Merge branch 'pytorch:site' into site

Author: NeoKish

_data/ecosystem/ptdd/2021/posters.yaml

@@ -0,0 +1,10 @@
+---
+layout: ecosystem_detail
+title: Flower
+summary: Flower - A Friendly Federated Learning Framework
+link: https://flower.dev
+summary-home: Flower - A Friendly Federated Learning Framework
+featured-home: false
+github-id: adap/flower
+date-added: 01/05/22
+---

_ecosystem/flower

@@ -0,0 +1,10 @@
+---
+layout: ecosystem_detail
+title: torchgeo
+summary: Datasets, transforms, and models for geospatial data
+link: https://github.com/microsoft/torchgeo
+summary-home: Datasets, transforms, and models for geospatial data
+featured-home: false
+github-id: microsoft/torchgeo
+date-added: 01/05/22
+---

_ecosystem/torchgeo

@@ -5,10 +5,10 @@ date: December 1, 2021
 header-image: assets/images/pytorch_developer_day_2021.png
 ---
 
-The PyTorch Developer Day is a virtual event that brings together leading researchers and developers from the Machine Learning (ML) community to join a multiple set of talks covering new software releases, ways PyTorch is being used in academia and industry, and current trends in ML development.  There will also be ample opportunity for networking with your peers and colleagues.
+The PyTorch Developer Day is a virtual event that brings together leading researchers and developers from the Machine Learning (ML) community to join a multiple set of talks covering new software releases, ways PyTorch is being used in academia and industry, and current trends in ML development. Find all the talks below:
+ 
+- [Keynotes](https://www.youtube.com/c/PyTorch/playlists?view=50&sort=dd&shelf_id=4): Learn about the innovations, the new features, updates, and release of PyTorch, and how industries are using it for production and deployment.
+ 
+- [Fireside Chat](https://youtu.be/JWdDl9Tvw6g): An informal and intimate conversation with two pioneers in the field of AI (and PyTorch) sharing their thoughts and vision for the future, commentary on top-of-mind trends they are seeing.
 
-*Call for Content Now Open!*
-
-We want to hear from you, our community. Submit your poster abstracts today. Please submit the **title** and **brief summary** of your project, tools, and libraries that could benefit PyTorch researchers in academia and industry, application developers, and ML engineers for consideration. The focus must be on academic papers, machine learning research, or open-source projects related to PyTorch development, Responsible AI or Mobile. Please no sales pitches. **Deadline for submission is September 24, 2021**.
-
-Submit your Poster abstract [HERE](http://pytorchdeveloperday.fbreg.com) You can submit your poster abstract in your registration form.
+- [Community Talks](https://www.youtube.com/watch?v=7yQ4FgtYvj8): PyTorch has grown thanks to our community. Hear from our members on the work being done with PyTorch.

_events/pytorch_developer_day.md

@@ -25,7 +25,7 @@ PyTorch is supported on Linux distributions that use [glibc](https://www.gnu.org
 ### Python
 {: #linux-python}
 
-Python 3.6 or greater is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
+Python 3.7 or greater is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
 
 > Tip: By default, you will have to use the command `python3` to run Python. If you want to use just the command `python`, instead of `python3`, you can symlink `python` to the `python3` binary.
 

_get_started/installation/linux.md

@@ -15,7 +15,7 @@ PyTorch is supported on macOS 10.10 (Yosemite) or above.
 ### Python
 {: #mac-python}
 
-It is recommended that you use Python 3.5 or greater, which can be installed either through the Anaconda package manager (see [below](#anaconda)), [Homebrew](https://brew.sh/), or the [Python website](https://www.python.org/downloads/mac-osx/).
+It is recommended that you use Python 3.7 or greater, which can be installed either through the Anaconda package manager (see [below](#anaconda)), [Homebrew](https://brew.sh/), or the [Python website](https://www.python.org/downloads/mac-osx/).
 
 ### Package Manager
 {: #mac-package-manager}

_get_started/installation/mac.md

@@ -18,7 +18,7 @@ PyTorch is supported on the following Windows distributions:
 ### Python
 {: #windows-python}
 
-Currently, PyTorch on Windows only supports Python 3.x; Python 2.x is not supported.
+Currently, PyTorch on Windows only supports Python 3.7-3.9; Python 2.x is not supported.
 
 As it is not installed by default on Windows, there are multiple ways to install Python:
 

_get_started/installation/windows.md

@@ -18,6 +18,10 @@
             <span class="dropdown-title">Ecosystem Day - 2021</span>
             <p>See the posters presented at ecosystem day 2021</p>
           </a>
+          <a class="nav-dropdown-item" href="{{ site.baseurl}}/ecosystem/ptdd/2021">
+            <span class="dropdown-title">Developer Day - 2021</span>
+            <p>See the posters presented at developer day 2021</p>
+          </a>
         </div>
       </div>
     </li>

_includes/main_menu.html

@@ -37,6 +37,9 @@
           <li>
             <a href="{{ site.baseurl }}/ecosystem/pted/2021">Ecosystem Day 2021</a>
           </li>
+          <li>
+            <a href="{{ site.baseurl }}/ecosystem/ptdd/2021">Developer Day 2021</a>
+          </li>
         </ul>
 
