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  <div class="section" id="command-line-arguments">
<span id="command-line-arguments"></span><h1>Command-line arguments<a class="headerlink" href="#command-line-arguments" title="Permalink to this headline">¶</a></h1>
<p>We&#8217;ll take <code class="docutils literal"><span class="pre">doc/howto/cluster/src/word2vec</span></code> as an example to introduce distributed training using PaddlePaddle v2 API.</p>
<div class="section" id="starting-parameter-server">
<span id="starting-parameter-server"></span><h2>Starting parameter server<a class="headerlink" href="#starting-parameter-server" title="Permalink to this headline">¶</a></h2>
<p>Type the below command to start a parameter server which will wait for trainers to connect:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ paddle pserver --port<span class="o">=</span><span class="m">7164</span> --ports_num<span class="o">=</span><span class="m">1</span> --ports_num_for_sparse<span class="o">=</span><span class="m">1</span> --num_gradient_servers<span class="o">=</span><span class="m">1</span> --nics<span class="o">=</span>eth0
</pre></div>
</div>
<p>If you wish to run parameter servers in background, and save a log file, you can type:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ stdbuf -oL /usr/bin/nohup paddle pserver --port<span class="o">=</span><span class="m">7164</span> --ports_num<span class="o">=</span><span class="m">1</span> --ports_num_for_sparse<span class="o">=</span><span class="m">1</span> --num_gradient_servers<span class="o">=</span><span class="m">1</span> --nics<span class="o">=</span>eth0 <span class="p">&amp;</span>&gt; pserver.log <span class="p">&amp;</span>
</pre></div>
</div>
<p>Parameter Description</p>
<ul class="simple">
<li>port: <strong>required, default 7164</strong>, port which parameter server will listen on. If ports_num greater than 1, parameter server will listen on multiple ports for more network throughput.</li>
<li>ports_num: <strong>required, default 1</strong>, total number of ports will listen on.</li>
<li>ports_num_for_sparse: <strong>required, default 0</strong>, number of ports which serves sparse parameter update.</li>
<li>num_gradient_servers: <strong>required, default 1</strong>, total number of gradient servers.</li>
<li>nics: <strong>optional, default xgbe0,xgbe1</strong>, network device name which paramter server will listen on.</li>
</ul>
</div>
<div class="section" id="starting-trainer">
<span id="starting-trainer"></span><h2>Starting trainer<a class="headerlink" href="#starting-trainer" title="Permalink to this headline">¶</a></h2>
<p>Type the command below to start the trainer(name the file whatever you want, like &#8220;train.py&#8221;)</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>$ python train.py
</pre></div>
</div>
<p>Trainers&#8217; network need to be connected with parameter servers&#8217; network to finish the job. Trainers need to know port and IPs to locate parameter servers. You can pass arguments to trainers through <a class="reference external" href="https://en.wikipedia.org/wiki/Environment_variable">environment variables</a> or pass to <code class="docutils literal"><span class="pre">paddle.init()</span></code> function. Arguments passed to the <code class="docutils literal"><span class="pre">paddle.init()</span></code> function will overwrite environment variables.</p>
<p>Use environment viriables:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">PADDLE_INIT_USE_GPU</span><span class="o">=</span>False
<span class="nb">export</span> <span class="nv">PADDLE_INIT_TRAINER_COUNT</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_PORT</span><span class="o">=</span><span class="m">7164</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_PORTS_NUM</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_PORTS_NUM_FOR_SPARSE</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_NUM_GRADIENT_SERVERS</span><span class="o">=</span><span class="m">1</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_TRAINER_ID</span><span class="o">=</span><span class="m">0</span>
<span class="nb">export</span> <span class="nv">PADDLE_INIT_PSERVERS</span><span class="o">=</span><span class="m">127</span>.0.0.1
python train.py
</pre></div>
</div>
<p>Pass arguments:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">.</span><span class="n">init</span><span class="p">(</span>
        <span class="n">use_gpu</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
        <span class="n">trainer_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="n">port</span><span class="o">=</span><span class="mi">7164</span><span class="p">,</span>
        <span class="n">ports_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="n">ports_num_for_sparse</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="n">num_gradient_servers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
        <span class="n">trainer_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
        <span class="n">pservers</span><span class="o">=</span><span class="s2">&quot;127.0.0.1&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>Parameter Description</p>
<ul class="simple">
<li>use_gpu: <strong>optional, default False</strong>, set to &#8220;True&#8221; to enable GPU training.</li>
<li>trainer_count: <strong>required, default 1</strong>, number of threads in current trainer.</li>
<li>port: <strong>required, default 7164</strong>, port to connect to parameter server.</li>
<li>ports_num: <strong>required, default 1</strong>, number of ports for communication.</li>
<li>ports_num_for_sparse: <strong>required, default 0</strong>, number of ports for sparse type caculation.</li>
<li>num_gradient_servers: <strong>required, default 1</strong>, number of trainers in current job.</li>
<li>trainer_id: <strong>required, default 0</strong>, ID for every trainer, start from 0.</li>
<li>pservers: <strong>required, default 127.0.0.1</strong>, list of IPs of parameter servers, separated by &#8221;,&#8221;.</li>
</ul>
</div>
<div class="section" id="prepare-training-dataset">
<span id="prepare-training-dataset"></span><h2>Prepare Training Dataset<a class="headerlink" href="#prepare-training-dataset" title="Permalink to this headline">¶</a></h2>
<p>Here&#8217;s some example code <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py">prepare.py</a>, it will download public <code class="docutils literal"><span class="pre">imikolov</span></code> dataset and split it into multiple files according to job parallelism(trainers count). Modify <code class="docutils literal"><span class="pre">SPLIT_COUNT</span></code> at the begining of <code class="docutils literal"><span class="pre">prepare.py</span></code> to change the count of output files.</p>
