Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
“86fd748808dee2448bf368f3b1389f91ec6e9d29”上不存在“develop/doc_cn/howto/usage/cmd_parameter”
提交 1ff93606 编写于 作者: T Travis CI
浏览文件
操作

Deploy to GitHub Pages: 45a78a4f

上级 8f6f6b5f
展开全部 显示空白变更内容
内联 并排
Showing with 920 addition and 2 deletion
develop/doc/_sources/design/cluster_train/save_model.md.txt 0 → 100644
浏览文件 @ 1ff93606
# Design Doc: Save Model
## Overview
The model is the output of the training process. There are two
ways from which user can obtain a model:
- Save model triggered by user code: user code asks PaddlePaddle to
save a model.
- Convert model from the checkpoint: model being converted from
pservers' periodic checkpoint. In this way, the user can cancel a
job at any time, and still have a relatively fresh model (we
checkpoint around every 5 minutes).
### Trainer Saving Model vs. Pservers Saving Model
Both trainers and pservers have access to the model. So the model can
be saved from a trainer or pservers. We need to decide where the model
is saved from.
#### Dense Update vs. Sparse Update
There are two types of model update methods: dense update and sparse
update (when the model parameter is configured to be sparse).
- Dense update
Every trainer has it's own full copy of the model. Every model
update will update the entire model.
- Sparse update
The training input is sparse, and the trainer does not have the
entire model. It will only download the sub-model necessary related
to the input. When updating the model, only the sub-model related to
the training input is updated.
#### Pservers Saving Model
The benefit of letting pservers save model is they have the entire
model all the time. However, since pservers are on different nodes, it
requires a merging process to merge model shards into the same
model. Thus requires the pservers to write models to a distributed
filesystem, making the checkpoint shards visible to the merge program.
#### Trainer Saving Model
The benefit of letting one trainer to save the model is it does not
require a distributed filesystem. And it's reusing the same save model
logic when training locally - except when doing sparse update, the
trainer needs to download the entire model during the saving process.
#### Conclusion
Given trainer saving model does not require a distributed filesystem,
and is an intuitive extension to trainer saving model when training
locally, we decide to let the trainer save the model when doing
distributed training.
### Convert Model from Checkpoint
TODO
## Timeline
We first implement trainer save the model. Converting the latest
snapshot to a model will be a TODO for future.
## Trainer Save Model
### Trainer Election
One trainer will be elected as the one to save the model. When using
etcd, trainer ID is a randomly generated UUID, we will utilize etcd to
elect one trainer. When not using etcd, unique trainer IDs will be
given by the administrator, the trainer whose ID is "0" is elected to
save the model.
### Model Save Path
Each trainer will be given the directory to save the model. The
elected trainer will save the model to
`given-directory/trainerID`. Since the trainer ID is unique, this
would prevent concurrent save to the same file when multiple trainers
are elected to save the model when split-brain problem happens.
### What Happens When Model Is Saving
It takes some time to save model, we need to define what will happen
when save model is taking place.
When doing dense update, the trainer uses the local model. Pservers
does not need to pause model update.
When doing sparse update. The trainer needs to download the entire
model while saving. To get the most accurate model, the model update
needs to be paused before the download starts and resumed after the
download finishes. Otherwise, the trainer gets a model that is
"polluted": some part of the model is old, some part of the model is
new.
It's unclear that the "polluted" model will be inferior due to the
stochastic nature of deep learning, and pausing the model update will
add more complexity to the system. Since supporting sparse update is a
TODO item. We defer the evaluation of pause the model update or not
during saving model to the future.
