beginner_source/dist_overview.rst 번역 (#1046)

* 번역: beginner_source/dist_overview.rst 문서 번역 추가

* 번역: beginner_source/dist_overview.rst 문서 번역 수정

* 번역: beginner_source/dist_overview.rst 문서 번역 수정

* 번역: beginner_source/dist_overview.rst 문서 번역 수정

Author: KJH622

beginner_source/dist_overview.rst

@@ -1,94 +1,87 @@
-PyTorch Distributed Overview
+PyTorch 분산 개요
 ============================
-**Author**: `Will Constable <https://github.com/wconstab/>`_, `Wei Feng <https://github.com/weifengpy>`_
-
+**저자**: `Will Constable <https://github.com/wconstab/>`_, `Wei Feng <https://github.com/weifengpy>`_
+**번역**: `강지현 <https://github.com/KJH622>`_
 .. note::
-   |edit| View and edit this tutorial in `github <https://github.com/pytorchkorea/tutorials-kr/blob/main/beginner_source/dist_overview.rst>`__.
-
-This is the overview page for the ``torch.distributed`` 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.
+   |edit| 이 튜토리얼을 여기서 보고 편집하세요 `github <https://github.com/pytorchkorea/tutorials-kr/blob/main/beginner_source/dist_overview.rst>`__.
 
+이 문서는 ``torch.distributed`` 패키지의 개요 페이지입니다.
+이 페이지의 목표는 문서를 주제별로 분류하고 
+각 주제를 간략히 설명하는 것입니다. PyTorch로 분산 학습 애플리케이션을 처음 구축한다면,
+이 문서를 참고하여 여러분의 사용 사례에 가장 적합한 기술을 찾아보는 것을 권장합니다.
 
-Introduction
+서론
 ------------
 
-The PyTorch Distributed library includes a collective of parallelism modules,
-a communications layer, and infrastructure for launching and
-debugging large training jobs.
+파이토치 분산 라이브러리는 여러 병렬화 모듈, 통신 계층, 그리고 대규모 학습 작업의 실행 및 디버깅을 위한 인프라로 구성됩니다.
 
 
-Parallelism APIs
+병렬 처리 API
 ****************
 
-These Parallelism Modules offer high-level functionality and compose with existing models:
+이러한 병렬화 모듈은 고수준 기능을 제공하며 기존 모델과 조합하여 사용할 수 있습니다.
 
-- `Distributed Data-Parallel (DDP) <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__
-- `Fully Sharded Data-Parallel Training (FSDP2) <https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html>`__
-- `Tensor Parallel (TP) <https://pytorch.org/docs/stable/distributed.tensor.parallel.html>`__
-- `Pipeline Parallel (PP) <https://pytorch.org/docs/main/distributed.pipelining.html>`__
+- `분산 데이터 병렬 처리 (DDP, Distributed Data-Parallel) <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__
+- `완전 샤딩 데이터 병렬 학습 (FSDP2, Fully Sharded Data-Parallel Training) <https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html>`__
+- `텐서 병렬 처리 (TP, Tensor Parallel) <https://pytorch.org/docs/stable/distributed.tensor.parallel.html>`__
+- `파이프라인 병렬 처리 (PP, Pipeline Parallel) <https://pytorch.org/docs/main/distributed.pipelining.html>`__
 
-Sharding primitives
+샤딩 기본 요소(Sharding primitives)
 *******************
 
-``DTensor`` and ``DeviceMesh`` are primitives used to build parallelism in terms of sharded or replicated tensors on N-dimensional process groups.
+``DTensor`` 와 ``DeviceMesh`` 는 N차원 프로세스 그룹에서 텐서를 샤딩하거나 복제하는 방식으로 병렬화를 구성할 때 사용하는 기본 구성요소입니다.
 
