span> dist_sync |
| </code></pre></div></div> |
| |
| <p><strong><em>Note:</em></strong> Sometimes the jobs linger at the slave computers even though you’ve pressed <code class="highlighter-rouge">Ctrl-c</code> |
| at the root node. To terminate them, use the following command:</p> |
| |
| <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="nb">cat </span>hosts | xargs <span class="nt">-I</span><span class="o">{}</span> ssh <span class="nt">-o</span> <span class="nv">StrictHostKeyChecking</span><span class="o">=</span>no <span class="o">{}</span> <span class="s1">'uname -a; pgrep python | xargs kill -9'</span> |
| </code></pre></div></div> |
| |
| <p><strong><em>Note:</em></strong> The preceding example is very simple to train and therefore isn’t a good |
| benchmark for distributed training. Consider using other <a href="https://github.com/apache/mxnet/tree/master/example/image-classification">examples</a>.</p> |
| |
| <h3 id="more-options">More Options</h3> |
| <h4 id="use-multiple-data-shards">Use Multiple Data Shards</h4> |
| <p>It is common to pack a dataset into multiple files, especially when working in a distributed environment. |
| <em>MXNet</em> supports direct loading from multiple data shards. |
| Put all of the record files into a folder, and point the data path to the folder.</p> |
| |
| <h4 id="use-yarn-and-sge">Use YARN and SGE</h4> |
| <p>Although using SSH can be simple when you don’t have a cluster scheduling framework, |
| <em>MXNet</em> is designed to be portable to various platforms. |
| We provide scripts available in <a href="https://github.com/dmlc/dmlc-core/tree/master/tracker">tracker</a> |
| to allow running on other cluster frameworks, including Hadoop (YARN) and SGE. |
| We welcome contributions from the community of examples of running <em>MXNet</em> on your favorite distributed platform.</p> |
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