Deploy to GitHub Pages: 15b35f9a

develop/doc/_sources/design/refactor/session.md.txt

+# Design Doc: Session
+
+## Abstract
+
+The *session* object encapsulates the environment in which the
+computation graph is executed.
+
+We will have the *local* session and *remote* session, they offer the
+same [interface](#interface). The local session encapsulates the local
+runtime environment and the remote session encapsulates the cluster
+runtime environment.
+
+The local runtime environment contains:
+
+1. computation devices (i.e., CPU, GPU) handles, and
+1. the [scope](../scope.md) which holds all variables.
+
+The remote runtime environment contains:
+
+1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster,
+   and
+1. the distributed [scope](../scope.md) in a cluster which holds all
+   variables.
+
+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.
+
+
+## Background
+
+The current design has an implicit global session in which
+`paddle.eval()` is executed. The pain point is:
+
+Since the user is not able to explicitly switch between runtime
+environments, the user cannot run a topology in two independent
+environments.
+
+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.
+
+Furthermore, we have no concept that encapsulates a remote environment
+that executes a computation graph.
+
+We need the session object to address above issues.
+
+
+## Session
+
+A session is an object that owns the runtime environment. All
+computations are executed through `session.eval()`.
+
+
+### Interface
+
+```python
+eval(
+    targets,
+    feed_dict=None,
+)
+```
+
+Evaluates the target Operations or Variables in `targets`.
+
+- *targets*: the evaluation targets. Can be a single Operation or
+  Variable, or a list with the Operations or Variables as
+  elements. The value returned by `eval()` has the same shape as the
+  `target` argument.
+
+  The PaddlePaddle program is represented by
+  the [ProgramDesc](../design/program.md), `eval()` will infer the
+  ProgramDesc from the given targets and run the PaddlePaddle
+  program. Please
+  see
+  [this graph](./distributed_architecture.md#local-training-architecture) for
+  the detailed illustration for the local session
+  and
+  [this graph](./distributed_architecture.md#distributed-training-architecture) for
+  the detailed illustration for the remote session.
+
+- *feed_dict*: a dictionary that contains the tensors which override
+  the edges of the computation graph.
+
+  feed_dict not only can provide the input data, it can override any
+  OP's input as well:
+
+  ```python
+  a = pd.constant(2.0, name="a")
+  b = pd.variable(name="b")
+  c = pd.mul(a,b)
+  sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0
+  ```
+
+```python
+close()
+```
+
+Closes the session and releases the scope that the session owns.
+
+
+### Create a Local Session
+
+```python
+session(
+    devices=None
+)
+```
+
+Creates a new session. One session owns one global scope, so creating
+multiple sessions will create different scopes.
+
+- *devices*: a single `string` or a list of `string` of device names,
+  the corresponding devices will be the computation devices for
+  `eval()`. If not specified, all available devices (e.g., all GPUs)
+  will be used. The user doesn't need to specify the CPU device since
+  it will be always used. Multiple sessions can use the same device.
+
+
+#### Example
+
+```Python
+a = paddle.constant(1.0)
+b = paddle.constant(2.0)
+c = a + b
+sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"])
+sess.eval(c)
+sess.close()
+```
+
+### Create a Remote Session
+
+```python
+create_cloud_job(
+    name,
+    num_trainer,
+    mem_per_trainer,
+    gpu_per_trainer,
+    cpu_per_trainer,
+    num_ps,
+    mem_per_ps,
+    cpu_per_ps,
+)
+```
+
+Creates a Paddle Cloud job. Fails if the job name exists.
+
+```python
+get_cloud_job(
+    name
+)
+```
+
+Gets a Paddle Cloud job.
+
+```python
+remote_session(
+    job
+)
+```
+
+- *job*: the Paddle Cloud job.
+
+#### Example
+
+```Python
+reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud
+image = reader.column(0)
+label = reader.column(1)
+fc1 = paddle.op.fc(image, size=256, act="sigmoid")
+fc2 = paddle.op.fc(fc1, size=10, act="softmax")
+cost = paddle.op.cross_entropy(fc2, label)
+opt = paddle.optimizer.sgd(cost)
+
+job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1)
+sess = paddle.remote_ession(job)
+for i in range(1000):
+    sess.eval(opt)
+sess.close()
+```

develop/doc/design/refactor/distributed_architecture.html

@@ -295,7 +295,7 @@ once. When the target is the <code class="docutils literal"><span class="pre">co
 the cost value.</p>
 <p>The Python <code class="docutils literal"><span class="pre">session</span></code> is a wrapper of the C++ <code class="docutils literal"><span class="pre">Session</span></code> class. For more
 information about <code class="docutils literal"><span class="pre">Session</span></code>, please
-see <a class="reference external" href="design/refactor/session.md">Design Doc: Session</a>.</p>
+see <a class="reference internal" href="session.html"><span class="doc">Design Doc: Session</span></a>.</p>
 </div>
 </div>
 <div class="section" id="paddlepaddle-converter">

develop/doc/design/refactor/session.html

+
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+    <li>Design Doc: Session</li>
+  </ul>
+</div>
+      
+      <div class="wy-nav-content" id="doc-content">
+        <div class="rst-content">
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <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="Permalink to this headline">¶</a></h1>
+<div class="section" id="abstract">
+<span id="abstract"></span><h2>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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="Permalink to this headline">¶</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>
+</div>
+</div>
+</div>
+</div>
+</div>
+
+
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+  
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+    <p>
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develop/doc_cn/_sources/design/refactor/session.md.txt

