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

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

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

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

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

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---
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title: 'Bringing the PyTorch Community Together'
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author: Team PyTorch
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ext_url: /blog/bringing-the-pytorch-community-together/
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date: January 22, 2025
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---
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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

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---
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title: "docTR joins PyTorch Ecosystem: From Pixels to Data, Building a Recognition Pipeline with PyTorch and docTR"
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author: Olivier Dulcy & Sebastian Olivera, Mindee
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ext_url: /blog/doctr-joins-pytorch-ecosystem/
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date: Dec 18, 2024
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---
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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

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---
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title: "MLOps Workflow Simplified for PyTorch with Arm and GitHub Collaboration"
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author: Eric Sondhi, Arm
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ext_url: /blog/mlops-workflow/
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date: Jan 15, 2025
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---
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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

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---
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title: "vLLM Joins PyTorch Ecosystem: Easy, Fast, and Cheap LLM Serving for Everyone"
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author: vLLM Team
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ext_url: /blog/vllm-joins-pytorch/
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date: Dec 9, 2024
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---
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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!
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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

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---
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title: 'How Outreach Productionizes PyTorch-based Hugging Face Transformers for NLP'
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ext_url: https://www.databricks.com/blog/2021/05/14/how-outreach-productionizes-pytorch-based-hugging-face-transformers-for-nlp.html
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date: May 14, 2021
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tags: ["Advertising & Marketing"]
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---
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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

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---
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title: 'Solliance makes headlines with cryptocurrency news analysis platform powered by Azure Machine Learning, PyTorch'
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ext_url: https://medium.com/pytorch/solliance-makes-headlines-with-cryptocurrency-news-analysis-platform-powered-by-azure-machine-52a2a290fefb
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date: Mar 14, 2022
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tags: ["Finance"]
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---
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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

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---
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title: 'Create a Wine Recommender Using NLP on AWS'
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ext_url: https://www.capitalone.com/tech/machine-learning/create-wine-recommender-using-nlp/
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date: March 2, 2022
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tags: ["Finance"]
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---
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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

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---
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title: 'Crayon boosts speed, accuracy of healthcare auditing process using Azure Machine Learning and PyTorch'
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ext_url: https://www.microsoft.com/en/customers/story/1503427278296945327-crayon-partner-professional-services-azure
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date: June 28, 2022
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tags: ["Healthcare"]
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---
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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/13.md

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---
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title: 'Extracting value from siloed healthcare data using federated learning with Azure Machine Learning'
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ext_url: https://www.microsoft.com/en/customers/story/1587521717158304168-microsoft-partner-professional-services-azure
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date: December 30, 2022
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tags: ["Healthcare"]
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---
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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/14.md

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---
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title: 'HippoScreen Improves AI Performance by 2.4x with oneAPI Tools'
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ext_url: https://www.intel.com/content/www/us/en/developer/articles/case-study/hipposcreen-boosts-ai-performance-2-4x-with-oneapi.html
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date: Feb 21, 2023
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tags: ["Healthcare"]
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---
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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/16.md

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---
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title: "Disney's Creative Genome by Miquel Farré"
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ext_url: https://www.youtube.com/watch?v=KuDxEhHk2Rk
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date: Apr 27, 2021
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tags: ["Media & Entertainment"]
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---
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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/17.md

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---
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title: 'How Disney uses PyTorch for animated character recognition'
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ext_url: https://medium.com/pytorch/how-disney-uses-pytorch-for-animated-character-recognition-a1722a182627
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date: Jul 16, 2020
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tags: ["Media & Entertainment"]
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---
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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/18.md

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---
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title: 'Machine Learning at Tubi: Powering Free Movies, TV and News for All'
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ext_url: https://medium.com/pytorch/machine-learning-at-tubi-powering-free-movies-tv-and-news-for-all-51499643018e
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date: Feb 25, 2021
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tags: ["Media & Entertainment"]
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---
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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/19.md

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---
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title: 'How Pixar uses AI and GANs to create high-resolution content'
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ext_url: https://venturebeat.com/business/how-pixar-uses-ai-and-gans-to-create-high-resolution-content/
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date: July 17, 2020
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tags: ["Media & Entertainment"]
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---
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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/2.md

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---
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title: 'Amazon Ads Uses PyTorch and AWS Inferentia to Scale Models for Ads Processing'
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ext_url: /blog/amazon-ads-case-study/
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date: February 24, 2022
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tags: ["Advertising & Marketing", "Retail"]
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---
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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/20.md

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---
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title: 'Running BERT model inference on AWS Inf1: From model compilation to speed comparison'
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ext_url: https://note.com/asahi_ictrad/n/nf5195eb53b88
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date: November 21, 2021
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tags: ["Media & Entertainment"]
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---
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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/21.md

