apply v1.9, pytorch/pytorch.github.io@d43b11c

Author: 9bow

Gemfile.lock

@@ -211,14 +211,14 @@ GEM
       rb-fsevent (~> 0.10, >= 0.10.3)
       rb-inotify (~> 0.9, >= 0.9.10)
     mercenary (0.3.6)
-    mini_portile2 (2.5.0)
+    mini_portile2 (2.5.1)
     minima (2.5.1)
       jekyll (>= 3.5, < 5.0)
       jekyll-feed (~> 0.9)
       jekyll-seo-tag (~> 2.1)
     minitest (5.14.4)
     multipart-post (2.1.1)
-    nokogiri (1.11.2)
+    nokogiri (1.11.4)
       mini_portile2 (~> 2.5.0)
       racc (~> 1.4)
     octokit (4.20.0)

_about/website.md

@@ -14,6 +14,6 @@ snippet: >
 
 파이토치 한국 사용자 모임은 2018년 중순, 학습을 목적으로 PyTorch 튜토리얼 문서를 한국어로 번역하면서 시작하였습니다.<br />
 
-Facebook, Inc와 관련없는 개인들이 시작한 비공식 / 비영리 사이트로, 한국어를 사용하시는 많은 분들께 PyTorch를 소개하고 함께 배우며 성장하는 것을 목표로 하고 있습니다.<br />
+PyTorch를 학습하고 사용하는 PyTorch 한국 사용자들이 시작한 사용자 커뮤니티로, 한국어를 사용하시는 많은 분들께 PyTorch를 소개하고 함께 배우며 성장하는 것을 목표로 하고 있습니다.<br />
 
 이 홈페이지 또는 튜토리얼에 개선이 필요한 부분을 발견하셨다면 <a href="https://github.com/9bow/PyTorchKR" target="_blank">홈페이지 저장소</a> 또는 <a href="https://github.com/9bow/PyTorch-tutorials-kr" target="_blank">튜토리얼 저장소</a>에 이슈 또는 PR을 남겨주세요.

_features/torchserve.md

@@ -1,5 +1,5 @@
 ---
-title: TorchServe (Experimental)
+title: TorchServe
 order: 2
 snippet: >
   ```python

_get_started/installation/azure.md

@@ -9,6 +9,13 @@ Azure [provides](https://azure.microsoft.com/en-us/services/machine-learning-ser
 * dedicated, pre-built [machine learning virtual machines](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/){:target="_blank"}, complete with PyTorch.
 * bare Linux and Windows virtual machines for you to do a custom install of PyTorch.
 
+## PyTorch Enterprise on Azure
+{: #pytorch-enterprise-on-azure}
+
+Microsoft is one of the founding members and also the inaugural participant of the [PyTorch Enterprise Support Program](https://pytorch.org/enterprise-support-program). Microsoft offers PyTorch Enterprise on Azure as a part of Microsoft [Premier](https://www.microsoft.com/en-us/msservices/premier-support) and [Unified](https://www.microsoft.com/en-us/msservices/unified-support-solutions?activetab=pivot1:primaryr4) Support. The PyTorch Enterprise support service includes long-term support to selected versions of PyTorch for up to 2 years, prioritized troubleshooting, and the latest integration with [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/) and other PyTorch add-ons including ONNX Runtime for faster inference. 
+
+To learn more and get started with PyTorch Enterprise on Microsoft Azure, [visit here](https://azure.microsoft.com/en-us/develop/pytorch/).
+
 ## Azure Primer
 {: #microsoft-azure-primer}
 

_get_started/installation/linux.md

@@ -141,8 +141,8 @@ For the majority of PyTorch users, installing from a pre-built binary via a pack
 ### Prerequisites
 {: #linux-prerequisites-2}
 
-1. Install Anaconda[#anaconda]
+1. Install [Anaconda](#anaconda)
 2. Install [CUDA](https://developer.nvidia.com/cuda-downloads), if your machine has a [CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus).
-3. Follow the steps described here: https://github.com/pytorch/pytorch#from-source
+3. Follow the steps described here: [https://github.com/pytorch/pytorch#from-source](https://github.com/pytorch/pytorch#from-source)
 
 You can verify the installation as described [above](#linux-verification).

_get_started/installation/mac.md

@@ -96,6 +96,6 @@ For the majority of PyTorch users, installing from a pre-built binary via a pack
 
 1. Install [Anaconda](#anaconda)
 2. Install [CUDA](https://developer.nvidia.com/cuda-downloads), if your machine has a [CUDA-enabled GPU](https://developer.nvidia.com/cuda-gpus).
-3. Follow the steps described here: https://github.com/pytorch/pytorch#from-source
+3. Follow the steps described here: [https://github.com/pytorch/pytorch#from-source](https://github.com/pytorch/pytorch#from-source)
 
 You can verify the installation as described [above](#mac-verification).

_get_started/previous-versions.md

@@ -101,7 +101,7 @@ conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cpuonly -c pyt
 ##### OSX
 
