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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
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<li class="toctree-l1 current"><a class="reference internal" href="recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_classification_with_torchaudio_tutorial.html">Speech Command Classification with torchaudio</a></li>
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<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
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<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋ง ํ๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
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<p class="caption"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
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๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">ํ์ดํ๋ผ์ธ ๋ณ๋ ฌํ๋ก ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต์ํค๊ธฐ</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
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<div class="section" id="direct-device-to-device-communication-with-tensorpipe-cuda-rpc">
<h1>Direct Device-to-Device Communication with TensorPipe CUDA RPC<a class="headerlink" href="#direct-device-to-device-communication-with-tensorpipe-cuda-rpc" title="Permalink to this headline">ยถ</a></h1>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Direct device-to-device RPC (CUDA RPC) is introduced in PyTorch 1.8
as a prototype feature. This API is subject to change.</p>
</div>
<p>In this recipe, you will learn:</p>
<ul class="simple">
<li>The high-level idea of CUDA RPC.</li>
<li>How to use CUDA RPC.</li>
</ul>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">ยถ</a></h2>
<ul class="simple">
<li>PyTorch 1.8+</li>
<li><a class="reference external" href="https://tutorials.pytorch.kr/intermediate/rpc_tutorial.html">Getting Started With Distributed RPC Framework</a></li>
</ul>
</div>
<div class="section" id="what-is-cuda-rpc">
<h2>What is CUDA RPC?<a class="headerlink" href="#what-is-cuda-rpc" title="Permalink to this headline">ยถ</a></h2>
<p>CUDA RPC supports directly sending Tensors from local CUDA memory to remote
CUDA memory. Prior to v1.8 release, PyTorch RPC only accepts CPU Tensors. As a
result, when an application needs to send a CUDA Tensor through RPC, it has
to first move the Tensor to CPU on the caller, send it via RPC, and then move
it to the destination device on the callee, which incurs both unnecessary
synchronizations and D2H and H2D copies. Since v1.8, RPC allows users to
configure a per-process global device map using the
<a class="reference external" href="https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map">set_device_map</a>
API, specifying how to map local devices to remote devices. More specifically,
if <code class="docutils literal notranslate"><span class="pre">worker0</span></code>โs device map has an entry <code class="docutils literal notranslate"><span class="pre">"worker1"</span> <span class="pre">:</span> <span class="pre">{"cuda:0"</span> <span class="pre">:</span> <span class="pre">"cuda:1"}</span></code>,
all RPC arguments on <code class="docutils literal notranslate"><span class="pre">"cuda:0"</span></code> from <code class="docutils literal notranslate"><span class="pre">worker0</span></code> will be directly sent to
<code class="docutils literal notranslate"><span class="pre">"cuda:1"</span></code> on <code class="docutils literal notranslate"><span class="pre">worker1</span></code>. The response of an RPC will use the inverse of
the caller device map, i.e., if <code class="docutils literal notranslate"><span class="pre">worker1</span></code> returns a Tensor on <code class="docutils literal notranslate"><span class="pre">"cuda:1"</span></code>,
it will be directly sent to <code class="docutils literal notranslate"><span class="pre">"cuda:0"</span></code> on <code class="docutils literal notranslate"><span class="pre">worker0</span></code>. All intended
device-to-device direct communication must be specified in the per-process
device map. Otherwise, only CPU tensors are allowed.</p>
<p>Under the hood, PyTorch RPC relies on <a class="reference external" href="https://github.com/pytorch/tensorpipe">TensorPipe</a>
as the communication backend. PyTorch RPC extracts all Tensors from each
request or response into a list and packs everything else into a binary
payload. Then, TensorPipe will automatically choose a communication channel
for each Tensor based on Tensor device type and channel availability on both
the caller and the callee. Existing TensorPipe channels cover NVLink, InfiniBand,
SHM, CMA, TCP, etc.</p>
</div>
<div class="section" id="how-to-use-cuda-rpc">
<h2>How to use CUDA RPC?<a class="headerlink" href="#how-to-use-cuda-rpc" title="Permalink to this headline">ยถ</a></h2>
<p>The code below shows how to use CUDA RPC. The model contains two linear layers
and is split into two shards. The two shards are placed on <code class="docutils literal notranslate"><span class="pre">worker0</span></code> and
<code class="docutils literal notranslate"><span class="pre">worker1</span></code> respectively, and <code class="docutils literal notranslate"><span class="pre">worker0</span></code> serves as the master that drives the
forward and backward passes. Note that we intentionally skipped
<a class="reference external" href="https://pytorch.org/docs/master/rpc.html#module-torch.distributed.optim">DistributedOptimizer</a>
to highlight the performance improvements when using CUDA RPC. The experiment