develop/doc/design/cluster_train/save_model.html 0 → 100644
浏览文件 @ 1ff93606
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Design Doc: Save Model &mdash; PaddlePaddle documentation</title>
<link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<link rel="index" title="Index"
href="../../genindex.html"/>
<link rel="search" title="Search" href="../../search.html"/>
<link rel="top" title="PaddlePaddle documentation" href="../../index.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/css/override.css" type="text/css" />
<script>
var _hmt = _hmt || [];
(function() {
var hm = document.createElement("script");
hm.src = "//hm.baidu.com/hm.js?b9a314ab40d04d805655aab1deee08ba";
var s = document.getElementsByTagName("script")[0];
s.parentNode.insertBefore(hm, s);
})();
</script>
<script src="../../_static/js/modernizr.min.js"></script>
</head>
<body class="wy-body-for-nav" role="document">
<header class="site-header">
<div class="site-logo">
<a href="/"><img src="../../_static/images/PP_w.png"></a>
</div>
<div class="site-nav-links">
<div class="site-menu">
<a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Folk me on Github</a>
<div class="language-switcher dropdown">
<a type="button" data-toggle="dropdown">
<span>English</span>
<i class="fa fa-angle-up"></i>
<i class="fa fa-angle-down"></i>
</a>
<ul class="dropdown-menu">
<li><a href="/doc_cn">中文</a></li>
<li><a href="/doc">English</a></li>
</ul>
</div>
<ul class="site-page-links">
<li><a href="/">Home</a></li>
</ul>
</div>
<div class="doc-module">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_en.html">GET STARTED</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_en.html">HOW TO</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../about/index_en.html">ABOUT</a></li>
</ul>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
<input type="text" name="q" placeholder="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
</div>
</header>
<div class="main-content-wrap">
<nav class="doc-menu-vertical" role="navigation">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_en.html">GET STARTED</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/build_and_install/index_en.html">Install and Build</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/docker_install_en.html">PaddlePaddle in Docker Containers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/ubuntu_install_en.html">Debian Package installation guide</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/build_from_source_en.html">Installing from Sources</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_en.html">HOW TO</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cmd_parameter/index_en.html">Set Command-line Parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/use_case_en.html">Use Case</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/arguments_en.html">Argument Outline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/detail_introduction_en.html">Detail Description</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_en.html">Run Distributed Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_en.html">Paddle On Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_aws_en.html">Distributed PaddlePaddle Training on AWS with Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/new_layer_en.html">Write New Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/contribute_to_paddle_en.html">Contribute Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/deep_model/rnn/index_en.html">RNN Models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/rnn_config_en.html">RNN Configuration</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/optimization/gpu_profiling_en.html">Tune GPU Performance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">Data Reader Interface and DataSets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/run_logic.html">Training and Inference</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../about/index_en.html">ABOUT</a></li>
</ul>
</nav>
<section class="doc-content-wrap">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li>Design Doc: Save Model</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-save-model">
<span id="design-doc-save-model"></span><h1>Design Doc: Save Model<a class="headerlink" href="#design-doc-save-model" title="Permalink to this headline"></a></h1>
<div class="section" id="overview">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline"></a></h2>
<p>The model is the output of the training process. There are two
ways from which user can obtain a model:</p>
<ul class="simple">
<li>Save model triggered by user code: user code asks PaddlePaddle to
save a model.</li>
<li>Convert model from the checkpoint: model being converted from
pservers&#8217; periodic checkpoint. In this way, the user can cancel a
job at any time, and still have a relatively fresh model (we
checkpoint around every 5 minutes).</li>
</ul>
<div class="section" id="trainer-saving-model-vs-pservers-saving-model">
<span id="trainer-saving-model-vs-pservers-saving-model"></span><h3>Trainer Saving Model vs. Pservers Saving Model<a class="headerlink" href="#trainer-saving-model-vs-pservers-saving-model" title="Permalink to this headline"></a></h3>
<p>Both trainers and pservers have access to the model. So the model can
be saved from a trainer or pservers. We need to decide where the model
is saved from.</p>
<div class="section" id="dense-update-vs-sparse-update">
<span id="dense-update-vs-sparse-update"></span><h4>Dense Update vs. Sparse Update<a class="headerlink" href="#dense-update-vs-sparse-update" title="Permalink to this headline"></a></h4>
<p>There are two types of model update methods: dense update and sparse
update (when the model parameter is configured to be sparse).</p>
<ul>
<li><p class="first">Dense update</p>
