Merge branch 'site' into Chillee-patch-1

Author: Chillee

.github/workflows/update-quick-start-module.yml

@@ -66,12 +66,12 @@ jobs:
     runs-on: "ubuntu-20.04"
     environment: pytorchbot-env
     steps:
-      - name: Checkout builder
+      - name: Checkout pytorch.github.io
         uses: actions/checkout@v2
       - name: Setup Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.8
+          python-version: 3.9
           architecture: x64
       - name: Create json file
         shell: bash
@@ -105,8 +105,8 @@ jobs:
         uses: peter-evans/create-pull-request@v3
         with:
           token: ${{ secrets.PYTORCHBOT_TOKEN }}
-          commit-message: Modify published_versions.json file
-          title: '[Getting Started Page] Modify published_versions.json file'
+          commit-message: Modify published_versions.json, releases.json and quick-start-module.js
+          title: '[Getting Started Page] Modify published_versions.json, releases.json and quick-start-module.js'
           body: >
             This PR is auto-generated. It updates Getting Started page
           labels: automated pr

.github/workflows/validate-quick-start-module.yml

@@ -18,16 +18,14 @@ on:
 
 jobs:
   validate-nightly-binaries:
-    uses: pytorch/builder/.github/workflows/validate-binaries.yml@main
+    uses: pytorch/test-infra/.github/workflows/validate-binaries.yml@main
     with:
       os: all
       channel: "nightly"
-      ref: main
   validate-release-binaries:
     if: always()
-    uses: pytorch/builder/.github/workflows/validate-binaries.yml@main
+    uses: pytorch/test-infra/.github/workflows/validate-binaries.yml@main
     needs: validate-nightly-binaries
     with:
       os: all
       channel: "release"
-      ref: main

_board_info/arm.md

@@ -0,0 +1,9 @@
+---
+title: arm
+summary: ''
+link: https://www.arm.com/
+image: /assets/images/members/arm-logo.svg
+class: pytorch-resource
+order: 2
+featured-home: true
+---

_board_info/amazon-web-services.md renamed to _board_info/aws.md

@@ -0,0 +1,8 @@
+---
+title: 'Bringing the PyTorch Community Together'
+author: Team PyTorch
+ext_url: /blog/bringing-the-pytorch-community-together/
+date: January 22, 2025
+---
+
+As we step into a new year, it’s a great moment to reflect on the incredible community events that made 2024 a memorable year for the PyTorch Foundation. Global meetups, events, and conferences brought the community together to learn, connect, and grow. Here’s a quick recap of the year’s highlights and what to expect in 2025.

_community_blog/bringing-the-pytorch-community-together.md

@@ -0,0 +1,8 @@
+---
+title: "docTR joins PyTorch Ecosystem: From Pixels to Data, Building a Recognition Pipeline with PyTorch and docTR"
+author: Olivier Dulcy & Sebastian Olivera, Mindee
+ext_url: /blog/doctr-joins-pytorch-ecosystem/
+date: Dec 18, 2024
+---
+
+We’re thrilled to announce that the docTR project has been integrated into the PyTorch ecosystem! This integration ensures that docTR aligns with PyTorch’s standards and practices, giving developers a reliable, community-backed solution for powerful OCR workflows.

_community_blog/doctr-joins-pytorch-ecosystem.md

@@ -0,0 +1,8 @@
+---
+title: "MLOps Workflow Simplified for PyTorch with Arm and GitHub Collaboration"
+author: Eric Sondhi, Arm
+ext_url: /blog/mlops-workflow/
+date: Jan 15, 2025
+---
+
+PyTorch is one of the most widely used and most powerful deep learning frameworks for training and deploying complex neural networks. It has never been easier to train and deploy AI applications, and low-cost, high-performance, energy-efficient hardware, tools, and technology for creating optimized workflows are more accessible than ever.  But data science, machine learning, and devops can be deep topics unto themselves, and it can be overwhelming for developers with one specialty to see how they all come together in the real world, or even to know where to get started. 

