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.

_community_stories/1.md

@@ -0,0 +1,7 @@
+---
+title: 'Solliance makes headlines with cryptocurrency news analysis platform powered by Azure Machine Learning, PyTorch'
+ext_url: https://medium.com/pytorch/solliance-makes-headlines-with-cryptocurrency-news-analysis-platform-powered-by-azure-machine-52a2a290fefb
+date: Mar 14, 2022
+tags: ["Finance"]
+---
+Solliance delivers cutting-edge solutions that fill gaps across a wide variety of industries. Through its recent collaboration with Baseline, Solliance revolutionizes the cryptocurrency trading experience, extracting news insights from more than 150,000 global sources in near real time. To manage Baseline workloads, Solliance brought Microsoft Azure Machine Learning and PyTorch together for maximum processing power and deep learning capabilities. The result: investors can get under the headlines and see which specific news metrics are moving the volatile crypto market to make more informed trading decisions, while Baseline can release new features in weeks instead of months.

_community_stories/10.md

@@ -0,0 +1,7 @@
+---
+title: 'Create a Wine Recommender Using NLP on AWS'
+ext_url: https://www.capitalone.com/tech/machine-learning/create-wine-recommender-using-nlp/
+date: March 2, 2022
+tags: ["Finance"]
+---
+In this tutorial, we’ll build a simple machine learning pipeline using a BERT word embedding model and the Nearest Neighbor algorithm to recommend wines based on user inputted preferences. To create and power this recommendation engine, we’ll leverage AWS’s SageMaker platform, which provides a fully managed way for us to train and deploy our service.

_community_stories/11.md

@@ -0,0 +1,7 @@
+---
+title: 'Crayon boosts speed, accuracy of healthcare auditing process using Azure Machine Learning and PyTorch'
+ext_url: https://www.microsoft.com/en/customers/story/1503427278296945327-crayon-partner-professional-services-azure
+date: June 28, 2022
+tags: ["Healthcare"]
+---
+Healthcare providers need to be able to verify that they’re maintaining the highest operating safety and efficacy standards. Those standards are set by a national accreditation organization whose surveyors, often healthcare professionals themselves, regularly visit facilities and document situations that might need to be corrected or brought back in line with the latest rules and policies. That assessment and accreditation process generates a huge amount of data, and even the most experienced surveyors struggle to keep ahead of the ongoing development of thousands of policy rules that might be relevant in any particular scenario. Vaagan and his team took on the task of fixing the issue by building a machine learning solution that could ingest text from those reports and return a top ten list of the latest associated rules with unprecedented accuracy. They used Azure technology, development tools, and services to bring that solution to fruition. Crayon customers report clear time savings with the new healthcare solution. Just as important, the solution provides consistent responses that aren’t subject to the vagaries of individual interpretation or potentially out-of-date data.

_community_stories/12.md

@@ -0,0 +1,7 @@
+---
+title: 'Extracting value from siloed healthcare data using federated learning with Azure Machine Learning'
+ext_url: https://www.microsoft.com/en/customers/story/1587521717158304168-microsoft-partner-professional-services-azure
+date: December 30, 2022
+tags: ["Healthcare"]
+---
+Sensitive information such as healthcare data is often siloed within health organization boundaries. This has posed a challenge to machine learning models used by the health and life sciences industry that require data for training purposes. To improve patient care and accelerate health industry progression, the Microsoft Health & Life Sciences AI group used a federated learning setup to train their biomedical natural language processing service, Text Analytics for Health, while preserving the trust boundaries of siloed data. The federated learning framework was built using Microsoft Azure Machine Learning and open-source technologies to help organizations analyze siloed data and build new applications without compromising data privacy.

_community_stories/13.md

@@ -0,0 +1,7 @@
+---
+title: 'HippoScreen Improves AI Performance by 2.4x with oneAPI Tools'
+ext_url: https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html
+date: Feb 21, 2023
+tags: ["Healthcare"]
+---
+The Taiwan-based neurotechnology startup used tools and frameworks in the Intel® oneAPI Base and AI Analytics Toolkits to the improve efficiency and build times of deep-learning models used in its Brain Waves AI system. As a result, HippoScreen is able to broaden the system’s applications to a wider range of psychiatric conditions and diseases.

_community_stories/14.md

@@ -0,0 +1,7 @@
+---
+title: "Disney's Creative Genome by Miquel Farré"
+ext_url: https://www.youtube.com/watch?v=KuDxEhHk2Rk
+date: Apr 27, 2021
+tags: ["Media & Entertainment"]
+---
+Miquel Farré is a senior technology manager at Disney, taking the lead on projects at the intersection of video technology, machine learning and web applications. Metadata that drives content searchability is most often indexed at the title level, with limited governance and high ambiguity; at best, keyword metadata has been added to a title as a layer of enrichment.