repeats the forward and backward passes 10 times and measures the total
execution time. It compares using CUDA RPC against manually staging to CPU
memory and using CPU RPC.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">autograd</span>
<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">comm_mode</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
<span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comm_mode</span> <span class="o">=</span> <span class="n">comm_mode</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># x.to() is a no-op if x is already on self.device</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
<span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">comm_mode</span> <span class="o">==</span> <span class="s2">"cpu"</span> <span class="k">else</span> <span class="n">y</span>
<span class="k">def</span> <span class="nf">parameter_rrefs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">rpc</span><span class="o">.</span><span class="n">RRef</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">()]</span>
<span class="k">def</span> <span class="nf">measure</span><span class="p">(</span><span class="n">comm_mode</span><span class="p">):</span>
<span class="c1"># local module on "worker0/cuda:0"</span>
<span class="n">lm</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">(</span><span class="s2">"cuda:0"</span><span class="p">,</span> <span class="n">comm_mode</span><span class="p">)</span>
<span class="c1"># remote module on "worker1/cuda:1"</span>
<span class="n">rm</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">remote</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="n">MyModule</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="s2">"cuda:1"</span><span class="p">,</span> <span class="n">comm_mode</span><span class="p">))</span>
<span class="c1"># prepare random inputs</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">tik</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">context</span><span class="p">()</span> <span class="k">as</span> <span class="n">ctx</span><span class="p">:</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">rm</span><span class="o">.</span><span class="n">rpc_sync</span><span class="p">()</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">lm</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="p">[</span><span class="n">y</span><span class="o">.</span><span class="n">sum</span><span class="p">()])</span>
<span class="c1"># synchronize on "cuda:0" to make sure that all pending CUDA ops are</span>
<span class="c1"># included in the measurements</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">(</span><span class="s2">"cuda:0"</span><span class="p">)</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
<span class="n">tok</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">comm_mode</span><span class="si">}</span><span class="s2"> RPC total execution time: </span><span class="si">{</span><span class="n">tok</span> <span class="o">-</span> <span class="n">tik</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">run_worker</span><span class="p">(</span><span class="n">rank</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_ADDR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'localhost'</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'MASTER_PORT'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'29500'</span>
<span class="n">options</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">TensorPipeRpcBackendOptions</span><span class="p">(</span><span class="n">num_worker_threads</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
<span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">options</span><span class="o">.</span><span class="n">set_device_map</span><span class="p">(</span><span class="s2">"worker1"</span><span class="p">,</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">})</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
<span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">rpc_backend_options</span><span class="o">=</span><span class="n">options</span>
<span class="p">)</span>
<span class="n">measure</span><span class="p">(</span><span class="n">comm_mode</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">)</span>
<span class="n">measure</span><span class="p">(</span><span class="n">comm_mode</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
<span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">rpc_backend_options</span><span class="o">=</span><span class="n">options</span>
<span class="p">)</span>
<span class="c1"># block until all rpcs finish</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
<span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s2">"__main__"</span><span class="p">:</span>
<span class="n">world_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Outputs are displayed below, which shows that CUDA RPC can help to achieve
34X speed up compared to CPU RPC in this experiment.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cpu</span> <span class="n">RPC</span> <span class="n">total</span> <span class="n">execution</span> <span class="n">time</span><span class="p">:</span> <span class="mf">2.3145179748535156</span> <span class="n">Seconds</span>
<span class="n">cuda</span> <span class="n">RPC</span> <span class="n">total</span> <span class="n">execution</span> <span class="n">time</span><span class="p">:</span> <span class="mf">0.06867480278015137</span> <span class="n">Seconds</span>
</pre></div>
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