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# Design Doc: Remote Parameter Updater for Cluster Train
For an overview of distribute training, please refer to [distributed training design doc](README.md). In this design doc, we will discuss the parameter updater that will use parameter server cclient [The Client Library of Parameter Server Design Doc](pserver_client.md) to manage and update parameters.
## Parameter Updater
Parameter Updater is used by trainer to manage and update parameter, there are mainly two kind of parameter updater: local and remote, since this design is for cluster train, we will only discuss remote parameter updater here.
### Remote Parameter Updater
Remote Parameter Updater manage parameters through remote parameter server with the client that communicate with pserver([The Client Library of Parameter Server Design Doc](pserver_client.md))
In PaddlePaddle Python V2 API, trainer is implemented in python, and the trainer will hold a instance of parameter updater and call it's functions directly. In this design, we will also expose the api of RemoteParameterUpdater to python with swig.
#### Sparse Remote Parameter Updater
Since we will only implement dense parameter management new, the mechanism for sparse parameter will be discussed in next stage.
### Interface Design
TBD
develop/doc/api/v2/run_logic.html
浏览文件 @ ca79f901
...@@ -375,7 +375,7 @@ Trainer.train.</p> ...@@ -375,7 +375,7 @@ Trainer.train.</p>
<p>Module Trainer</p> <p>Module Trainer</p>
<dl class="class"> <dl class="class">
<dt> <dt>
<em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em>, <em>is_local=True</em><span class="sig-paren">)</span></dt> <em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em>, <em>is_local=True</em>, <em>pserver_spec=None</em><span class="sig-paren">)</span></dt>
<dd><p>Simple SGD Trainer. <dd><p>Simple SGD Trainer.
SGD Trainer combines data reader, network topolopy and update_equation together SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.</p> to train/test a neural network.</p>
......
develop/doc/design/cluster_train/remote_parameter_updater.html 0 → 100644
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<li>Design Doc: Remote Parameter Updater for Cluster Train</li>
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<span id="design-doc-remote-parameter-updater-for-cluster-train"></span><h1>Design Doc: Remote Parameter Updater for Cluster Train<a class="headerlink" href="#design-doc-remote-parameter-updater-for-cluster-train" title="Permalink to this headline"></a></h1>
<p>For an overview of distribute training, please refer to <a class="reference internal" href="README.html"><span class="doc">distributed training design doc</span></a>. In this design doc, we will discuss the parameter updater that will use parameter server cclient <a class="reference internal" href="pserver_client.html"><span class="doc">The Client Library of Parameter Server Design Doc</span></a> to manage and update parameters.</p>
<div class="section" id="parameter-updater">
<span id="parameter-updater"></span><h2>Parameter Updater<a class="headerlink" href="#parameter-updater" title="Permalink to this headline"></a></h2>
<p>Parameter Updater is used by trainer to manage and update parameter, there are mainly two kind of parameter updater: local and remote, since this design is for cluster train, we will only discuss remote parameter updater here.</p>
<div class="section" id="remote-parameter-updater">
<span id="remote-parameter-updater"></span><h3>Remote Parameter Updater<a class="headerlink" href="#remote-parameter-updater" title="Permalink to this headline"></a></h3>
<p>Remote Parameter Updater manage parameters through remote parameter server with the client that communicate with pserver(<a class="reference internal" href="pserver_client.html"><span class="doc">The Client Library of Parameter Server Design Doc</span></a>)</p>
<p>In PaddlePaddle Python V2 API, trainer is implemented in python, and the trainer will hold a instance of parameter updater and call it&#8217;s functions directly. In this design, we will also expose the api of RemoteParameterUpdater to python with swig.</p>
<div class="section" id="sparse-remote-parameter-updater">
<span id="sparse-remote-parameter-updater"></span><h4>Sparse Remote Parameter Updater<a class="headerlink" href="#sparse-remote-parameter-updater" title="Permalink to this headline"></a></h4>
<p>Since we will only implement dense parameter management new, the mechanism for sparse parameter will be discussed in next stage.</p>
</div>
</div>
<div class="section" id="interface-design">
<span id="interface-design"></span><h3>Interface Design<a class="headerlink" href="#interface-design" title="Permalink to this headline"></a></h3>
<p>TBD</p>
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develop/doc_cn/_sources/design/cluster_train/remote_parameter_updater.md.txt 0 → 100644
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# Design Doc: Remote Parameter Updater for Cluster Train
For an overview of distribute training, please refer to [distributed training design doc](README.md). In this design doc, we will discuss the parameter updater that will use parameter server cclient [The Client Library of Parameter Server Design Doc](pserver_client.md) to manage and update parameters.