<p>Every trainer has it&#8217;s own full copy of the model. Every model
update will update the entire model.</p>
</li>
<li><p class="first">Sparse update</p>
<p>The training input is sparse, and the trainer does not have the
entire model. It will only download the sub-model necessary related
to the input. When updating the model, only the sub-model related to
the training input is updated.</p>
</li>
</ul>
</div>
<div class="section" id="pservers-saving-model">
<span id="pservers-saving-model"></span><h4>Pservers Saving Model<a class="headerlink" href="#pservers-saving-model" title="Permalink to this headline"></a></h4>
<p>The benefit of letting pservers save model is they have the entire
model all the time. However, since pservers are on different nodes, it
requires a merging process to merge model shards into the same
model. Thus requires the pservers to write models to a distributed
filesystem, making the checkpoint shards visible to the merge program.</p>
</div>
<div class="section" id="trainer-saving-model">
<span id="trainer-saving-model"></span><h4>Trainer Saving Model<a class="headerlink" href="#trainer-saving-model" title="Permalink to this headline"></a></h4>
<p>The benefit of letting one trainer to save the model is it does not
require a distributed filesystem. And it&#8217;s reusing the same save model
logic when training locally - except when doing sparse update, the
trainer needs to download the entire model during the saving process.</p>
</div>
<div class="section" id="conclusion">
<span id="conclusion"></span><h4>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline"></a></h4>
<p>Given trainer saving model does not require a distributed filesystem,
and is an intuitive extension to trainer saving model when training
locally, we decide to let the trainer save the model when doing
distributed training.</p>
</div>
</div>
<div class="section" id="convert-model-from-checkpoint">
<span id="convert-model-from-checkpoint"></span><h3>Convert Model from Checkpoint<a class="headerlink" href="#convert-model-from-checkpoint" title="Permalink to this headline"></a></h3>
<p>TODO</p>
</div>
</div>
<div class="section" id="timeline">
<span id="timeline"></span><h2>Timeline<a class="headerlink" href="#timeline" title="Permalink to this headline"></a></h2>
<p>We first implement trainer save the model. Converting the latest
snapshot to a model will be a TODO for future.</p>
</div>
<div class="section" id="trainer-save-model">
<span id="trainer-save-model"></span><h2>Trainer Save Model<a class="headerlink" href="#trainer-save-model" title="Permalink to this headline"></a></h2>
<div class="section" id="trainer-election">
<span id="trainer-election"></span><h3>Trainer Election<a class="headerlink" href="#trainer-election" title="Permalink to this headline"></a></h3>
<p>One trainer will be elected as the one to save the model. When using
etcd, trainer ID is a randomly generated UUID, we will utilize etcd to
elect one trainer. When not using etcd, unique trainer IDs will be
given by the administrator, the trainer whose ID is &#8220;0&#8221; is elected to
save the model.</p>
</div>
<div class="section" id="model-save-path">
<span id="model-save-path"></span><h3>Model Save Path<a class="headerlink" href="#model-save-path" title="Permalink to this headline"></a></h3>
<p>Each trainer will be given the directory to save the model. The
elected trainer will save the model to
<code class="docutils literal"><span class="pre">given-directory/trainerID</span></code>. Since the trainer ID is unique, this
would prevent concurrent save to the same file when multiple trainers
are elected to save the model when split-brain problem happens.</p>
</div>
<div class="section" id="what-happens-when-model-is-saving">
<span id="what-happens-when-model-is-saving"></span><h3>What Happens When Model Is Saving<a class="headerlink" href="#what-happens-when-model-is-saving" title="Permalink to this headline"></a></h3>
<p>It takes some time to save model, we need to define what will happen
when save model is taking place.</p>
<p>When doing dense update, the trainer uses the local model. Pservers
does not need to pause model update.</p>
<p>When doing sparse update. The trainer needs to download the entire
model while saving. To get the most accurate model, the model update
needs to be paused before the download starts and resumed after the
download finishes. Otherwise, the trainer gets a model that is
&#8220;polluted&#8221;: some part of the model is old, some part of the model is
new.</p>
<p>It&#8217;s unclear that the &#8220;polluted&#8221; model will be inferior due to the
stochastic nature of deep learning, and pausing the model update will
add more complexity to the system. Since supporting sparse update is a
TODO item. We defer the evaluation of pause the model update or not
during saving model to the future.</p>
</div>
</div>
</div>
</div>
</div>
<footer>
<hr/>
<div role="contentinfo">
<p>
&copy; Copyright 2016, PaddlePaddle developers.
</p>
</div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../../',
VERSION:'',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: ".txt",
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/javascript" src="../../_static/js/theme.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/js/perfect-scrollbar.jquery.min.js"></script>
<script src="../../_static/js/paddle_doc_init.js"></script>
</body>
</html>
\ No newline at end of file
develop/doc/searchindex.js
浏览文件 @ 1ff93606
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
develop/doc_cn/_sources/design/cluster_train/save_model.md.txt 0 → 100644
浏览文件 @ 1ff93606
# Design Doc: Save Model
## Overview
The model is the output of the training process. There are two
ways from which user can obtain a model:
- Save model triggered by user code: user code asks PaddlePaddle to
save a model.