 ```
-pip install torch==1.7.1 torchvision==0.8.2 torchaudio=0.7.2
+pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
 ```
 
 ##### Linux and Windows
@@ -158,7 +158,7 @@ conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cpuonly -c pyt
 ##### OSX
 
 ```
-pip install torch==1.7.0 torchvision==0.8.0 torchaudio=0.7.0
+pip install torch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0
 ```
 
 ##### Linux and Windows

_includes/contributor_side_nav.html

@@ -1,7 +1,7 @@
 <div class="col-md-3 get-started-cloud-sidebar">
   <div class="sticky-top">
     <ul id="get-started-contributor-sidebar-list">
-      {% assign past_issues = site.past_issues | sort_natural: "date" %}
+      {% assign past_issues = site.past_issues | sort_natural: "date" | reverse %}
 
       {% for item in past_issues %}
         {% assign currentdate = item.date | date: "%B %Y" %}
@@ -11,7 +11,7 @@
         {% endif %}
 
         <ul>
-          <li><a href="#issue-{{ item.issue }}">{{ item.title }}</a></li>
+          <li><a href="#issue-{{ item.issue }}">{{ date }}</a></li>
         </ul>
       {% endfor %}
     </ul>

_includes/footer.html

@@ -50,8 +50,8 @@ <h2>커뮤니티</h2>
 
     <div class="trademark-disclaimer">
       <ul>
-        <li>이 사이트는 PyTorch 한국어 사용자 커뮤니티로 Facebook, Inc와 관련이 없습니다. PyTorch, PyTorch 로고 및 모든 관련 표기는 Facebook, Inc의 상표입니다.</li>
-        <li>This site is a user community and is not related with Facebook, Inc. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.</li>
+        <li>이 사이트는 PyTorch 한국 사용자 커뮤니티로 Facebook, Inc에서 운영하는 사이트가 아닙니다. PyTorch, PyTorch 로고 및 모든 관련 표기는 Facebook, Inc의 상표입니다.</li>
+        <li>This site is a user community and is not operated by Facebook, Inc. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.</li>
       </ul>
     </div>
   </div>

_includes/quick_start_cloud_options.html

@@ -45,7 +45,8 @@
   <div class="cloud-option-row">
     <div class="cloud-option" data-toggle="cloud-dropdown">
       <div class="cloud-option-body microsoft-azure" id="microsoft-azure">
-        Microsoft Azure
+        <p>Microsoft Azure -</p>
+        <span>PyTorch Enterprise Program</span>
       </div>
 
       <ul>

_includes/quick_start_local.html

@@ -1,5 +1,5 @@
 <p>사용 환경을 선택하고 설치 명령을 복사해서 실행해 보세요. Stable 버전은 테스트 및 지원되고 있는 가장 최근의 PyTorch 버전으로, 대부분의 사용자에게 적합합니다.
-  Preview 버전은 아직 완전히 테스트되지 않은 최신 1.9 버전으로 매일 업데이트됩니다. 사용 중인 패키지 매니저에 따라 <b>아래의 사전 요구사항(예: numpy)</b>이 충족되었는지 확인해 주세요.
+  Preview 버전은 아직 완전히 테스트되지 않은 최신 1.10 버전으로 매일 업데이트됩니다. 사용 중인 패키지 매니저에 따라 <b>아래의 사전 요구사항(예: numpy)</b>이 충족되었는지 확인해 주세요.
   모든 의존성을 설치할 수 있는 Anaconda를 패키지 매니저로 추천합니다. <a href="{{ site.baseurl }}/get-started/previous-versions">이전 버전의 PyTorch도 설치할 수 있습니다</a>. LibTorch는 C++에서만 지원합니다.
 </p>
 
@@ -30,12 +30,15 @@
       <div class="col-md-12 title-block mobile-heading">
         <div class="option-text">PyTorch 빌드</div>
       </div>
-      <div class="col-md-6 option block version selected" id="stable">
-        <div class="option-text">Stable (1.8.1)</div>
+      <div class="col-md-4 option block version selected" id="stable">
+        <div class="option-text">Stable (1.9.0)</div>
       </div>
-      <div class="col-md-6 option block version" id="preview">
+      <div class="col-md-4 option block version" id="preview">
         <div class="option-text">Preview (Nightly)</div>
       </div>
+      <div class="col-md-4 option block version" id="lts">
+        <div class="option-text">LTS (1.8.1)</div>
+      </div>
     </div>
     <div class="row os">
       <div class="col-md-12 title-block mobile-heading">
@@ -89,8 +92,8 @@
       <div class="col-md-3 option block version" id="cuda11.1">
         <div class="option-text">CUDA 11.1</div>
       </div>
-      <div class="col-md-3 option block version" id="rocm4.0">
-        <div class="option-text">ROCm 4.0 (beta)</div>
+      <div class="col-md-3 option block version" id="rocm4.2">
+        <div class="option-text">ROCm 4.2 (beta)</div>
       </div>
       <div class="col-md-3 option block version" id="accnone">
         <div class="option-text">CPU</div>

_layouts/blog.html

@@ -19,7 +19,7 @@
       <h1 class="blog-index-title">
