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# Design Doc: CSP in PaddlePaddle Fluid
## Motivation
Concurrent programming is important for deep learning. Few example applications are:
1. The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.
2. The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.
Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn't have the concept of a graph at all, as the design goal of Fluid is that of a programming language.
## Concurrent Programming Models
There were many concurrent programming models, implemented in various forms:
| concurrent programming model | implementation |
|-----|-----|
| mutex | types and functions in standard libraries |
| semaphore | types and functions in standard libraries |
| communicating sequential processes (CSP) | Go programming language |
| actor model | Erlang programming language |
| message passing | MPI |
| bulk synchronous parallel (BSP) | Pregel distributed programming framework |
Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.
### CSP v.s. Actor Model
A well-known implementation of Actor Model is the Erlang programming language. In Actor Model, *processes* could send messages to another process and receive messages from another process given the process IDs. We can find the three ingredients, process with ID, send, and recv, in MPI too. Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code. Our concern with Actor Model is that it doesn't seem reasonable to implement process management in a programming language's runtime library; instead, it should be the operating systems' responsibility to manage processes and libraries like MPI for send/recv.
## CSP in Fluid
Fluid has two fundamental control-flows: *if-else* and *while*. If we are to implement CSP, we need the following:
1. a new data type: *channel* and operators *send* and *recv*,
1. *goroutine* or thread, and
1. a new control-flow: select.
We also need Python wrappers for the above components.
The type *channel* is conceptually the blocking queue. In Go, its implemented is a [blocking circular queue](https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50), which supports send and recv.
The `select` operation has been in OS kernels long before Go language. All Unix kernels implement system calls *poll* and *select*. They monitor multiple file descriptors to see if I/O is possible on any of them. This takes O(N) time. Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time. In BSD systems, there is a similar system call *kqueue*. Go's Linux implementation uses epoll.
It might be a good idea to implement Fluid's select using epoll too. In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
### Type Channel
Fluid supports many data types:
1. Tensor,
1. Row-sparse Tensor
1. LoD Tensor,
1. Tensor array, etc
Each data type is registered in the [`framework.proto`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127) as an enum value. To add a new type channel, we need to add a new type enum.
To expose a C++ type to Python, we need to edit the [`pybind.cc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) file. [Here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164) is an example how we expose C++ class LoDTensor.
## Syntax Design
### Create Channel
In Go, we create a channel by specifying the element type and buffer size:
```go
ch := make(chan int) // a channel without buffer
ch1 := make(chan int, 100) // a channel that can buffer 100 ints.
```
In Fluid, we should be able to do the same:
```python
ch = fluid.make_chan(dtype=INT)
ch1 = fluid.make_chan(dtype=INT, 100)
```
In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
```python
ch = fluid.make_chan(dtype=Tensor, etype=float16)
```
or Tensors of Tensors of float16 etc.
The point here is that we need a consistent way to compose types, like in C++ we can have `Tensor<Tensor<...<float16>...> >`.