_community_blog/mlops-workflow.md

@@ -0,0 +1,42 @@
+---
+layout: blog_detail
+title: "PyTorch Shanghai Meetup Notes"
+ext_url: /blog/pytorch-shanghai-notes/
+date: Sep 8, 2024
+---
+
+We are honored to successfully host the PyTorch Shanghai Meetup on August 15, 2024. This Meetup has received great attention from the industry. We invited senior PyTorch developers from Intel and Huawei as guest speakers, who shared their valuable experience and the latest technical trends. In addition, this event also attracted PyTorch enthusiasts from many technology companies and well-known universities. A total of more than 40 participants gathered together to discuss and exchange the latest applications and technological advances of PyTorch.  
+
+This Meetup not only strengthened the connection between PyTorch community members, but also provided a platform for local AI technology enthusiasts to learn, communicate and grow. We look forward to the next gathering to continue to promote the development of PyTorch technology in the local area.
+
+## 1. PyTorch Foundation Updates
+
+![man instructing students](/assets/images/pytorch-shanghai-notes/fg2.jpg){:style="width:100%"}
+
+PyTorch Board member Fred Li shared the latest updates in the PyTorch community, He reviewed the development history of the PyTorch community, explained in detail the growth path of community developers, encouraged everyone to delve deeper into technology, and introduced the upcoming PyTorch Conference 2024 related matters.
+
+## 2. Intel’s Journey with PyTorch Democratizing AI with ubiquitous hardware and open software
+
+PyTorch CPU module maintainer Jiong Gong shared 6-year technical contributions from Intel to PyTorch and its ecosystem, explored the remarkable advancements that Intel has made in both software and hardware democratizing AI, ensuring accessibility, and optimizing performance across a diverse range of Intel hardware platforms.
+
+![man instructing students](/assets/images/pytorch-shanghai-notes/fg3.jpg){:style="width:100%"}
+
+## 3. Exploring Multi-Backend Support in PyTorch Ecosystem: A Case Study of Ascend
+
+![man instructing students](/assets/images/pytorch-shanghai-notes/fg4.jpg){:style="width:100%"}
+
+Fengchun Hua, a PyTorch contributor from Huawei, took Huawei Ascend NPU as an example to demonstrate the latest achievements in multi-backend support for PyTorch applications. He introduced the hardware features of Huawei Ascend NPU and the infrastructure of CANN (Compute Architecture for Neural Networks), and explained the key achievements and innovations in native support work. He also shared the current challenges and the next work plan.  
+
+Yuanhao Ji, another PyTorch contributor from Huawei, then introduced the Autoload Device Extension proposal, explained its implementation details and value in improving the scalability of PyTorch, and introduced the latest work progress of the PyTorch Chinese community.
+
+## 4. Intel XPU Backend for Inductor
+
+![man instructing students](/assets/images/pytorch-shanghai-notes/fg5.jpg){:style="width:100%"}
+
+Eikan is a PyTorch contributor from Intel. He focuses on torch.compile stack for both Intel CPU and GPU. In this session, Eikan presented Intel's efforts on torch.compile for Intel GPUs. He provided updates on the current status of Intel GPUs within PyTorch, covering both functionality and performance aspects. Additionally, Eikan used Intel GPU as a case study to demonstrate how to integrate a new backend into the Inductor using Triton. 
+
+## 5. PyTorch PrivateUse1 Evolution Approaches and Insights
+
+![man instructing students](/assets/images/pytorch-shanghai-notes/fg6.jpg){:style="width:100%"}
+
+Jiawei Li, a PyTorch collaborator from Huawei, introduced PyTorch's Dispatch mechanism and emphasized the limitations of DIspatchKey. He took Huawei Ascend NPU as an example to share the best practices of the PyTorch PrivateUse1 mechanism. He mentioned that while using the PrivateUse1 mechanism, Huawei also submitted many improvements and bug fixes for the mechanism to the PyTorch community. He also mentioned that due to the lack of upstream CI support for out-of-tree devices, changes in upstream code may affect their stability and quality, and this insight was recognized by everyone.  

_community_blog/pytorch-shanghai-notes.md

@@ -0,0 +1,10 @@
+---
+title: "vLLM Joins PyTorch Ecosystem: Easy, Fast, and Cheap LLM Serving for Everyone"
+author: vLLM Team
+ext_url: /blog/vllm-joins-pytorch/
+date: Dec 9, 2024
+---
+
+We’re thrilled to announce that the [vLLM project](https://github.com/vllm-project/vllm) has become a PyTorch ecosystem project, and joined the PyTorch ecosystem family!
+
+Running large language models (LLMs) is both resource-intensive and complex, especially as these models scale to hundreds of billions of parameters. That’s where vLLM comes in — a high-throughput, memory-efficient inference and serving engine designed for LLMs.

_community_blog/vllm-joins-pytorch.md

@@ -0,0 +1,7 @@
+---
+title: 'How Outreach Productionizes PyTorch-based Hugging Face Transformers for NLP'
+ext_url: https://www.databricks.com/blog/2021/05/14/how-outreach-productionizes-pytorch-based-hugging-face-transformers-for-nlp.html
+date: May 14, 2021
+tags: ["Advertising & Marketing"]
+---
+At Outreach, a leading sales engagement platform, our data science team is a driving force behind our innovative product portfolio largely driven by deep learning and AI. We recently announced enhancements to the Outreach Insights feature, which is powered by the proprietary Buyer Sentiment deep learning model developed by the Outreach Data Science team. This model allows sales teams to deepen their understanding of customer sentiment through the analysis of email reply content, moving from just counting the reply rate to classification of the replier’s intent.