         <li class="{% if current[1] == 'mobile' %}active{% endif %}">

_includes/mobile_menu.html

@@ -8,7 +8,7 @@ var supportedOperatingSystems = new Map([
 var supportedComputePlatforms = new Map([
   ['cuda10.2', new Set(['linux', 'windows'])],
   ['cuda11.x', new Set(['linux', 'windows'])],
-  ['rocm4.2', new Set(['linux'])],
+  ['rocm4.x', new Set(['linux'])],
   ['accnone', new Set(['linux', 'macos', 'windows'])],
 ]);
 
@@ -115,21 +115,35 @@ function disableUnsupportedPlatforms(os) {
   });
 }
 
-// Change CUDA version depending on build type
+// Change compute versiosn depending on build type
 function changeCUDAVersion(ptbuild) {
-  var element = document.getElementById("cuda11.x");
-  if (element == null) {
+  var cuda_element = document.getElementById("cuda11.x");
+  var rocm_element = document.getElementById("rocm4.x");
+  if (cuda_element == null) {
     console.log("Failed to find cuda11.x element");
     return;
   }
-  if (element.childElementCount != 1) {
+  if (cuda_element.childElementCount != 1) {
     console.log("Unexpected number of children for cuda11.x element");
     return;
   }
-  if (ptbuild == "preview" || ptbuild == "stable") {
-    element.children[0].textContent = "CUDA 11.3";
+  if (rocm_element == null) {
+    console.log("Failed to find rocm4.x element");
+    return;
+  }
+  if (rocm_element.childElementCount != 1) {
+    console.log("Unexpected number of children for rocm4.x element");
+    return;
+  }
+  if (ptbuild == "preview") {
+    rocm_element.children[0].textContent = "ROCM 4.3 (beta)";
+    cuda_element.children[0].textContent = "CUDA 11.3";
+  } else if (ptbuild == "stable") {
+    rocm_element.children[0].textContent = "ROCM 4.2 (beta)";
+    cuda_element.children[0].textContent = "CUDA 11.3";
   } else {
-    element.children[0].textContent = "CUDA 11.1";
+    rocm_element.children[0].textContent = "ROCM 4.2 (beta)";
+    cuda_element.children[0].textContent = "CUDA 11.1";
   }
 }
 

_includes/quick-start-module.js

@@ -35,7 +35,7 @@
         <div class="option-text">PyTorch Build</div>
       </div>
       <div class="col-md-4 option block version selected" id="stable">
-        <div class="option-text">Stable (1.10.1)</div>
+        <div class="option-text">Stable (1.10.2)</div>
       </div>
       <div class="col-md-4 option block version" id="preview">
         <div class="option-text">Preview (Nightly)</div>
@@ -96,7 +96,7 @@
       <div class="col-md-3 option block version" id="cuda11.x">
         <div class="option-text">CUDA 11.3</div>
       </div>
-      <div class="col-md-3 option block version" id="rocm4.2">
+      <div class="col-md-3 option block version" id="rocm4.x">
         <div class="option-text">ROCm 4.2 (beta)</div>
       </div>
       <div class="col-md-3 option block version" id="accnone">

_includes/quick_start_local.html

@@ -16,6 +16,11 @@ To get started with PyTorch on iOS, we recommend exploring the following [HelloW
 
 HelloWorld is a simple image classification application that demonstrates how to use PyTorch C++ libraries on iOS. The code is written in Swift and uses Objective-C as a bridge.
 
+### Requirements
+
+- XCode 11.0 or above
+- iOS 12.0 or above
+
 ### 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 - [MobileNet v2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/), which is already packaged in [TorchVision](https://pytorch.org/docs/stable/torchvision/index.html). To install it, run the command below.
@@ -32,7 +37,7 @@ Once we have TorchVision installed successfully, let's navigate to the HelloWorl
 python trace_model.py
 ```
 
-If everything works well, we should have our model - `model.pt` generated in the `HelloWorld` folder. Now copy the model file to our application folder `HelloWorld/model`.
+If everything works well, `model.pt` should be generated and saved in the `HelloWorld/HelloWorld/model` folder.
 
 > To find out more details about TorchScript, please visit [tutorials on pytorch.org](https://pytorch.org/tutorials/advanced/cpp_export.html)
 
@@ -65,7 +70,7 @@ guard var pixelBuffer = resizedImage.normalized() else {
 }
 ```
 
-We first load the image from our bundle and resize it to 224x224. Then we call this `normalized()` category method to normalized the pixel buffer. Let's take a closer look at the code below.
+We first load the image from our bundle and resize it to 224x224. Then we call this `normalized()` category method to normalize the pixel buffer. Let's take a closer look at the code below.
 
 ```swift
 var normalizedBuffer: [Float32] = [Float32](repeating: 0, count: w * h * 3)
@@ -82,7 +87,7 @@ The code might look weird at first glance, but it’ll make sense once we unders
 
 ####  TorchScript Module
 
-Now that we have preprocessed our input data and we have a pre-trained TorchScript model, the next step is to use them to run predication. To do that, we'll first load our model into the application.
+Now that we have preprocessed our input data and we have a pre-trained TorchScript model, the next step is to use them to run prediction. To do that, we'll first load our model into the application.
 
 ```swift
 private lazy var module: TorchModule = {