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---
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title: 'Ambient Clinical Intelligence: Generating Medical Reports with PyTorch'
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ext_url: /blog/ambient-clinical-intelligence-generating-medical-reports-with-pytorch/
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date: May 12, 2022
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tags: ["Medical"]
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---
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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/22.md

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---
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title: 'AstraZeneca is using PyTorch-powered algorithms to discover new drugs'
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ext_url: https://www.zdnet.com/article/astrazeneca-is-using-pytorch-powered-algorithms-to-discover-new-drugs/
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date: Sept. 30, 2020
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tags: ["Medical"]
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---
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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/23.md

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---
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title: 'Deploying huggingface‘s BERT to production with pytorch/serve'
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ext_url: https://medium.com/analytics-vidhya/deploy-huggingface-s-bert-to-production-with-pytorch-serve-27b068026d18
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date: Apr 25, 2020
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tags: ["Medical"]
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---
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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.

‎_community_stories/24.md

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---
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title: 'How AI is Helping Vets to Help our Pets'
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ext_url: https://medium.com/pytorch/how-ai-is-helping-vets-to-help-our-pets-e6e3d58c052e
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date: Sep 7, 2021
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tags: ["Medical"]
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---
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1 in 4 dogs, and 1 in 5 cats, will develop cancer at some point in their lives. Pets today have a better chance of being successfully treated than ever, thanks to advances in early recognition, diagnosis and treatment.

‎_community_stories/25.md

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title: 'How theator Built a Continuous Training Framework To Scale up Its Surgical Intelligence Platform'
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ext_url: https://medium.com/pytorch/how-theator-built-a-continuous-training-framework-to-scale-up-its-surgical-intelligence-platform-b5135e3229fd
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date: Dec 17, 2020
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tags: ["Medical"]
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---
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Performing surgery is largely about decision making. As Dr. Frank Spencer put it in 1978, “A skillfully performed operation is about 75% decision making and 25% dexterity”. Five decades later, and the surgical field is finally — albeit gradually — implementing advances in data science and AI to enhance surgeons’ ability to make the best decisions in the operating room. That’s where theator comes in: the company is re-imagining surgery with a Surgical Intelligence platform that leverages highly advanced AI, specifically machine learning and computer vision technology, to analyze every step, event, milestone, and critical junction of surgical procedures — significantly boosting surgeons’ overall performance.

‎_community_stories/26.md

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title: 'Speeding up drug discovery with advanced machine learning'
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ext_url: https://medium.com/pytorch/speeding-up-drug-discovery-with-advanced-machine-learning-b17d59e0daa6
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date: Sep 30, 2020
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tags: ["Medical"]
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---
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Whatever our job title happens to be at AstraZeneca, we’re seekers. I’m part of the Biological Insights Knowledge Graph (BIKG) team. We help scientists comb through massive amounts of data in our quest to find the information we need to help us deliver life-changing medicines.

‎_community_stories/27.md

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---
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title: 'Using PyTorch to streamline machine-learning projects'
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ext_url: https://www.zdnet.com/article/using-pytorch-to-streamline-machine-learning-projects/
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date: Dec. 17, 2020
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tags: ["Medical"]
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---
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For many surgeons, the possibility of going back into the operating room to review the actions they carried out on a patient could provide invaluable medical insights.

‎_community_stories/28.md

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---
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title: 'Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS'
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ext_url: https://aws.amazon.com/blogs/machine-learning/run-inference-at-scale-for-openfold-a-pytorch-based-protein-folding-ml-model-using-amazon-eks/
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date: Oct. 25, 2022
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tags: ["Medical"]
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---
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In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive, and time consuming, and can take years when using traditional methods such as X-ray diffraction. Applying machine learning (ML) to predict these structures can significantly accelerate the time to predict protein structures—from years to hours. Several high-profile research teams have released algorithms such as AlphaFold2 (AF2), RoseTTAFold, and others. These algorithms were recognized by Science magazine as the 2021 Breakthrough of the Year.

‎_community_stories/29.md

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title: 'Optimize Protein Folding Costs with OpenFold on AWS Batch'
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ext_url: https://aws.amazon.com/blogs/hpc/optimize-protein-folding-costs-with-openfold-on-aws-batch/
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date: Oct. 4, 2022
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tags: ["Medical"]
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---
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Knowing the physical structure of proteins is an important part of the drug discovery process. Machine learning (ML) algorithms like AlphaFold v2.0 significantly reduce the cost and time needed to generate usable protein structures. These projects have also inspired development of AI-driven workflows for de novo protein design and protein-ligand interaction analysis.

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