<p>In the real world, we often use <code class="docutils literal"><span class="pre">MapReduce</span></code> job&#8217;s output as training data, so there will be lots of files. You can use <code class="docutils literal"><span class="pre">mod</span></code> to assign training file to trainers:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="n">train_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">flist</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s2">&quot;/train_data/&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">flist</span><span class="p">:</span>
  <span class="n">suffix</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;-&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
  <span class="k">if</span> <span class="n">suffix</span> <span class="o">%</span> <span class="n">TRAINER_COUNT</span> <span class="o">==</span> <span class="n">TRAINER_ID</span><span class="p">:</span>
    <span class="n">train_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>
</div>
<p>Example code <code class="docutils literal"><span class="pre">prepare.py</span></code> will split training data and testing data into 3 files with digital suffix like <code class="docutils literal"><span class="pre">-00000</span></code>, <code class="docutils literal"><span class="pre">-00001</span></code> and<code class="docutils literal"><span class="pre">-00002</span></code>:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>train.txt
train.txt-00000
train.txt-00001
train.txt-00002
test.txt
test.txt-00000
test.txt-00001
test.txt-00002
</pre></div>
</div>
<p>When job started, every trainer needs to get it&#8217;s own part of data. In some distributed systems a storage service will be provided, so the date under that path can be accessed by all the trainer nodes. Without the storage service, you must copy the training data to each trainer node.</p>
<p>Different training jobs may have different data format and <code class="docutils literal"><span class="pre">reader()</span></code> function, developers may need to write different data prepare scripts and <code class="docutils literal"><span class="pre">reader()</span></code> functions for their job.</p>
</div>
<div class="section" id="prepare-training-program">
<span id="prepare-training-program"></span><h2>Prepare Training program<a class="headerlink" href="#prepare-training-program" title="Permalink to this headline">¶</a></h2>
<p>We&#8217;ll create a <em>workspace</em> directory on each node, storing your training program, dependencies, mounted or downloaded dataset directory.</p>
<p>Your workspace may looks like:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>.
<span class="p">|</span>-- my_lib.py
<span class="p">|</span>-- word_dict.pickle
<span class="p">|</span>-- train.py
<span class="p">|</span>-- train_data_dir/
<span class="p">|</span>   <span class="p">|</span>-- train.txt-00000
<span class="p">|</span>   <span class="p">|</span>-- train.txt-00001
<span class="p">|</span>   <span class="p">|</span>-- train.txt-00002
<span class="sb">`</span>-- test_data_dir/
    <span class="p">|</span>-- test.txt-00000
    <span class="p">|</span>-- test.txt-00001
    <span class="sb">`</span>-- test.txt-00002
</pre></div>
</div>
<ul>
<li><p class="first"><code class="docutils literal"><span class="pre">my_lib.py</span></code>: user defined libraries, like PIL libs. This is optional.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">word_dict.pickle</span></code>: dict file for training word embeding.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">train.py</span></code>: training program. Sample code: <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py">api_train_v2_cluster.py</a>. <strong><em>NOTE:</em></strong> You may need to modify the head part of <code class="docutils literal"><span class="pre">train.py</span></code> when using different cluster platform to retrive configuration environment variables:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cluster_train_file</span> <span class="o">=</span> <span class="s2">&quot;./train_data_dir/train/train.txt&quot;</span>
<span class="n">cluster_test_file</span> <span class="o">=</span> <span class="s2">&quot;./test_data_dir/test/test.txt&quot;</span>
<span class="n">node_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;OMPI_COMM_WORLD_RANK&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">node_id</span><span class="p">:</span>
    <span class="k">raise</span> <span class="ne">EnvironmentError</span><span class="p">(</span><span class="s2">&quot;must provied OMPI_COMM_WORLD_RANK&quot;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">train_data_dir</span></code>: containing training data. Mount from storage service or copy trainning data to here.</p>
</li>
<li><p class="first"><code class="docutils literal"><span class="pre">test_data_dir</span></code>: containing testing data.</p>
</li>
</ul>
</div>
<div class="section" id="async-sgd-update">
<span id="async-sgd-update"></span><h2>Async SGD Update<a class="headerlink" href="#async-sgd-update" title="Permalink to this headline">¶</a></h2>
<p>We can set some parameters of the optimizer to make it support async SGD update.
For example, we can set the <code class="docutils literal"><span class="pre">is_async</span></code> and <code class="docutils literal"><span class="pre">async_lagged_grad_discard_ratio</span></code> of the <code class="docutils literal"><span class="pre">AdaGrad</span></code> optimizer:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">adagrad</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">AdaGrad</span><span class="p">(</span>
    <span class="n">is_async</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
    <span class="n">async_lagged_grad_discard_ratio</span><span class="o">=</span><span class="mf">1.6</span><span class="p">,</span>
    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">3e-3</span><span class="p">,</span>
    <span class="n">regularization</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">L2Regularization</span><span class="p">(</span><span class="mf">8e-4</span><span class="p">))</span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">is_async</span></code>: Is Async-SGD or not.</li>
<li><code class="docutils literal"><span class="pre">async_lagged_grad_discard_ratio</span></code>: For async SGD gradient commit control.
when <code class="docutils literal"><span class="pre">async_lagged_grad_discard_ratio</span> <span class="pre">*</span> <span class="pre">num_gradient_servers</span></code> commit passed,
current async gradient will be discard silently.</li>
</ul>
</div>
</div>


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