+# Design Doc: Session
+
+## Abstract
+
+The *session* object encapsulates the environment in which the
+computation graph is executed.
+
+We will have the *local* session and *remote* session, they offer the
+same [interface](#interface). The local session encapsulates the local
+runtime environment and the remote session encapsulates the cluster
+runtime environment.
+
+The local runtime environment contains:
+
+1. computation devices (i.e., CPU, GPU) handles, and
+1. the [scope](../scope.md) which holds all variables.
+
+The remote runtime environment contains:
+
+1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster,
+   and
+1. the distributed [scope](../scope.md) in a cluster which holds all
+   variables.
+
+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.
+
+
+## Background
+
+The current design has an implicit global session in which
+`paddle.eval()` is executed. The pain point is:
+
+Since the user is not able to explicitly switch between runtime
+environments, the user cannot run a topology in two independent
+environments.
+
+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.
+
+Furthermore, we have no concept that encapsulates a remote environment
+that executes a computation graph.
+
+We need the session object to address above issues.
+
+
+## Session
+
+A session is an object that owns the runtime environment. All
+computations are executed through `session.eval()`.
+
+
+### Interface
+
+```python
+eval(
+    targets,
+    feed_dict=None,
+)
+```
+
+Evaluates the target Operations or Variables in `targets`.
+
+- *targets*: the evaluation targets. Can be a single Operation or
+  Variable, or a list with the Operations or Variables as
+  elements. The value returned by `eval()` has the same shape as the
+  `target` argument.
+
+  The PaddlePaddle program is represented by
+  the [ProgramDesc](../design/program.md), `eval()` will infer the
+  ProgramDesc from the given targets and run the PaddlePaddle
+  program. Please
+  see
+  [this graph](./distributed_architecture.md#local-training-architecture) for
+  the detailed illustration for the local session
+  and
+  [this graph](./distributed_architecture.md#distributed-training-architecture) for
+  the detailed illustration for the remote session.
+
+- *feed_dict*: a dictionary that contains the tensors which override
+  the edges of the computation graph.
+
+  feed_dict not only can provide the input data, it can override any
+  OP's input as well:
+
+  ```python
+  a = pd.constant(2.0, name="a")
+  b = pd.variable(name="b")
+  c = pd.mul(a,b)
+  sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0
+  ```
+
+```python
+close()
+```
+
+Closes the session and releases the scope that the session owns.
+
+
+### Create a Local Session
+
+```python
+session(
+    devices=None
+)
+```
+
+Creates a new session. One session owns one global scope, so creating
+multiple sessions will create different scopes.
+
+- *devices*: a single `string` or a list of `string` of device names,
+  the corresponding devices will be the computation devices for
+  `eval()`. If not specified, all available devices (e.g., all GPUs)
+  will be used. The user doesn't need to specify the CPU device since
+  it will be always used. Multiple sessions can use the same device.
+
+
+#### Example
+
+```Python
+a = paddle.constant(1.0)
+b = paddle.constant(2.0)
+c = a + b
+sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"])
+sess.eval(c)
+sess.close()
+```
+
+### Create a Remote Session
+
+```python
+create_cloud_job(
+    name,
+    num_trainer,
+    mem_per_trainer,
+    gpu_per_trainer,
+    cpu_per_trainer,
+    num_ps,
+    mem_per_ps,
+    cpu_per_ps,
+)
+```
+
+Creates a Paddle Cloud job. Fails if the job name exists.
+
+```python
+get_cloud_job(
+    name
+)
+```
+
+Gets a Paddle Cloud job.
+
+```python
+remote_session(
+    job
+)
+```
+
+- *job*: the Paddle Cloud job.
+
+#### Example
+
+```Python
+reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud
+image = reader.column(0)
+label = reader.column(1)
+fc1 = paddle.op.fc(image, size=256, act="sigmoid")
+fc2 = paddle.op.fc(fc1, size=10, act="softmax")
+cost = paddle.op.cross_entropy(fc2, label)
+opt = paddle.optimizer.sgd(cost)
+
+job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1)
+sess = paddle.remote_ession(job)
+for i in range(1000):
+    sess.eval(opt)
+sess.close()
+```

develop/doc_cn/design/refactor/distributed_architecture.html

@@ -309,7 +309,7 @@ once. When the target is the <code class="docutils literal"><span class="pre">co
 the cost value.</p>
 <p>The Python <code class="docutils literal"><span class="pre">session</span></code> is a wrapper of the C++ <code class="docutils literal"><span class="pre">Session</span></code> class. For more
 information about <code class="docutils literal"><span class="pre">Session</span></code>, please
-see <a class="reference external" href="design/refactor/session.md">Design Doc: Session</a>.</p>
+see <a class="reference internal" href="session.html"><span class="doc">Design Doc: Session</span></a>.</p>
 </div>
 </div>
 <div class="section" id="paddlepaddle-converter">

develop/doc_cn/design/refactor/session.html

+
+
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+
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+
+  
+  
+    
+
+  
+
+  
+  
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+  <ul class="wy-breadcrumbs">
+      
+    <li>Design Doc: Session</li>
+  </ul>
+</div>
+      
+      <div class="wy-nav-content" id="doc-content">
+        <div class="rst-content">
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  <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>
+</div>
+</div>
+</div>
+</div>
+</div>
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