- Convert model from the checkpoint: model being converted from
pservers' periodic checkpoint. In this way, the user can cancel a
job at any time, and still have a relatively fresh model (we
checkpoint around every 5 minutes).
### Trainer Saving Model vs. Pservers Saving Model
Both trainers and pservers have access to the model. So the model can
be saved from a trainer or pservers. We need to decide where the model
is saved from.
#### Dense Update vs. Sparse Update
There are two types of model update methods: dense update and sparse
update (when the model parameter is configured to be sparse).
- Dense update
Every trainer has it's own full copy of the model. Every model
update will update the entire model.
- Sparse update
The training input is sparse, and the trainer does not have the
entire model. It will only download the sub-model necessary related
to the input. When updating the model, only the sub-model related to
the training input is updated.
#### Pservers Saving Model
The benefit of letting pservers save model is they have the entire
model all the time. However, since pservers are on different nodes, it
requires a merging process to merge model shards into the same
model. Thus requires the pservers to write models to a distributed
filesystem, making the checkpoint shards visible to the merge program.
#### Trainer Saving Model
The benefit of letting one trainer to save the model is it does not
require a distributed filesystem. And it's reusing the same save model
logic when training locally - except when doing sparse update, the
trainer needs to download the entire model during the saving process.
#### Conclusion
Given trainer saving model does not require a distributed filesystem,
and is an intuitive extension to trainer saving model when training
locally, we decide to let the trainer save the model when doing
distributed training.
### Convert Model from Checkpoint
TODO
## Timeline
We first implement trainer save the model. Converting the latest
snapshot to a model will be a TODO for future.
## Trainer Save Model
### Trainer Election
One trainer will be elected as the one to save the model. When using
etcd, trainer ID is a randomly generated UUID, we will utilize etcd to
elect one trainer. When not using etcd, unique trainer IDs will be
given by the administrator, the trainer whose ID is "0" is elected to
save the model.
### Model Save Path
Each trainer will be given the directory to save the model. The
elected trainer will save the model to
`given-directory/trainerID`. Since the trainer ID is unique, this
would prevent concurrent save to the same file when multiple trainers
are elected to save the model when split-brain problem happens.
### What Happens When Model Is Saving
It takes some time to save model, we need to define what will happen
when save model is taking place.
When doing dense update, the trainer uses the local model. Pservers
does not need to pause model update.
When doing sparse update. The trainer needs to download the entire
model while saving. To get the most accurate model, the model update
needs to be paused before the download starts and resumed after the
download finishes. Otherwise, the trainer gets a model that is
"polluted": some part of the model is old, some part of the model is
new.
It's unclear that the "polluted" model will be inferior due to the
stochastic nature of deep learning, and pausing the model update will
add more complexity to the system. Since supporting sparse update is a
TODO item. We defer the evaluation of pause the model update or not
during saving model to the future.
develop/doc_cn/design/cluster_train/save_model.html 0 → 100644
浏览文件 @ 1ff93606
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Design Doc: Save Model &mdash; PaddlePaddle 文档</title>
<link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<link rel="index" title="索引"
href="../../genindex.html"/>
<link rel="search" title="搜索" href="../../search.html"/>
<link rel="top" title="PaddlePaddle 文档" href="../../index.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
<link rel="stylesheet" href="../../_static/css/override.css" type="text/css" />
<script>
var _hmt = _hmt || [];
(function() {
var hm = document.createElement("script");
hm.src = "//hm.baidu.com/hm.js?b9a314ab40d04d805655aab1deee08ba";
var s = document.getElementsByTagName("script")[0];
s.parentNode.insertBefore(hm, s);
})();
</script>
<script src="../../_static/js/modernizr.min.js"></script>
</head>
<body class="wy-body-for-nav" role="document">
<header class="site-header">
<div class="site-logo">
<a href="/"><img src="../../_static/images/PP_w.png"></a>
</div>
<div class="site-nav-links">
<div class="site-menu">
<a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Folk me on Github</a>
<div class="language-switcher dropdown">
<a type="button" data-toggle="dropdown">
<span>English</span>
<i class="fa fa-angle-up"></i>
<i class="fa fa-angle-down"></i>
</a>
<ul class="dropdown-menu">
<li><a href="/doc_cn">中文</a></li>
<li><a href="/doc">English</a></li>
</ul>
</div>
<ul class="site-page-links">
<li><a href="/">Home</a></li>
</ul>
</div>