         <a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a>
       </h1>
-      <p class="featured-blog-preview">{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 100 }}</p>
+      <p class="featured-blog-preview">{{ post.excerpt | strip_html | truncate: 100 }}</p>
 
       <a href="{{ site.baseurl }}{{ post.url }}" class="btn btn-lg with-right-arrow">
         Read More

_layouts/hub_detail.html

@@ -20,14 +20,23 @@ <h1>
         </h1>
 
         <div class="row">
-          <div class="col-md-6">
+          <div class="col-md-4">
             <p class="detail-lead">By {{ page.author }} </p>
           </div>
 
-          <div class="col-md-6">
+          <div class="col-md-8">
             <p class="detail-lead lead-summary">{{ page.summary }}</p>
-            <a href="{{ page.github-link }}"><button class="btn btn-lg with-right-white-arrow detail-github-link">View on Github</button></a>
-            <a href="https://colab.research.google.com/github/pytorch/pytorch.github.io/blob/master/{{ page.path | replace: "_hub", "assets/hub" | replace: ".md", ".ipynb" }}"><button class="btn btn-lg with-right-white-arrow detail-colab-link">Open on Google Colab</button></a>
+            <div class="detail-button-container">
+              <a href="{{ page.github-link }}"><button class="btn btn-lg with-right-white-arrow detail-github-link">View on Github</button></a>
+              <a href="https://colab.research.google.com/github/pytorch/pytorch.github.io/blob/master/{{ page.path | replace: "_hub", "assets/hub" | replace: ".md", ".ipynb" }}"><button class="btn btn-lg with-right-white-arrow detail-colab-link">Open on Google Colab</button></a>
+              {% if page.demo-model-link %}
+                {% if page.demo-model-button-text == blank or page.demo-model-button-text == nil %}
+                  <a href="{{ page.demo-model-link }}"><button class="btn btn-lg with-right-white-arrow detail-web-demo-link">Demo Model Output</button></a>
+                {% else %}
+                  <a href="{{ page.demo-model-link }}"><button class="btn btn-lg with-right-white-arrow detail-web-demo-link">{{ page.demo-model-button-text }}</button></a>
+                {% endif %}
+              {% endif %}
+            </div>
           </div>
         </div>
       </div>

_mobile/android.md

@@ -17,7 +17,7 @@ This application runs TorchScript serialized TorchVision pretrained resnet18 mod
 
 #### 1. Model Preparation
 
-Let’s start with model preparation. If you are familiar with PyTorch, you probably should already know how to train and save your model. In case you don’t, we are going to use a pre-trained image classification model ([Resnet18](https://pytorch.org/hub/pytorch_vision_resnet/)), which is packaged in [TorchVision](https://pytorch.org/docs/stable/torchvision/index.html).
+Let’s start with model preparation. If you are familiar with PyTorch, you probably should already know how to train and save your model. In case you don’t, we are going to use a pre-trained image classification model ([MobileNetV2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/)).
 To install it, run the command below:
 ```
 pip install torchvision
@@ -27,14 +27,17 @@ To serialize the model you can use python [script](https://github.com/pytorch/an
 ```
 import torch
 import torchvision
+from torch.utils.mobile_optimizer import optimize_for_mobile
 
-model = torchvision.models.resnet18(pretrained=True)
+model = torchvision.models.mobilenet_v2(pretrained=True)
 model.eval()
 example = torch.rand(1, 3, 224, 224)
 traced_script_module = torch.jit.trace(model, example)
-traced_script_module.save("app/src/main/assets/model.pt")
+traced_script_module_optimized = optimize_for_mobile(traced_script_module)
+traced_script_module_optimized._save_for_lite_interpreter("app/src/main/assets/model.ptl")
+
 ```
-If everything works well, we should have our model - `model.pt` generated in the assets folder of android application.
+If everything works well, we should have our model - `model.ptl` generated in the assets folder of android application.
 That will be packaged inside android application as `asset` and can be used on the device.
 
