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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/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="basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="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="basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="introyt.html">Introduction to PyTorch - YouTube Series</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="introyt/captumyt.html">Model Understanding with Captum</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<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="transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํ ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="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="text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li>
</ul>
<p class="caption"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<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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<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
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C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
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<li class="toctree-l1"><a class="reference internal" href="hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li>
<li class="toctree-l1"><a class="reference internal" href="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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<li class="toctree-l1 current"><a class="current reference internal" href="#">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>
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<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>
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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="pytorch-distributed-overview">
<h1>PyTorch Distributed Overview<a class="headerlink" href="#pytorch-distributed-overview" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://mrshenli.github.io/">Shen Li</a></p>
<p>This is the overview page for the <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> package. The goal of
this page is to categorize documents into different topics and briefly
describe each of them. If this is your first time building distributed training
applications using PyTorch, it is recommended to use this document to navigate
to the technology that can best serve your use case.</p>
<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">ยถ</a></h2>
<p>As of PyTorch v1.6.0, features in <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> can be categorized into
three main components:</p>
<ul class="simple">
<li><a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">Distributed Data-Parallel Training</a>
(DDP) is a widely adopted single-program multiple-data training paradigm. With
DDP, the model is replicated on every process, and every model replica will be
fed with a different set of input data samples. DDP takes care of gradient
communication to keep model replicas synchronized and overlaps it with the
gradient computations to speed up training.</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">RPC-Based Distributed Training</a>
(RPC) supports general training structures that cannot fit into
data-parallel training such as distributed pipeline parallelism, parameter
server paradigm, and combinations of DDP with other training paradigms. It
helps manage remote object lifetime and extends the
<a class="reference external" href="https://pytorch.org/docs/stable/autograd.html">autograd engine</a> beyond
machine boundaries.</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/distributed.html">Collective Communication</a>
(c10d) library supports sending tensors across processes within a group. It
offers both collective communication APIs (e.g.,
<a class="reference external" href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce">all_reduce</a>
and <a class="reference external" href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather">all_gather</a>)
and P2P communication APIs (e.g.,
<a class="reference external" href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.send">send</a>
and <a class="reference external" href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.isend">isend</a>).
DDP and RPC (<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#process-group-backend">ProcessGroup Backend</a>)
are built on c10d, where the former uses collective communications
and the latter uses P2P communications. Usually, developers do not need to
directly use this raw communication API, as the DDP and RPC APIs can serve
many distributed training scenarios. However, there are use cases where this API
is still helpful. One example would be distributed parameter averaging, where
applications would like to compute the average values of all model parameters
after the backward pass instead of using DDP to communicate gradients. This can
decouple communications from computations and allow finer-grain control over
what to communicate, but on the other hand, it also gives up the performance
optimizations offered by DDP. The
<a class="reference external" href="../intermediate/dist_tuto.html">Writing Distributed Applications with PyTorch</a>
shows examples of using c10d communication APIs.</li>
</ul>
</div>
<div class="section" id="data-parallel-training">
<h2>Data Parallel Training<a class="headerlink" href="#data-parallel-training" title="Permalink to this headline">ยถ</a></h2>
<p>PyTorch provides several options for data-parallel training. For applications
that gradually grow from simple to complex and from prototype to production, the