-- `DTensor <https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/README.md>`__ represents a tensor that is sharded and/or replicated, and communicates automatically to reshard tensors as needed by operations.
-- `DeviceMesh <https://pytorch.org/docs/stable/distributed.html#devicemesh>`__ abstracts the accelerator device communicators into a multi-dimensional array, which manages the underlying ``ProcessGroup`` instances for collective communications in multi-dimensional parallelisms.  Try out our `Device Mesh Recipe <https://tutorials.pytorch.kr/recipes/distributed_device_mesh.html>`__ to learn more.
+- `DTensor <https://github.com/pytorch/pytorch/blob/main/torch/distributed/tensor/README.md>`__ 는 샤딩되거나/복제된 텐서를 나타내며, 연산의 요구에 따라 텐서를 재샤딩하기 위한 통신을 자동으로 수행합니다.
+- `DeviceMesh <https://pytorch.org/docs/stable/distributed.html#devicemesh>`__ 는 가속기 디바이스의 커뮤니케이터(communicator)를 다차원 배열로 추상화하며, 다차원 병렬성에서 집합(collective) 통신을 수행하기 위한 하위 ``ProcessGroup`` 인스턴스들을 관리합니다. 더 알아보려면 `Device Mesh 레시피 <https://tutorials.pytorch.kr/recipes/distributed_device_mesh.html>`__ 를 직접 따라 해보세요.
 
-Communications APIs
+통신 API
 *******************
 
-The `PyTorch distributed communication layer (C10D) <https://pytorch.org/docs/stable/distributed.html>`__ offers both collective communication APIs (e.g., `all_reduce <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce>`__
-  and `all_gather <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather>`__)
-  and P2P communication APIs (e.g.,
-  `send <https://pytorch.org/docs/stable/distributed.html#torch.distributed.send>`__
-  and `isend <https://pytorch.org/docs/stable/distributed.html#torch.distributed.isend>`__),
-  which are used under the hood in all of the parallelism implementations.
-  `Writing Distributed Applications with PyTorch <../intermediate/dist_tuto.html>`__
-  shows examples of using c10d communication APIs.
+`PyTorch 분산 통신 계층 (C10D) <https://pytorch.org/docs/stable/distributed.html>`__ 은 집합 통신 API (예: `all_reduce(전체 축소) <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_reduce>`__
+   , `all_gather(전체 수집) <https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_gather>`__)
+   와 P2P 통신 API (예: `send(동기 전송) <https://pytorch.org/docs/stable/distributed.html#torch.distributed.send>`__
+   , `isend(비동기 전송) <https://pytorch.org/docs/stable/distributed.html#torch.distributed.isend>`__)를 모두 제공하며,
+   이러한 API는 모든 병렬화 구현에서 내부적으로 사용됩니다.
+   `PyTorch로 분산 애플리케이션 작성하기 <../intermediate/dist_tuto.html>`__ 는 C10D 통신 API 사용 예제를 보여 줍니다.
 
-Launcher
+실행기(Launcher)
 ********
 
-`torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__ is a widely-used launcher script, which spawns processes on the local and remote machines for running distributed PyTorch programs.
+`torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__ 은 널리 쓰이는 실행기 스크립트로, 분산 PyTorch 프로그램을 실행하기 위해 로컬 및 원격 머신에서 프로세스를 생성합니다.
 
 
-Applying Parallelism To Scale Your Model
+모델 확장을 위한 병렬화 적용
 ----------------------------------------
 
-Data Parallelism is a widely adopted single-program multiple-data training paradigm
-where the model is replicated on every process, every model replica computes local gradients for
-a different set of input data samples, gradients are averaged within the data-parallel communicator group before each optimizer step.
+데이터 병렬화(Data Parallelism)는 널리 채택된 SPMD(single-program multiple-data) 학습 패러다임으로,
+모델이 모든 프로세스에 복제되고 각 모델의 복제본이 서로 다른 입력 데이터 샘플 집합에 대해 로컬 변화도를 계산합니다.
+그런 다음 각 옵티마이저 스텝 전에 데이터-병렬 통신 그룹 내에서 변화도를 평균화합니다.
 
-Model Parallelism techniques (or Sharded Data Parallelism) are required when a model doesn't fit in GPU, and can be combined together to form multi-dimensional (N-D) parallelism techniques.
+모델 병렬화(Model Parallelism) 기법(또는 샤딩된 데이터 병렬화)은 모델이 GPU 메모리에 들어가지 않을 때 필요하며, 서로 결합해 다차원(N-D) 병렬화 기법을 구성할 수 있습니다.
 