 More details about TorchScript you can find in [tutorials on pytorch.org](https://pytorch.org/docs/stable/jit.html)
@@ -62,8 +65,8 @@ repositories {
 }
 
 dependencies {
-    implementation 'org.pytorch:pytorch_android:1.4.0'
-    implementation 'org.pytorch:pytorch_android_torchvision:1.4.0'
+    implementation 'org.pytorch:pytorch_android_lite:1.9.0'
+    implementation 'org.pytorch:pytorch_android_torchvision:1.9.0'
 }
 ```
 Where `org.pytorch:pytorch_android` is the main dependency with PyTorch Android API, including libtorch native library for all 4 android abis (armeabi-v7a, arm64-v8a, x86, x86_64).
@@ -79,11 +82,11 @@ As a first step we read `image.jpg` to `android.graphics.Bitmap` using the stand
 Bitmap bitmap = BitmapFactory.decodeStream(getAssets().open("image.jpg"));
 ```
 
-#### 5. Loading TorchScript Module
+#### 5. Loading Mobile Module
 ```
-Module module = Module.load(assetFilePath(this, "model.pt"));
+Module module = Module.load(assetFilePath(this, "model.ptl"));
 ```
-`org.pytorch.Module` represents `torch::jit::script::Module` that can be loaded with `load` method specifying file path to the serialized to file model.
+`org.pytorch.Module` represents `torch::jit::mobile::Module` that can be loaded with `load` method specifying file path to the serialized to file model.
 
 #### 6. Preparing Input
 ```
@@ -387,6 +390,21 @@ SELECTED_OP_LIST=MobileNetV2.yaml scripts/build_pytorch_android.sh arm64-v8a
 
 After successful build you can integrate the result aar files to your android gradle project, following the steps from previous section of this tutorial (Building PyTorch Android from Source).
 
+## Use PyTorch JIT interpreter
+
+PyTorch JIT interpreter is the default interpreter before 1.9 (a version of our PyTorch interpreter that is not as size-efficient). It will still be supported in 1.9, and can be used via `build.gradle`:
+```
+repositories {
+    jcenter()
+}
+
+dependencies {
+    implementation 'org.pytorch:pytorch_android:1.9.0'
+    implementation 'org.pytorch:pytorch_android_torchvision:1.9.0'
+}
+```
+
+
 ## Android Tutorials
 
 Watch the following [video](https://youtu.be/5Lxuu16_28o) as PyTorch Partner Engineer Brad Heintz walks through steps for setting up the PyTorch Runtime for Android projects:

_mobile/home.md

@@ -24,15 +24,14 @@ PyTorch Mobile is in beta stage right now, and is already in wide scale producti
 * Support for tracing and scripting via TorchScript IR
 * Support for XNNPACK floating point kernel libraries for Arm CPUs
 * Integration of QNNPACK for 8-bit quantized kernels. Includes support for per-channel quantization, dynamic quantization and more
-* Build level optimization and selective compilation depending on the operators needed for user applications, i.e., the final binary size of the app is determined by the actual operators the app needs
+* Provides an [efficient mobile interpreter in Android and iOS](https://pytorch.org/tutorials/recipes/mobile_interpreter.html). Also supports build level optimization and selective compilation depending on the operators needed for user applications (i.e., the final binary size of the app is determined by the actual operators the app needs).
 * Streamline model optimization via optimize_for_mobile
 * Support for hardware backends like GPU, DSP, and NPU will be available soon in Beta
 
 
 ## Prototypes
 We have launched the following features in prototype, available in the PyTorch nightly releases, and would love to get your feedback on the [PyTorch forums](https://discuss.pytorch.org/c/mobile/18):
 
-* Runtime binary size reduction via our [Lite Interpreter](https://pytorch.org/tutorials/prototype/lite_interpreter.html)
 * GPU support on [iOS via Metal](https://pytorch.org/tutorials/prototype/ios_gpu_workflow.html)
 * GPU support on [Android via Vulkan](https://pytorch.org/tutorials/prototype/vulkan_workflow.html)
 * DSP and NPU support on Android via [Google NNAPI](https://pytorch.org/tutorials/prototype/nnapi_mobilenetv2.html)

_mobile/ios.md

@@ -94,10 +94,10 @@ private lazy var module: TorchModule = {
     }
 }()
 ```
-Note that the `TorchModule` Class is an Objective-C wrapper of `torch::jit::script::Module`.
+Note that the `TorchModule` Class is an Objective-C wrapper of `torch::jit::mobile::Module`.
 
 ```cpp
-torch::jit::script::Module module = torch::jit::load(filePath.UTF8String);
+torch::jit::mobile::Module module = torch::jit::_load_for_mobile(filePath.UTF8String);
 ```
 Since Swift can not talk to C++ directly, we have to either use an Objective-C class as a bridge, or create a C wrapper for the C++ library. For demo purpose, we're going to wrap everything in this Objective-C class.
 
