Rebuild

Author: 9bow

Diff for: docs/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 6c06769a8bb35d184e276e3b2abf2872
+config: d3c78aee0796eaca17b472b08560486c
 tags: 645f666f9bcd5a90fca523b33c5a78b7

Diff for: docs/_downloads/20d348a2f64b9656713fbceddcacc057/ddp_pipeline.py

@@ -174,7 +174,7 @@
 #    만약 (검증 손실(validation loss) 결과에 따라) 가장 성능이 좋은 모델만 유지할
 #    계획이라면, ``best_model_state = model.state_dict()`` 은 모델의 복사본이 아닌
 #    모델의 현재 상태에 대한 참조(reference)만 반환한다는 사실을 잊으시면 안됩니다!
-#    따라서 ``best_model_state``` 을 직렬화(serialize)하거나,
+#    따라서 ``best_model_state`` 을 직렬화(serialize)하거나,
 #    ``best_model_state = deepcopy(model.state_dict())`` 을 사용해야 합니다.
 #    그렇지 않으면, 제일 좋은 성능을 내는 ``best_model_state`` 은 계속되는 학습 단계에서
 #    갱신될 것입니다. 결과적으로, 최종 모델의 상태는 과적합(overfit)된 상태가 됩니다.

Diff for: docs/_downloads/2e90a43e7f36e061a37d5b2647820512/saving_loading_models.py

@@ -9,7 +9,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>ONNX Live 튜토리얼 &mdash; PyTorch Tutorials 1.10.1+cu113 documentation</title>
+  <title>ONNX Live 튜토리얼 &mdash; PyTorch Tutorials 1.10.2+cu102 documentation</title>
 
 
 
@@ -111,7 +111,7 @@
 
 
                 <div class="version">
-                  1.10.1+cu113
+                  1.10.2+cu102
                 </div>
 
 
@@ -264,7 +264,7 @@
 <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="rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
 </ul>
 <p class="caption"><span class="caption-text">Mobile</span></p>

Diff for: docs/_downloads/997c0b8c84ea4c2b88f308901819e723/saving_loading_models.ipynb

@@ -9,7 +9,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>C++ 프론트엔드의 자동 미분 (autograd) &mdash; PyTorch Tutorials 1.10.1+cu113 documentation</title>
+  <title>C++ 프론트엔드의 자동 미분 (autograd) &mdash; PyTorch Tutorials 1.10.2+cu102 documentation</title>
 
 
 
@@ -113,7 +113,7 @@
 
 
                 <div class="version">
-                  1.10.1+cu113
+                  1.10.2+cu102
                 </div>
 
 
@@ -266,7 +266,7 @@
 <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="rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
 </ul>
 <p class="caption"><span class="caption-text">Mobile</span></p>

Diff for: docs/_downloads/e081556e6a716888948cd45335518ed9/ddp_pipeline.ipynb

@@ -9,7 +9,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>C++에서 TorchScript 모델 로딩하기 &mdash; PyTorch Tutorials 1.10.1+cu113 documentation</title>
+  <title>C++에서 TorchScript 모델 로딩하기 &mdash; PyTorch Tutorials 1.10.2+cu102 documentation</title>
 
 
 
@@ -113,7 +113,7 @@
 
 
                 <div class="version">
-                  1.10.1+cu113
+                  1.10.2+cu102
                 </div>
 
 
@@ -266,7 +266,7 @@
 <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="rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
 </ul>
 <p class="caption"><span class="caption-text">Mobile</span></p>

Diff for: docs/advanced/ONNXLive.html

@@ -9,7 +9,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>Custom C++ and CUDA Extensions &mdash; PyTorch Tutorials 1.10.1+cu113 documentation</title>
+  <title>Custom C++ and CUDA Extensions &mdash; PyTorch Tutorials 1.10.2+cu102 documentation</title>
 
 
 
@@ -113,7 +113,7 @@
 
 
                 <div class="version">
-                  1.10.1+cu113
+                  1.10.2+cu102
                 </div>
 
 
@@ -266,7 +266,7 @@
 <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="rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
 </ul>
 <p class="caption"><span class="caption-text">Mobile</span></p>

Diff for: docs/advanced/cpp_autograd.html

@@ -9,7 +9,7 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-  <title>PyTorch C++ 프론트엔드 사용하기 &mdash; PyTorch Tutorials 1.10.1+cu113 documentation</title>
+  <title>PyTorch C++ 프론트엔드 사용하기 &mdash; PyTorch Tutorials 1.10.2+cu102 documentation</title>
 
 
 
@@ -113,7 +113,7 @@
 
 
                 <div class="version">
-                  1.10.1+cu113
+                  1.10.2+cu102
                 </div>
 
 
@@ -266,7 +266,7 @@
 <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="rpc_ddp_tutorial.html">분산 데이터 병렬(DDP)과 분산 RPC 프레임워크 결합</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pipeline_tutorial.html">파이프라인 병렬화로 트랜스포머 모델 학습시키기</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">Training Transformer models using Distributed Data Parallel and Pipeline Parallelism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">분산 데이터 병렬 처리와 병렬 처리 파이프라인을 사용한 트랜스포머 모델 학습</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
 </ul>
 <p class="caption"><span class="caption-text">Mobile</span></p>