<div class="doc-module">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_cn.html">新手入门</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_cn.html">进阶指南</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_cn.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../faq/index_cn.html">FAQ</a></li>
</ul>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
<input type="text" name="q" placeholder="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
</div>
</header>
<div class="main-content-wrap">
<nav class="doc-menu-vertical" role="navigation">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../getstarted/index_cn.html">新手入门</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/build_and_install/index_cn.html">安装与编译</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/docker_install_cn.html">PaddlePaddle的Docker容器使用方式</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/ubuntu_install_cn.html">Ubuntu部署PaddlePaddle</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/cmake/build_from_source_cn.html">PaddlePaddle的编译选项</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../howto/index_cn.html">进阶指南</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cmd_parameter/index_cn.html">设置命令行参数</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/use_case_cn.html">使用案例</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/arguments_cn.html">参数概述</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/cluster/cluster_train_cn.html">运行分布式训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_basis_cn.html">Kubernetes 简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_cn.html">Kubernetes单机训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/usage/k8s/k8s_distributed_cn.html">Kubernetes分布式训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/write_docs_cn.html">如何贡献/修改文档</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/dev/contribute_to_paddle_cn.html">如何贡献代码</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/deep_model/rnn/index_cn.html">RNN相关模型</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/rnn_config_cn.html">RNN配置</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/recurrent_group_cn.html">Recurrent Group教程</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/hierarchical_layer_cn.html">支持双层序列作为输入的Layer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html">单双层RNN API对比介绍</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../howto/optimization/gpu_profiling_cn.html">GPU性能分析与调优</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_cn.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">模型配置</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/layer.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/evaluators.html">Evaluators</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/data.html">数据访问</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/run_logic.html">训练与应用</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../faq/index_cn.html">FAQ</a></li>
</ul>
</nav>
<section class="doc-content-wrap">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li>Design Doc: Save Model</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-save-model">
<span id="design-doc-save-model"></span><h1>Design Doc: Save Model<a class="headerlink" href="#design-doc-save-model" title="永久链接至标题"></a></h1>
<div class="section" id="overview">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="永久链接至标题"></a></h2>
<p>The model is the output of the training process. There are two
ways from which user can obtain a model:</p>
<ul class="simple">
<li>Save model triggered by user code: user code asks PaddlePaddle to
save a model.</li>
<li>Convert model from the checkpoint: model being converted from
pservers&#8217; periodic checkpoint. In this way, the user can cancel a
job at any time, and still have a relatively fresh model (we
checkpoint around every 5 minutes).</li>
</ul>
<div class="section" id="trainer-saving-model-vs-pservers-saving-model">
<span id="trainer-saving-model-vs-pservers-saving-model"></span><h3>Trainer Saving Model vs. Pservers Saving Model<a class="headerlink" href="#trainer-saving-model-vs-pservers-saving-model" title="永久链接至标题"></a></h3>
<p>Both trainers and pservers have access to the model. So the model can
be saved from a trainer or pservers. We need to decide where the model
is saved from.</p>
<div class="section" id="dense-update-vs-sparse-update">
<span id="dense-update-vs-sparse-update"></span><h4>Dense Update vs. Sparse Update<a class="headerlink" href="#dense-update-vs-sparse-update" title="永久链接至标题"></a></h4>
<p>There are two types of model update methods: dense update and sparse
update (when the model parameter is configured to be sparse).</p>
<ul>
<li><p class="first">Dense update</p>
<p>Every trainer has it&#8217;s own full copy of the model. Every model
update will update the entire model.</p>
</li>
<li><p class="first">Sparse update</p>
<p>The training input is sparse, and the trainer does not have the
entire model. It will only download the sub-model necessary related
to the input. When updating the model, only the sub-model related to
the training input is updated.</p>
