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    <li>Design Doc: Session</li>
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  <div class="section" id="design-doc-session">
<span id="design-doc-session"></span><h1>Design Doc: Session<a class="headerlink" href="#design-doc-session" title="永久链接至标题"></a></h1>
<div class="section" id="abstract">
<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="永久链接至标题"></a></h2>
<p>The <em>session</em> object encapsulates the environment in which the
computation graph is executed.</p>
<p>We will have the <em>local</em> session and <em>remote</em> session, they offer the
same <a class="reference external" href="#interface">interface</a>. The local session encapsulates the local
runtime environment and the remote session encapsulates the cluster
runtime environment.</p>
<p>The local runtime environment contains:</p>
<ol class="simple">
<li>computation devices (i.e., CPU, GPU) handles, and</li>
<li>the <a class="reference internal" href="../scope.html"><span class="doc">scope</span></a> which holds all variables.</li>
</ol>
<p>The remote runtime environment contains:</p>
<ol class="simple">
<li>computation devices (i.e., CPU and GPU on node 0, 1) in a cluster,
and</li>
<li>the distributed <a class="reference internal" href="../scope.html"><span class="doc">scope</span></a> in a cluster which holds all
variables.</li>
</ol>
<p>The user can create a remote session on Paddle Cloud and evaluate the
computation graph with it. In this way, the user can control the
remote computation resource in a cluster from his local computer.</p>
</div>
<div class="section" id="background">
<span id="background"></span><h2>Background<a class="headerlink" href="#background" title="永久链接至标题"></a></h2>
<p>The current design has an implicit global session in which
<code class="docutils literal"><span class="pre">paddle.eval()</span></code> is executed. The pain point is:</p>
<p>Since the user is not able to explicitly switch between runtime
environments, the user cannot run a topology in two independent
environments.</p>
<p>For example, in reinforcement learning, the user may want to have a
stale model for inference and a fresh model for training, and only
replace the stale model with the fresh model periodically.</p>
<p>Furthermore, we have no concept that encapsulates a remote environment
that executes a computation graph.</p>
<p>We need the session object to address above issues.</p>
</div>
<div class="section" id="session">
<span id="session"></span><h2>Session<a class="headerlink" href="#session" title="永久链接至标题"></a></h2>
<p>A session is an object that owns the runtime environment. All
computations are executed through <code class="docutils literal"><span class="pre">session.eval()</span></code>.</p>
<div class="section" id="interface">
<span id="interface"></span><h3>Interface<a class="headerlink" href="#interface" title="永久链接至标题"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="nb">eval</span><span class="p">(</span>
    <span class="n">targets</span><span class="p">,</span>
    <span class="n">feed_dict</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Evaluates the target Operations or Variables in <code class="docutils literal"><span class="pre">targets</span></code>.</p>
<ul>
<li><p class="first"><em>targets</em>: the evaluation targets. Can be a single Operation or
Variable, or a list with the Operations or Variables as
elements. The value returned by <code class="docutils literal"><span class="pre">eval()</span></code> has the same shape as the
<code class="docutils literal"><span class="pre">target</span></code> argument.</p>
<p>The PaddlePaddle program is represented by
the <a class="reference external" href="design/design/program.md">ProgramDesc</a>, <code class="docutils literal"><span class="pre">eval()</span></code> will infer the
ProgramDesc from the given targets and run the PaddlePaddle
program. Please
see
<a class="reference external" href="/design/refactor/distributed_architecture.html#local-training-architecture">this graph</a> for
the detailed illustration for the local session
and
<a class="reference external" href="/design/refactor/distributed_architecture.html#distributed-training-architecture">this graph</a> for
the detailed illustration for the remote session.</p>
</li>
<li><p class="first"><em>feed_dict</em>: a dictionary that contains the tensors which override
the edges of the computation graph.</p>
<p>feed_dict not only can provide the input data, it can override any
OP&#8217;s input as well:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">variable</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
<span class="n">sess</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">targets</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;b&quot;</span><span class="p">:</span><span class="mf">3.0</span><span class="p">})</span> <span class="c1"># returns 6.0</span>
</pre></div>
</div>
</li>
</ul>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Closes the session and releases the scope that the session owns.</p>
</div>
<div class="section" id="create-a-local-session">
<span id="create-a-local-session"></span><h3>Create a Local Session<a class="headerlink" href="#create-a-local-session" title="永久链接至标题"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">session</span><span class="p">(</span>
    <span class="n">devices</span><span class="o">=</span><span class="bp">None</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Creates a new session. One session owns one global scope, so creating
multiple sessions will create different scopes.</p>
<ul class="simple">
<li><em>devices</em>: a single <code class="docutils literal"><span class="pre">string</span></code> or a list of <code class="docutils literal"><span class="pre">string</span></code> of device names,
the corresponding devices will be the computation devices for
<code class="docutils literal"><span class="pre">eval()</span></code>. If not specified, all available devices (e.g., all GPUs)
will be used. The user doesn&#8217;t need to specify the CPU device since
it will be always used. Multiple sessions can use the same device.</li>
</ul>
<div class="section" id="example">
<span id="example"></span><h4>Example<a class="headerlink" href="#example" title="永久链接至标题"></a></h4>
<div class="highlight-Python"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">session</span><span class="p">(</span><span class="n">devices</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gpu:0&quot;</span><span class="p">,</span> <span class="s2">&quot;gpu:1&quot;</span><span class="p">,</span> <span class="s2">&quot;fpga:0&quot;</span><span class="p">])</span>
<span class="n">sess</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
<span class="n">sess</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="create-a-remote-session">
<span id="create-a-remote-session"></span><h3>Create a Remote Session<a class="headerlink" href="#create-a-remote-session" title="永久链接至标题"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">create_cloud_job</span><span class="p">(</span>
    <span class="n">name</span><span class="p">,</span>
    <span class="n">num_trainer</span><span class="p">,</span>
    <span class="n">mem_per_trainer</span><span class="p">,</span>
    <span class="n">gpu_per_trainer</span><span class="p">,</span>
    <span class="n">cpu_per_trainer</span><span class="p">,</span>
    <span class="n">num_ps</span><span class="p">,</span>
    <span class="n">mem_per_ps</span><span class="p">,</span>
    <span class="n">cpu_per_ps</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Creates a Paddle Cloud job. Fails if the job name exists.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">get_cloud_job</span><span class="p">(</span>
    <span class="n">name</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Gets a Paddle Cloud job.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">remote_session</span><span class="p">(</span>
    <span class="n">job</span>
<span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><em>job</em>: the Paddle Cloud job.</li>
</ul>
<div class="section" id="example">
<span id="id1"></span><h4>Example<a class="headerlink" href="#example" title="永久链接至标题"></a></h4>
<div class="highlight-Python"><div class="highlight"><pre><span></span><span class="n">reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">recordio</span><span class="p">(</span><span class="s2">&quot;/pfs/home/peter/mnist-train-*&quot;</span><span class="p">)</span> <span class="c1"># data stored on Paddle Cloud</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">column</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">column</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">fc1</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;sigmoid&quot;</span><span class="p">)</span>
<span class="n">fc2</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">cross_entropy</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
<span class="n">opt</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">sgd</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>

<span class="n">job</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">create_cloud_job</span><span class="p">(</span><span class="s2">&quot;test&quot;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;1G&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;1G&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">remote_ession</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">opt</span><span class="p">)</span>
<span class="n">sess</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
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