_community_stories/16.md

@@ -0,0 +1,7 @@
+---
+title: 'How Disney uses PyTorch for animated character recognition'
+ext_url: https://medium.com/pytorch/how-disney-uses-pytorch-for-animated-character-recognition-a1722a182627
+date: Jul 16, 2020
+tags: ["Media & Entertainment"]
+---
+The long and incremental evolution of the media industry, from a traditional broadcast and home video model, to a more mixed model with increasingly digitally-accessible content, has accelerated the use of machine learning and artificial intelligence (AI). Advancing the implementation of these technologies is critical for a company like Disney that has produced nearly a century of content, as it allows for new consumer experiences and enables new applications for illustrators and writers to create the highest-quality content.

_community_stories/17.md

@@ -0,0 +1,7 @@
+---
+title: 'Machine Learning at Tubi: Powering Free Movies, TV and News for All'
+ext_url: https://medium.com/pytorch/machine-learning-at-tubi-powering-free-movies-tv-and-news-for-all-51499643018e
+date: Feb 25, 2021
+tags: ["Media & Entertainment"]
+---
+In this blog series, our aim is to highlight the nuances of Machine Learning in Tubi’s Ad-based Video on Demand (AVOD) space as practiced at Tubi. Machine Learning helps solve myriad problems involving recommendations, content understanding and ads. We extensively use PyTorch for several of these use cases as it provides us the flexibility, computational speed and ease of implementation to train large scale deep neural networks using GPUs.

_community_stories/18.md

@@ -0,0 +1,7 @@
+---
+title: 'How Pixar uses AI and GANs to create high-resolution content'
+ext_url: https://venturebeat.com/business/how-pixar-uses-ai-and-gans-to-create-high-resolution-content/
+date: July 17, 2020
+tags: ["Media & Entertainment"]
+---
+As digital animators continue to push the boundaries of technology and creativity, the technical teams that support them are turning to artificial intelligence and machine learning to deliver the tools they need. That’s the case at Pixar, where the company has made new machine learning breakthroughs it hopes will both improve quality and reduce costs.

_community_stories/19.md

@@ -0,0 +1,7 @@
+---
+title: 'Amazon Ads Uses PyTorch and AWS Inferentia to Scale Models for Ads Processing'
+ext_url: /blog/amazon-ads-case-study/
+date: February 24, 2022
+tags: ["Advertising & Marketing", "Retail"]
+---
+Amazon Ads uses PyTorch, TorchServe, and AWS Inferentia to reduce inference costs by 71% and drive scale out. Amazon Ads helps companies build their brand and connect with shoppers through ads shown both within and beyond Amazon’s store, including websites, apps, and streaming TV content in more than 15 countries. Businesses and brands of all sizes, including registered sellers, vendors, book vendors, Kindle Direct Publishing (KDP) authors, app developers, and agencies can upload their own ad creatives, which can include images, video, audio, and, of course, products sold on Amazon.

_community_stories/2.md

@@ -0,0 +1,7 @@
+---
+title: 'Running BERT model inference on AWS Inf1: From model compilation to speed comparison'
+ext_url: https://note.com/asahi_ictrad/n/nf5195eb53b88
+date: November 21, 2021 
+tags: ["Media & Entertainment"]
+---
+In this tech blog, we will compare the speed and cost of Inferentia, GPU, and CPU for a BERT sequence labeling example. We also provide a helpful tutorial on the steps for model compilation and inference on Inf1 instances.

_community_stories/20.md

@@ -0,0 +1,7 @@
+---
+title: 'Ambient Clinical Intelligence: Generating Medical Reports with PyTorch'
+ext_url: /blog/ambient-clinical-intelligence-generating-medical-reports-with-pytorch/
+date: May 12, 2022
+tags: ["Medical"]
+---
+Complete and accurate clinical documentation is an essential tool for tracking patient care. It allows for treatment plans to be shared among care teams to aid in continuity of care and ensures a transparent and effective process for reimbursement.

_community_stories/21.md

@@ -0,0 +1,7 @@
+---
+title: 'AstraZeneca is using PyTorch-powered algorithms to discover new drugs'
+ext_url: https://www.zdnet.com/article/astrazeneca-is-using-pytorch-powered-algorithms-to-discover-new-drugs/
+date: Sept. 30, 2020
+tags: ["Medical"]
+---
+Since it launched in 2017, Facebook's machine-learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot-farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. 

_community_stories/22.md

@@ -0,0 +1,7 @@
+---
+title: 'Deploying huggingface‘s BERT to production with pytorch/serve'
+ext_url: https://medium.com/analytics-vidhya/deploy-huggingface-s-bert-to-production-with-pytorch-serve-27b068026d18
+date: Apr 25, 2020
+tags: ["Medical"]
+---
+TL;DR: pytorch/serve is a new awesome framework to serve torch models in production. This story teaches you how to use it for huggingface/transformers models like BERT.