### Send and Recv
### Select
## Example Programs
### 1. RPC between Trainers and Parameter Servers
### 2. Concurrent Minibatch Loading
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<span id="design-doc-csp-in-paddlepaddle-fluid"></span><h1>Design Doc: CSP in PaddlePaddle Fluid<a class="headerlink" href="#design-doc-csp-in-paddlepaddle-fluid" title="Permalink to this headline"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this headline"></a></h2>
<p>Concurrent programming is important for deep learning. Few example applications are:</p>
<ol class="simple">
<li>The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.</li>
<li>The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.</li>
</ol>
<p>Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn&#8217;t have the concept of a graph at all, as the design goal of Fluid is that of a programming language.</p>
</div>
<div class="section" id="concurrent-programming-models">
<span id="concurrent-programming-models"></span><h2>Concurrent Programming Models<a class="headerlink" href="#concurrent-programming-models" title="Permalink to this headline"></a></h2>
<p>There were many concurrent programming models, implemented in various forms:</p>
<p>| concurrent programming model | implementation |
|&#8212;&#8211;|&#8212;&#8211;|
| mutex | types and functions in standard libraries |
| semaphore | types and functions in standard libraries |
| communicating sequential processes (CSP) | Go programming language |
| actor model | Erlang programming language |
| message passing | MPI |
| bulk synchronous parallel (BSP) | Pregel distributed programming framework |</p>
<p>Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.</p>
<div class="section" id="csp-v-s-actor-model">
<span id="csp-v-s-actor-model"></span><h3>CSP v.s. Actor Model<a class="headerlink" href="#csp-v-s-actor-model" title="Permalink to this headline"></a></h3>
<p>A well-known implementation of Actor Model is the Erlang programming language. In Actor Model, <em>processes</em> could send messages to another process and receive messages from another process given the process IDs. We can find the three ingredients, process with ID, send, and recv, in MPI too. Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code. Our concern with Actor Model is that it doesn&#8217;t seem reasonable to implement process management in a programming language&#8217;s runtime library; instead, it should be the operating systems&#8217; responsibility to manage processes and libraries like MPI for send/recv.</p>
</div>
</div>
<div class="section" id="csp-in-fluid">
<span id="csp-in-fluid"></span><h2>CSP in Fluid<a class="headerlink" href="#csp-in-fluid" title="Permalink to this headline"></a></h2>
<p>Fluid has two fundamental control-flows: <em>if-else</em> and <em>while</em>. If we are to implement CSP, we need the following:</p>
<ol class="simple">
<li>a new data type: <em>channel</em> and operators <em>send</em> and <em>recv</em>,</li>
<li><em>goroutine</em> or thread, and</li>
<li>a new control-flow: select.</li>
</ol>
<p>We also need Python wrappers for the above components.</p>
<p>The type <em>channel</em> is conceptually the blocking queue. In Go, its implemented is a <a class="reference external" href="https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50">blocking circular queue</a>, which supports send and recv.</p>
<p>The <code class="docutils literal"><span class="pre">select</span></code> operation has been in OS kernels long before Go language. All Unix kernels implement system calls <em>poll</em> and <em>select</em>. They monitor multiple file descriptors to see if I/O is possible on any of them. This takes O(N) time. Since Linux 2.6, a new system call, <em>epoll</em>, can do the same in O(1) time. In BSD systems, there is a similar system call <em>kqueue</em>. Go&#8217;s Linux implementation uses epoll.</p>
<p>It might be a good idea to implement Fluid&#8217;s select using epoll too. In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.</p>
<div class="section" id="type-channel">
<span id="type-channel"></span><h3>Type Channel<a class="headerlink" href="#type-channel" title="Permalink to this headline"></a></h3>
<p>Fluid supports many data types:</p>
<ol class="simple">
<li>Tensor,</li>
<li>Row-sparse Tensor</li>
<li>LoD Tensor,</li>
<li>Tensor array, etc</li>
</ol>
<p>Each data type is registered in the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127"><code class="docutils literal"><span class="pre">framework.proto</span></code></a> as an enum value. To add a new type channel, we need to add a new type enum.</p>
<p>To expose a C++ type to Python, we need to edit the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc"><code class="docutils literal"><span class="pre">pybind.cc</span></code></a> file. <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164">Here</a> is an example how we expose C++ class LoDTensor.</p>
</div>
</div>
<div class="section" id="syntax-design">
<span id="syntax-design"></span><h2>Syntax Design<a class="headerlink" href="#syntax-design" title="Permalink to this headline"></a></h2>
<div class="section" id="create-channel">
<span id="create-channel"></span><h3>Create Channel<a class="headerlink" href="#create-channel" title="Permalink to this headline"></a></h3>
<p>In Go, we create a channel by specifying the element type and buffer size:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="nx">ch</span> <span class="o">:=</span> <span class="nb">make</span><span class="p">(</span><span class="kd">chan</span> <span class="kt">int</span><span class="p">)</span> <span class="c1">// a channel without buffer</span>