</li>
</ul>
</div>
<div class="section" id="pservers-saving-model">
<span id="pservers-saving-model"></span><h4>Pservers Saving Model<a class="headerlink" href="#pservers-saving-model" title="永久链接至标题"></a></h4>
<p>The benefit of letting pservers save model is they have the entire
model all the time. However, since pservers are on different nodes, it
requires a merging process to merge model shards into the same
model. Thus requires the pservers to write models to a distributed
filesystem, making the checkpoint shards visible to the merge program.</p>
</div>
<div class="section" id="trainer-saving-model">
<span id="trainer-saving-model"></span><h4>Trainer Saving Model<a class="headerlink" href="#trainer-saving-model" title="永久链接至标题"></a></h4>
<p>The benefit of letting one trainer to save the model is it does not
require a distributed filesystem. And it&#8217;s reusing the same save model
logic when training locally - except when doing sparse update, the
trainer needs to download the entire model during the saving process.</p>
</div>
<div class="section" id="conclusion">
<span id="conclusion"></span><h4>Conclusion<a class="headerlink" href="#conclusion" title="永久链接至标题"></a></h4>
<p>Given trainer saving model does not require a distributed filesystem,
and is an intuitive extension to trainer saving model when training
locally, we decide to let the trainer save the model when doing
distributed training.</p>
</div>
</div>
<div class="section" id="convert-model-from-checkpoint">
<span id="convert-model-from-checkpoint"></span><h3>Convert Model from Checkpoint<a class="headerlink" href="#convert-model-from-checkpoint" title="永久链接至标题"></a></h3>
<p>TODO</p>
</div>
</div>
<div class="section" id="timeline">
<span id="timeline"></span><h2>Timeline<a class="headerlink" href="#timeline" title="永久链接至标题"></a></h2>
<p>We first implement trainer save the model. Converting the latest
snapshot to a model will be a TODO for future.</p>
</div>
<div class="section" id="trainer-save-model">
<span id="trainer-save-model"></span><h2>Trainer Save Model<a class="headerlink" href="#trainer-save-model" title="永久链接至标题"></a></h2>
<div class="section" id="trainer-election">
<span id="trainer-election"></span><h3>Trainer Election<a class="headerlink" href="#trainer-election" title="永久链接至标题"></a></h3>
<p>One trainer will be elected as the one to save the model. When using
etcd, trainer ID is a randomly generated UUID, we will utilize etcd to
elect one trainer. When not using etcd, unique trainer IDs will be
given by the administrator, the trainer whose ID is &#8220;0&#8221; is elected to
save the model.</p>
</div>
<div class="section" id="model-save-path">
<span id="model-save-path"></span><h3>Model Save Path<a class="headerlink" href="#model-save-path" title="永久链接至标题"></a></h3>
<p>Each trainer will be given the directory to save the model. The
elected trainer will save the model to
<code class="docutils literal"><span class="pre">given-directory/trainerID</span></code>. Since the trainer ID is unique, this
would prevent concurrent save to the same file when multiple trainers
are elected to save the model when split-brain problem happens.</p>
</div>
<div class="section" id="what-happens-when-model-is-saving">
<span id="what-happens-when-model-is-saving"></span><h3>What Happens When Model Is Saving<a class="headerlink" href="#what-happens-when-model-is-saving" title="永久链接至标题"></a></h3>
<p>It takes some time to save model, we need to define what will happen
when save model is taking place.</p>
<p>When doing dense update, the trainer uses the local model. Pservers
does not need to pause model update.</p>
<p>When doing sparse update. The trainer needs to download the entire
model while saving. To get the most accurate model, the model update
needs to be paused before the download starts and resumed after the
download finishes. Otherwise, the trainer gets a model that is
&#8220;polluted&#8221;: some part of the model is old, some part of the model is
new.</p>
<p>It&#8217;s unclear that the &#8220;polluted&#8221; model will be inferior due to the
stochastic nature of deep learning, and pausing the model update will
add more complexity to the system. Since supporting sparse update is a
TODO item. We defer the evaluation of pause the model update or not
during saving model to the future.</p>
</div>
</div>
</div>
</div>
</div>
<footer>
<hr/>
<div role="contentinfo">
<p>
&copy; Copyright 2016, PaddlePaddle developers.
</p>
</div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT:'../../',
VERSION:'',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: ".txt",
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/translations.js"></script>
<script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js"></script>
<script type="text/javascript" src="../../_static/js/theme.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/js/perfect-scrollbar.jquery.min.js"></script>
<script src="../../_static/js/paddle_doc_init.js"></script>
</body>
</html>
\ No newline at end of file
develop/doc_cn/searchindex.js
浏览文件 @ 1ff93606
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或