@@ -251,7 +251,8 @@ To use the custom built libraries the project, replace `#import <LibTorch/LibTor
 #include "caffe2/core/timer.h"
 #include "caffe2/utils/string_utils.h"
 #include "torch/csrc/autograd/grad_mode.h"
-#include "torch/csrc/jit/serialization/import.h"
+#include "torch/csrc/jit/mobile/import.h"
+#include "torch/csrc/jit/mobile/module.h"
 #include "torch/script.h"
 ```
 
@@ -289,6 +290,12 @@ SELECTED_OP_LIST=MobileNetV2.yaml BUILD_PYTORCH_MOBILE=1 IOS_ARCH=arm64 ./script
 torch::jit::GraphOptimizerEnabledGuard guard(false);
 ```
 
+## Use PyTorch JIT interpreter
+PyTorch JIT interpreter is the default interpreter before 1.9 (a version of our PyTorch interpreter that is not as size-efficient). It will still be supported in 1.9, and can be used in CocoaPods:
+```
+pod 'LibTorch', '~>1.9.0'
+```
+
 ## iOS Tutorials
 
 Watch the following [video](https://youtu.be/amTepUIR93k) as PyTorch Partner Engineer Brad Heintz walks through steps for setting up the PyTorch Runtime for iOS projects:

_news/news-item-1.md

@@ -1,5 +1,7 @@
 ---
 order: 1
-link: https://pytorch.org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/
-summary: (영문) Introducing PyTorch Profiler - the new and improved performance tool
+link: https://pytorch.org/blog/pytorch-1.9-released
+summary: (영문) PyTorch 1.9 Release, including torch.linalg and Mobile Interpreter
 ---
+
+

_news/news-item-2.md

@@ -1,6 +1,6 @@
 ---
 order: 2
-link: https://github.com/pytorch/pytorch/releases/tag/v1.8.1
-summary: (영문) PyTorch 1.8.1 is now available with improvements and bug fixes
+link: https://pytorch.org/blog/pytorch-1.9-new-library-releases
+summary: (영문) New PyTorch Library Releases in PyTorch 1.9, including TorchVision, TorchAudio, and more
 ---
 

_news/news-item-3.md

@@ -1,6 +1,6 @@
 ---
 order: 3
-link: https://pytorch.org/blog/ecosystem_day_2021/
-summary: (영문) Announcing PyTorch Ecosystem Day - April 21, 2021
+link: https://github.com/pytorch/pytorch/releases/tag/v1.8.1
+summary: (영문) PyTorch 1.8.1 is now available with improvements and bug fixes
 ---
 

_sass/article.scss

@@ -1,6 +1,7 @@
 article.pytorch-article {
   max-width: 920px;
   margin: 0 auto;
+  padding-bottom: 90px;
 
   h2,
   h3,