<span class="nx">ch1</span> <span class="o">:=</span> <span class="nb">make</span><span class="p">(</span><span class="kd">chan</span> <span class="kt">int</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="c1">// a channel that can buffer 100 ints.</span>
</pre></div>
</div>
<p>In Fluid, we should be able to do the same:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ch</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">INT</span><span class="p">)</span>
<span class="n">ch1</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">INT</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
<p>In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ch</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">etype</span><span class="o">=</span><span class="n">float16</span><span class="p">)</span>
</pre></div>
</div>
<p>or Tensors of Tensors of float16 etc.</p>
<p>The point here is that we need a consistent way to compose types, like in C++ we can have <code class="docutils literal"><span class="pre">Tensor&lt;Tensor&lt;...&lt;float16&gt;...&gt;</span> <span class="pre">&gt;</span></code>.</p>
</div>
<div class="section" id="send-and-recv">
<span id="send-and-recv"></span><h3>Send and Recv<a class="headerlink" href="#send-and-recv" title="Permalink to this headline"></a></h3>
</div>
<div class="section" id="select">
<span id="select"></span><h3>Select<a class="headerlink" href="#select" title="Permalink to this headline"></a></h3>
</div>
</div>
<div class="section" id="example-programs">
<span id="example-programs"></span><h2>Example Programs<a class="headerlink" href="#example-programs" title="Permalink to this headline"></a></h2>
<div class="section" id="rpc-between-trainers-and-parameter-servers">
<span id="rpc-between-trainers-and-parameter-servers"></span><h3>1. RPC between Trainers and Parameter Servers<a class="headerlink" href="#rpc-between-trainers-and-parameter-servers" title="Permalink to this headline"></a></h3>
</div>
<div class="section" id="concurrent-minibatch-loading">
<span id="concurrent-minibatch-loading"></span><h3>2. Concurrent Minibatch Loading<a class="headerlink" href="#concurrent-minibatch-loading" title="Permalink to this headline"></a></h3>
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# Design Doc: CSP in PaddlePaddle Fluid
## Motivation
Concurrent programming is important for deep learning. Few example applications are:
1. The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.
2. The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.
Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn't have the concept of a graph at all, as the design goal of Fluid is that of a programming language.
## Concurrent Programming Models
There were many concurrent programming models, implemented in various forms:
| concurrent programming model | implementation |
|-----|-----|
| mutex | types and functions in standard libraries |
| semaphore | types and functions in standard libraries |
| communicating sequential processes (CSP) | Go programming language |
| actor model | Erlang programming language |
| message passing | MPI |
| bulk synchronous parallel (BSP) | Pregel distributed programming framework |
Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.
### CSP v.s. Actor Model
A well-known implementation of Actor Model is the Erlang programming language. In Actor Model, *processes* could send messages to another process and receive messages from another process given the process IDs. We can find the three ingredients, process with ID, send, and recv, in MPI too. Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code. Our concern with Actor Model is that it doesn't seem reasonable to implement process management in a programming language's runtime library; instead, it should be the operating systems' responsibility to manage processes and libraries like MPI for send/recv.
## CSP in Fluid
Fluid has two fundamental control-flows: *if-else* and *while*. If we are to implement CSP, we need the following:
1. a new data type: *channel* and operators *send* and *recv*,
1. *goroutine* or thread, and
1. a new control-flow: select.
We also need Python wrappers for the above components.
The type *channel* is conceptually the blocking queue. In Go, its implemented is a [blocking circular queue](https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50), which supports send and recv.
The `select` operation has been in OS kernels long before Go language. All Unix kernels implement system calls *poll* and *select*. They monitor multiple file descriptors to see if I/O is possible on any of them. This takes O(N) time. Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time. In BSD systems, there is a similar system call *kqueue*. Go's Linux implementation uses epoll.
It might be a good idea to implement Fluid's select using epoll too. In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
### Type Channel
Fluid supports many data types:
1. Tensor,
1. Row-sparse Tensor
1. LoD Tensor,
1. Tensor array, etc
Each data type is registered in the [`framework.proto`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127) as an enum value. To add a new type channel, we need to add a new type enum.
To expose a C++ type to Python, we need to edit the [`pybind.cc`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) file. [Here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164) is an example how we expose C++ class LoDTensor.