## Parameter Updater
Parameter Updater is used by trainer to manage and update parameter, there are mainly two kind of parameter updater: local and remote, since this design is for cluster train, we will only discuss remote parameter updater here.
### Remote Parameter Updater
Remote Parameter Updater manage parameters through remote parameter server with the client that communicate with pserver([The Client Library of Parameter Server Design Doc](pserver_client.md))
In PaddlePaddle Python V2 API, trainer is implemented in python, and the trainer will hold a instance of parameter updater and call it's functions directly. In this design, we will also expose the api of RemoteParameterUpdater to python with swig.
#### Sparse Remote Parameter Updater
Since we will only implement dense parameter management new, the mechanism for sparse parameter will be discussed in next stage.
### Interface Design
TBD
develop/doc_cn/api/v2/run_logic.html
浏览文件 @ ca79f901
...@@ -382,7 +382,7 @@ Trainer.train.</p> ...@@ -382,7 +382,7 @@ Trainer.train.</p>
<p>Module Trainer</p> <p>Module Trainer</p>
<dl class="class"> <dl class="class">
<dt> <dt>
<em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em>, <em>is_local=True</em><span class="sig-paren">)</span></dt> <em class="property">class </em><code class="descclassname">paddle.v2.trainer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>cost</em>, <em>parameters</em>, <em>update_equation</em>, <em>extra_layers=None</em>, <em>is_local=True</em>, <em>pserver_spec=None</em><span class="sig-paren">)</span></dt>
<dd><p>Simple SGD Trainer. <dd><p>Simple SGD Trainer.
SGD Trainer combines data reader, network topolopy and update_equation together SGD Trainer combines data reader, network topolopy and update_equation together
to train/test a neural network.</p> to train/test a neural network.</p>
......
develop/doc_cn/design/cluster_train/remote_parameter_updater.html 0 → 100644
浏览文件 @ ca79f901
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<li>Design Doc: Remote Parameter Updater for Cluster Train</li>
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<span id="design-doc-remote-parameter-updater-for-cluster-train"></span><h1>Design Doc: Remote Parameter Updater for Cluster Train<a class="headerlink" href="#design-doc-remote-parameter-updater-for-cluster-train" title="永久链接至标题"></a></h1>
<p>For an overview of distribute training, please refer to <a class="reference internal" href="README.html"><span class="doc">distributed training design doc</span></a>. In this design doc, we will discuss the parameter updater that will use parameter server cclient <a class="reference internal" href="pserver_client.html"><span class="doc">The Client Library of Parameter Server Design Doc</span></a> to manage and update parameters.</p>
<div class="section" id="parameter-updater">
<span id="parameter-updater"></span><h2>Parameter Updater<a class="headerlink" href="#parameter-updater" title="永久链接至标题"></a></h2>
<p>Parameter Updater is used by trainer to manage and update parameter, there are mainly two kind of parameter updater: local and remote, since this design is for cluster train, we will only discuss remote parameter updater here.</p>
<div class="section" id="remote-parameter-updater">
<span id="remote-parameter-updater"></span><h3>Remote Parameter Updater<a class="headerlink" href="#remote-parameter-updater" title="永久链接至标题"></a></h3>
<p>Remote Parameter Updater manage parameters through remote parameter server with the client that communicate with pserver(<a class="reference internal" href="pserver_client.html"><span class="doc">The Client Library of Parameter Server Design Doc</span></a>)</p>
<p>In PaddlePaddle Python V2 API, trainer is implemented in python, and the trainer will hold a instance of parameter updater and call it&#8217;s functions directly. In this design, we will also expose the api of RemoteParameterUpdater to python with swig.</p>
<div class="section" id="sparse-remote-parameter-updater">
<span id="sparse-remote-parameter-updater"></span><h4>Sparse Remote Parameter Updater<a class="headerlink" href="#sparse-remote-parameter-updater" title="永久链接至标题"></a></h4>
<p>Since we will only implement dense parameter management new, the mechanism for sparse parameter will be discussed in next stage.</p>
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<span id="interface-design"></span><h3>Interface Design<a class="headerlink" href="#interface-design" title="永久链接至标题"></a></h3>
<p>TBD</p>
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