Merge branch 'site' into patch-1

Author: kashif

.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

_community_blog/bringing-the-pytorch-community-together.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/doctr-joins-pytorch-ecosystem.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/mlops-workflow.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/vllm-joins-pytorch.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_stories/1.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/10.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/11.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/12.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.