## Syntax Design
### Create Channel
In Go, we create a channel by specifying the element type and buffer size:
```go
ch := make(chan int) // a channel without buffer
ch1 := make(chan int, 100) // a channel that can buffer 100 ints.
```
In Fluid, we should be able to do the same:
```python
ch = fluid.make_chan(dtype=INT)
ch1 = fluid.make_chan(dtype=INT, 100)
```
In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
```python
ch = fluid.make_chan(dtype=Tensor, etype=float16)
```
or Tensors of Tensors of float16 etc.
The point here is that we need a consistent way to compose types, like in C++ we can have `Tensor<Tensor<...<float16>...> >`.
### Send and Recv
### Select
## Example Programs
### 1. RPC between Trainers and Parameter Servers
### 2. Concurrent Minibatch Loading
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<span id="design-doc-csp-in-paddlepaddle-fluid"></span><h1>Design Doc: CSP in PaddlePaddle Fluid<a class="headerlink" href="#design-doc-csp-in-paddlepaddle-fluid" title="永久链接至标题"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="永久链接至标题"></a></h2>
<p>Concurrent programming is important for deep learning. Few example applications are:</p>
<ol class="simple">
<li>The main thread keeps reading the next mini-batch while another thread uses the GPU for computing.</li>
<li>The main thread performs the computation while another thread uploads the local gradients from each trainer to the parameter server.</li>
</ol>
<p>Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously execute operators in a graph. However, Fluid doesn&#8217;t have the concept of a graph at all, as the design goal of Fluid is that of a programming language.</p>
</div>
<div class="section" id="concurrent-programming-models">
<span id="concurrent-programming-models"></span><h2>Concurrent Programming Models<a class="headerlink" href="#concurrent-programming-models" title="永久链接至标题"></a></h2>
<p>There were many concurrent programming models, implemented in various forms:</p>
<p>| concurrent programming model | implementation |
|&#8212;&#8211;|&#8212;&#8211;|
| mutex | types and functions in standard libraries |
| semaphore | types and functions in standard libraries |
| communicating sequential processes (CSP) | Go programming language |
| actor model | Erlang programming language |
| message passing | MPI |
| bulk synchronous parallel (BSP) | Pregel distributed programming framework |</p>
<p>Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid.</p>
<div class="section" id="csp-v-s-actor-model">
<span id="csp-v-s-actor-model"></span><h3>CSP v.s. Actor Model<a class="headerlink" href="#csp-v-s-actor-model" title="永久链接至标题"></a></h3>
<p>A well-known implementation of Actor Model is the Erlang programming language. In Actor Model, <em>processes</em> could send messages to another process and receive messages from another process given the process IDs. We can find the three ingredients, process with ID, send, and recv, in MPI too. Indeed, we can rewrite Erlang programs in Python + MPI with possibly fewer lines of code. Our concern with Actor Model is that it doesn&#8217;t seem reasonable to implement process management in a programming language&#8217;s runtime library; instead, it should be the operating systems&#8217; responsibility to manage processes and libraries like MPI for send/recv.</p>
</div>
</div>
<div class="section" id="csp-in-fluid">
<span id="csp-in-fluid"></span><h2>CSP in Fluid<a class="headerlink" href="#csp-in-fluid" title="永久链接至标题"></a></h2>
<p>Fluid has two fundamental control-flows: <em>if-else</em> and <em>while</em>. If we are to implement CSP, we need the following:</p>
<ol class="simple">
<li>a new data type: <em>channel</em> and operators <em>send</em> and <em>recv</em>,</li>
<li><em>goroutine</em> or thread, and</li>
<li>a new control-flow: select.</li>
</ol>
<p>We also need Python wrappers for the above components.</p>
<p>The type <em>channel</em> is conceptually the blocking queue. In Go, its implemented is a <a class="reference external" href="https://github.com/golang/go/blob/68ce117cf17b8debf5754bfd476345779b5b6616/src/runtime/chan.go#L31-L50">blocking circular queue</a>, which supports send and recv.</p>