common development trajectory would be:</p>
<ol class="arabic simple">
<li>Use single-device training if the data and model can fit in one GPU, and
training speed is not a concern.</li>
<li>Use single-machine multi-GPU
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html">DataParallel</a>
to make use of multiple GPUs on a single machine to speed up training with
minimal code changes.</li>
<li>Use single-machine multi-GPU
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a>,
if you would like to further speed up training and are willing to write a
little more code to set it up.</li>
<li>Use multi-machine <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a>
and the <a class="reference external" href="https://github.com/pytorch/examples/blob/master/distributed/ddp/README.md">launching script</a>,
if the application needs to scale across machine boundaries.</li>
<li>Use <a class="reference external" href="https://pytorch.org/docs/stable/distributed.elastic.html">torch.distributed.elastic</a>
to launch distributed training if errors (e.g., out-of-memory) are expected or if
resources can join and leave dynamically during training.</li>
</ol>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Data-parallel training also works with <a class="reference external" href="https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-multiple-gpus">Automatic Mixed Precision (AMP)</a>.</p>
</div>
<div class="section" id="torch-nn-dataparallel">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.DataParallel</span></code><a class="headerlink" href="#torch-nn-dataparallel" title="Permalink to this headline">ยถ</a></h3>
<p>The <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html">DataParallel</a>
package enables single-machine multi-GPU parallelism with the lowest coding
hurdle. It only requires a one-line change to the application code. The tutorial
<a class="reference external" href="../beginner/blitz/data_parallel_tutorial.html">Optional: Data Parallelism</a>
shows an example. Although <code class="docutils literal notranslate"><span class="pre">DataParallel</span></code> is very easy to
use, it usually does not offer the best performance because it replicates the
model in every forward pass, and its single-process multi-thread parallelism
naturally suffers from
<a class="reference external" href="https://wiki.python.org/moin/GlobalInterpreterLock">GIL</a> contention. To get
better performance, consider using
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a>.</p>
</div>
<div class="section" id="torch-nn-parallel-distributeddataparallel">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code><a class="headerlink" href="#torch-nn-parallel-distributeddataparallel" title="Permalink to this headline">ยถ</a></h3>
<p>Compared to <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html">DataParallel</a>,
<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html">DistributedDataParallel</a>
requires one more step to set up, i.e., calling
<a class="reference external" href="https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group">init_process_group</a>.
DDP uses multi-process parallelism, and hence there is no GIL contention across
model replicas. Moreover, the model is broadcast at DDP construction time instead
of in every forward pass, which also helps to speed up training. DDP is shipped
with several performance optimization technologies. For a more in-depth
explanation, refer to this
<a class="reference external" href="http://www.vldb.org/pvldb/vol13/p3005-li.pdf">paper</a> (VLDBโ20).</p>
<p>DDP materials are listed below:</p>
<ol class="arabic simple">
<li><a class="reference external" href="https://pytorch.org/docs/stable/notes/ddp.html">DDP notes</a>
offer a starter example and some brief descriptions of its design and
implementation. If this is your first time using DDP, start from this
document.</li>
<li><a class="reference external" href="../intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a>
explains some common problems with DDP training, including unbalanced
workload, checkpointing, and multi-device models. Note that, DDP can be
easily combined with single-machine multi-device model parallelism which is
described in the
<a class="reference external" href="../intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a>
tutorial.</li>
<li>The <a class="reference external" href="https://github.com/pytorch/examples/blob/stable/distributed/ddp/README.md">Launching and configuring distributed data parallel applications</a>
document shows how to use the DDP launching script.</li>
<li>The <a class="reference external" href="../recipes/zero_redundancy_optimizer.html">Shard Optimizer States With ZeroRedundancyOptimizer</a>
recipe demonstrates how <a class="reference external" href="https://pytorch.org/docs/stable/distributed.optim.html">ZeroRedundancyOptimizer</a>
helps to reduce optimizer memory footprint.</li>
<li>The <a class="reference external" href="../advanced/generic_oin.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a>
tutorial walks through using the generic join context for distributed training with uneven inputs.</li>
</ol>
</div>
<div class="section" id="torch-distributed-elastic">
<h3>torch.distributed.elastic<a class="headerlink" href="#torch-distributed-elastic" title="Permalink to this headline">ยถ</a></h3>
<p>With the growth of the application complexity and scale, failure recovery
becomes a requirement. Sometimes it is inevitable to hit errors
like out-of-memory (OOM) when using DDP, but DDP itself cannot recover from those errors,
and it is not possible to handle them using a standard <code class="docutils literal notranslate"><span class="pre">try-except</span></code> construct.
