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| <!--- under the License. --><div class="section" id="run-mxnet-on-multiple-cpu-gpus-with-data-parallelism"> |
| <span id="run-mxnet-on-multiple-cpu-gpus-with-data-parallelism"></span><h1>Run MXNet on Multiple CPU/GPUs with Data Parallelism<a class="headerlink" href="#run-mxnet-on-multiple-cpu-gpus-with-data-parallelism" title="Permalink to this headline">¶</a></h1> |
| <p><em>MXNet</em> supports training with multiple CPUs and GPUs, which may be located on different physical machines.</p> |
| <div class="section" id="data-parallelism-vs-model-parallelism"> |
| <span id="data-parallelism-vs-model-parallelism"></span><h2>Data Parallelism vs Model Parallelism<a class="headerlink" href="#data-parallelism-vs-model-parallelism" title="Permalink to this headline">¶</a></h2> |
| <p>By default, <em>MXNet</em> uses data parallelism to partition the workload over multiple |
| devices. |
| Assume there are <em>n</em> devices. |
| Then each one will receive a copy of the complete model |
| and train it on <em>1/n</em> of the data. |
| The results such as gradients and |
| updated model are communicated across these devices.</p> |
| <p>MXNet also supports model parallelism. |
| In this approach, each device holds onto only part of the model. |
| This proves useful when the model is too large to fit onto a single device. |
| As an example, see the following <a class="reference internal" href="model_parallel_lstm.html"><span class="doc">tutorial</span></a> |
| which shows how to use model parallelism for training a multi-layer LSTM model. |
| In this tutorial, we’ll focus on data parallelism.</p> |
| </div> |
| <div class="section" id="multiple-gpus-within-a-single-machine"> |
| <span id="multiple-gpus-within-a-single-machine"></span><h2>Multiple GPUs within a Single Machine<a class="headerlink" href="#multiple-gpus-within-a-single-machine" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="workload-partitioning"> |
| <span id="workload-partitioning"></span><h3>Workload Partitioning<a class="headerlink" href="#workload-partitioning" title="Permalink to this headline">¶</a></h3> |
| <p>By default, <em>MXNet</em> partitions a data batch evenly among the available GPUs. |
| Assume a batch size <em>b</em> and assume there are <em>k</em> GPUs, then in one iteration |
| each GPU will perform forward and backward on <em>b/k</em> examples. The |
| gradients are then summed over all GPUs before updating the model.</p> |
| </div> |
| <div class="section" id="how-to-use"> |
| <span id="how-to-use"></span><h3>How to Use<a class="headerlink" href="#how-to-use" title="Permalink to this headline">¶</a></h3> |
| <blockquote> |
| <div>To use GPUs, we need to compile MXNet with GPU support. For |
| example, set <code class="docutils literal"><span class="pre">USE_CUDA=1</span></code> in <code class="docutils literal"><span class="pre">config.mk</span></code> before <code class="docutils literal"><span class="pre">make</span></code>. (see |
| <a class="reference external" href="/versions/1.4.1/install/index.html">MXNet installation guide</a> for more options).</div></blockquote> |
| <p>If a machine has one or more GPU cards installed, |
| then each card is labeled by a number starting from 0. |
| To use a particular GPU, one can either |
| specify the context <code class="docutils literal"><span class="pre">context</span></code> in code |
| or pass <code class="docutils literal"><span class="pre">--gpus</span></code> at the command line. |
| For example, to use GPU 0 and 2 in python, |
| one can typically create a module with</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span> |
| <span class="n">module</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">2</span><span class="p">)],</span> <span class="o">...</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>while if the program accepts a <code class="docutils literal"><span class="pre">--gpus</span></code> flag (as seen in |
| <a class="reference external" href="https://github.com/dmlc/mxnet/tree/master/example/image-classification">example/image-classification</a>), |
| then we can try</p> |
| <div class="highlight-bash"><div class="highlight"><pre><span></span>python train_mnist.py --gpus <span class="m">0</span>,2 ... |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="advanced-usage"> |
| <span id="advanced-usage"></span><h3>Advanced Usage<a class="headerlink" href="#advanced-usage" title="Permalink to this headline">¶</a></h3> |
| <p>If the available GPUs are not all equally powerful, |
| we can partition the workload accordingly. |
| For example, if GPU 0 is 3 times faster than GPU 2, |
| then we might use the workload option <code class="docutils literal"><span class="pre">work_load_list=[3,</span> <span class="pre">1]</span></code>, |
| see <a class="reference external" href="/versions/1.4.1/api/python/module/module.html#mxnet.module.Module">Module</a> |
| for more details.</p> |
| <p>Training with multiple GPUs should yield the same results |
| as training on a single GPU if all other hyper-parameters are the same.f |
| In practice, the results may exhibit small differences, |
| owing to the randomness of I/O (random order or other augmentations), |
| weight initialization with different seeds, and CUDNN.</p> |
| <p>We can control on which devices the gradient is aggregated |
| and on which device the model is updated via <a class="reference external" href="/versions/1.4.1/api/python/kvstore/kvstore.html"><code class="docutils literal"><span class="pre">KVStore</span></code></a>, |
| the <em>MXNet</em> module that supports data communication. |
| One can either use <code class="docutils literal"><span class="pre">mx.kvstore.create(type)</span></code> to get an instance |
| or use the program flag <code class="docutils literal"><span class="pre">--kv-store</span> <span class="pre">type</span></code>.</p> |
| <p>There are two commonly used types,</p> |
| <ul class="simple"> |
| <li><code class="docutils literal"><span class="pre">local</span></code>: all gradients are copied to CPU memory and weights are updated there.</li> |
| <li><code class="docutils literal"><span class="pre">device</span></code>: both gradient aggregation and weight updates are run on GPUs. |
| With this setting, the <code class="docutils literal"><span class="pre">KVStore</span></code> also attempts to use GPU peer-to-peer communication, |
| potentially accelerating the communication. |
| Note that this option may result in higher GPU memory usage.</li> |
| </ul> |
| <p>When using a large number of GPUs, e.g. >=4, we suggest using <code class="docutils literal"><span class="pre">device</span></code> for better performance.</p> |
| </div> |
| </div> |
| <div class="section" id="distributed-training-with-multiple-devices-across-machines"> |
| <span id="distributed-training-with-multiple-devices-across-machines"></span><h2>Distributed training with multiple devices across machines<a class="headerlink" href="#distributed-training-with-multiple-devices-across-machines" title="Permalink to this headline">¶</a></h2> |
| <p>Refer <a class="reference external" href="/versions/master/faq/distributed_training.html">Distributed training</a> |
| for information on how distributed training works and how to use it.</p> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> |
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| <h3><a href="../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Run MXNet on Multiple CPU/GPUs with Data Parallelism</a><ul> |
| <li><a class="reference internal" href="#data-parallelism-vs-model-parallelism">Data Parallelism vs Model Parallelism</a></li> |
| <li><a class="reference internal" href="#multiple-gpus-within-a-single-machine">Multiple GPUs within a Single Machine</a><ul> |
| <li><a class="reference internal" href="#workload-partitioning">Workload Partitioning</a></li> |
| <li><a class="reference internal" href="#how-to-use">How to Use</a></li> |
| <li><a class="reference internal" href="#advanced-usage">Advanced Usage</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#distributed-training-with-multiple-devices-across-machines">Distributed training with multiple devices across machines</a></li> |
| </ul> |
| </li> |
| </ul> |
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| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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