-When deciding what parallelism techniques to choose for your model, use these common guidelines:
+모델에 적용할 병렬화 기법을 결정할 때는 다음의 일반적인 지침을 참고하세요.
 
-#. Use `DistributedDataParallel (DDP) <https://pytorch.org/docs/stable/notes/ddp.html>`__,
-   if your model fits in a single GPU but you want to easily scale up training using multiple GPUs.
+#. 모델이 단일 GPU를 탑재할 수 있지만, 여러 GPU로 쉽게 학습을 확장하고 싶다면 
+   `DistributedDataParallel (DDP, 분산 데이터 병렬화) <https://pytorch.org/docs/stable/notes/ddp.html>`__ 를 사용하세요.
 
-   * Use `torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__, to launch multiple pytorch processes if you are using more than one node.
+   * 여러 노드를 사용하는 경우, 여러 PyTorch 프로세스를 시작하려면 `torchrun <https://pytorch.org/docs/stable/elastic/run.html>`__ 을 사용하세요.
 
-   * See also: `Getting Started with Distributed Data Parallel <../intermediate/ddp_tutorial.html>`__
+   * 참고: `분산 데이터 병렬(DDP) 시작하기 <../intermediate/ddp_tutorial.html>`__
 
-#. Use `FullyShardedDataParallel (FSDP2) <https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html>`__ when your model cannot fit on one GPU.
+#. 모델이 단일 GPU에 탑재되지 않을 때는 `FullyShardedDataParallel (FSDP2) <https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html>`__ 을 사용하세요.
 
-   * See also: `Getting Started with FSDP2 <https://tutorials.pytorch.kr/intermediate/FSDP_tutorial.html>`__
+   * 참고: `FSDP2 시작하기 <https://tutorials.pytorch.kr/intermediate/FSDP_tutorial.html>`__
 
-#. Use `Tensor Parallel (TP) <https://pytorch.org/docs/stable/distributed.tensor.parallel.html>`__ and/or `Pipeline Parallel (PP) <https://pytorch.org/docs/main/distributed.pipelining.html>`__ if you reach scaling limitations with FSDP2.
+#. FSDP2로는 확장 한계에 도달한 경우, `Tensor Parallel (TP, Tensor 병렬화) <https://pytorch.org/docs/stable/distributed.tensor.parallel.html>`__ 및/또는 `Pipeline Parallel (PP, 파이프라인 병렬화) <https://pytorch.org/docs/main/distributed.pipelining.html>`__ 를 사용하세요.
 
-   * Try our `Tensor Parallelism Tutorial <https://tutorials.pytorch.kr/intermediate/TP_tutorial.html>`__
+   * `Tensor 병렬화 튜토리얼 <https://tutorials.pytorch.kr/intermediate/TP_tutorial.html>`__ 을 확인해 보세요.
 
-   * See also: `TorchTitan end to end example of 3D parallelism <https://github.com/pytorch/torchtitan>`__
+   * 참고: `TorchTitan 3D 병렬화 전체(end to end) 예제 <https://github.com/pytorch/torchtitan>`__
 
-.. note:: Data-parallel training also works with `Automatic Mixed Precision (AMP) <https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-multiple-gpus>`__.
+.. note:: 데이터 병렬 학습은 `자동 혼합 정밀도(AMP, Automatic Mixed Precision) <https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-multiple-gpus>`__ 와 함께에서도 동작합니다.
 
 
-PyTorch Distributed Developers
+PyTorch 분산 개발자
 ------------------------------
 
-If you'd like to contribute to PyTorch Distributed, refer to our
-`Developer Guide <https://github.com/pytorch/pytorch/blob/master/torch/distributed/CONTRIBUTING.md>`_.
+PyTorch 분산에 기여하고 싶다면 `개발자 가이드 <https://github.com/pytorch/pytorch/blob/master/torch/distributed/CONTRIBUTING.md>`_ 를 참고하세요.