This is because DDP requires all processes to operate in a closely synchronized manner
and all <code class="docutils literal notranslate"><span class="pre">AllReduce</span></code> communications launched in different processes must match.
If one of the processes in the group
throws an exception, it is likely to lead to desynchronization (mismatched
<code class="docutils literal notranslate"><span class="pre">AllReduce</span></code> operations) which would then cause a crash or hang.
<a class="reference external" href="https://pytorch.org/docs/stable/distributed.elastic.html">torch.distributed.elastic</a>
adds fault tolerance and the ability to make use of a dynamic pool of machines (elasticity).</p>
</div>
</div>
<div class="section" id="rpc-based-distributed-training">
<h2>RPC-Based Distributed Training<a class="headerlink" href="#rpc-based-distributed-training" title="Permalink to this headline">ยถ</a></h2>
<p>Many training paradigms do not fit into data parallelism, e.g.,
parameter server paradigm, distributed pipeline parallelism, reinforcement
learning applications with multiple observers or agents, etc.
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a> aims at
supporting general distributed training scenarios.</p>
<p><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html">torch.distributed.rpc</a>
has four main pillars:</p>
<ul class="simple">
<li><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#rpc">RPC</a> supports running
a given function on a remote worker.</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#rref">RRef</a> helps to manage the
lifetime of a remote object. The reference counting protocol is presented in the
<a class="reference external" href="https://pytorch.org/docs/stable/rpc/rref.html#remote-reference-protocol">RRef notes</a>.</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#distributed-autograd-framework">Distributed Autograd</a>
extends the autograd engine beyond machine boundaries. Please refer to
<a class="reference external" href="https://pytorch.org/docs/stable/rpc/distributed_autograd.html#distributed-autograd-design">Distributed Autograd Design</a>
for more details.</li>
<li><a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#module-torch.distributed.optim">Distributed Optimizer</a>
automatically reaches out to all participating workers to update
parameters using gradients computed by the distributed autograd engine.</li>
</ul>
<p>RPC Tutorials are listed below:</p>
<ol class="arabic simple">
<li>The <a class="reference external" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a>
tutorial first uses a simple Reinforcement Learning (RL) example to
demonstrate RPC and RRef. Then, it applies a basic distributed model
parallelism to an RNN example to show how to use distributed autograd and
distributed optimizer.</li>
<li>The <a class="reference external" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a>
tutorial borrows the spirit of
<a class="reference external" href="https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf">HogWild! training</a>
and applies it to an asynchronous parameter server (PS) training application.</li>
<li>The <a class="reference external" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a>
tutorial extends the single-machine pipeline parallel example (presented in
<a class="reference external" href="../intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a>)
to a distributed environment and shows how to implement it using RPC.</li>
<li>The <a class="reference external" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a>
tutorial demonstrates how to implement RPC batch processing using the
<a class="reference external" href="https://pytorch.org/docs/stable/rpc.html#torch.distributed.rpc.functions.async_execution">@rpc.functions.async_execution</a>
decorator, which can help speed up inference and training. It uses
RL and PS examples similar to those in the above tutorials 1 and 2.</li>
<li>The <a class="reference external" href="../advanced/rpc_ddp_tutorial.html">Combining Distributed DataParallel with Distributed RPC Framework</a>
tutorial demonstrates how to combine DDP with RPC to train a model using
distributed data parallelism combined with distributed model parallelism.</li>
</ol>
</div>
<div class="section" id="pytorch-distributed-developers">
<h2>PyTorch Distributed Developers<a class="headerlink" href="#pytorch-distributed-developers" title="Permalink to this headline">ยถ</a></h2>
<p>If youโd like to contribute to PyTorch Distributed, please refer to our
<a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/distributed/CONTRIBUTING.md">Developer Guide</a>.</p>
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