<p>The <code class="docutils literal"><span class="pre">select</span></code> operation has been in OS kernels long before Go language. All Unix kernels implement system calls <em>poll</em> and <em>select</em>. They monitor multiple file descriptors to see if I/O is possible on any of them. This takes O(N) time. Since Linux 2.6, a new system call, <em>epoll</em>, can do the same in O(1) time. In BSD systems, there is a similar system call <em>kqueue</em>. Go&#8217;s Linux implementation uses epoll.</p>
<p>It might be a good idea to implement Fluid&#8217;s select using epoll too. In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.</p>
<div class="section" id="type-channel">
<span id="type-channel"></span><h3>Type Channel<a class="headerlink" href="#type-channel" title="永久链接至标题"></a></h3>
<p>Fluid supports many data types:</p>
<ol class="simple">
<li>Tensor,</li>
<li>Row-sparse Tensor</li>
<li>LoD Tensor,</li>
<li>Tensor array, etc</li>
</ol>
<p>Each data type is registered in the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L117-L127"><code class="docutils literal"><span class="pre">framework.proto</span></code></a> as an enum value. To add a new type channel, we need to add a new type enum.</p>
<p>To expose a C++ type to Python, we need to edit the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc"><code class="docutils literal"><span class="pre">pybind.cc</span></code></a> file. <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc#L120-L164">Here</a> is an example how we expose C++ class LoDTensor.</p>
</div>
</div>
<div class="section" id="syntax-design">
<span id="syntax-design"></span><h2>Syntax Design<a class="headerlink" href="#syntax-design" title="永久链接至标题"></a></h2>
<div class="section" id="create-channel">
<span id="create-channel"></span><h3>Create Channel<a class="headerlink" href="#create-channel" title="永久链接至标题"></a></h3>
<p>In Go, we create a channel by specifying the element type and buffer size:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="nx">ch</span> <span class="o">:=</span> <span class="nb">make</span><span class="p">(</span><span class="kd">chan</span> <span class="kt">int</span><span class="p">)</span> <span class="c1">// a channel without buffer</span>
<span class="nx">ch1</span> <span class="o">:=</span> <span class="nb">make</span><span class="p">(</span><span class="kd">chan</span> <span class="kt">int</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="c1">// a channel that can buffer 100 ints.</span>
</pre></div>
</div>
<p>In Fluid, we should be able to do the same:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ch</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">INT</span><span class="p">)</span>
<span class="n">ch1</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">INT</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
</pre></div>
</div>
<p>In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ch</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">make_chan</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">etype</span><span class="o">=</span><span class="n">float16</span><span class="p">)</span>
</pre></div>
</div>
<p>or Tensors of Tensors of float16 etc.</p>
<p>The point here is that we need a consistent way to compose types, like in C++ we can have <code class="docutils literal"><span class="pre">Tensor&lt;Tensor&lt;...&lt;float16&gt;...&gt;</span> <span class="pre">&gt;</span></code>.</p>
</div>
<div class="section" id="send-and-recv">
<span id="send-and-recv"></span><h3>Send and Recv<a class="headerlink" href="#send-and-recv" title="永久链接至标题"></a></h3>
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<div class="section" id="select">
<span id="select"></span><h3>Select<a class="headerlink" href="#select" title="永久链接至标题"></a></h3>
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<div class="section" id="example-programs">
<span id="example-programs"></span><h2>Example Programs<a class="headerlink" href="#example-programs" title="永久链接至标题"></a></h2>
<div class="section" id="rpc-between-trainers-and-parameter-servers">
<span id="rpc-between-trainers-and-parameter-servers"></span><h3>1. RPC between Trainers and Parameter Servers<a class="headerlink" href="#rpc-between-trainers-and-parameter-servers" title="永久链接至标题"></a></h3>
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<div class="section" id="concurrent-minibatch-loading">
<span id="concurrent-minibatch-loading"></span><h3>2. Concurrent Minibatch Loading<a class="headerlink" href="#concurrent-minibatch-loading" title="永久链接至标题"></a></h3>
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