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<p class="blog-date">March 19, 2025</p>
<h4>
<a href="/blog/sglang-joins-pytorch/">SGLang Joins PyTorch Ecosystem: Efficient LLM Serving Engine</a>
</h4>
<p>We’re thrilled to announce that the SGLang project has been integrated into the PyTorch ecosystem! This integration ensures that SGLang aligns with PyTorch’s standards and practices, providing developers with a reliable and community-supported framework for fast and flexible serving of LLMs.
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<p class="blog-date">March 16, 2025</p>
<h4>
<a href="/blog/pytorch-at-gtc/">PyTorch at GTC 2025</a>
</h4>
<p>GTC is coming back to San Jose on March 17–21, 2025. Join PyTorch Foundation members Arm, AWS, Google Cloud, IBM, Lightning AI, Meta, Microsoft Azure, Snowflake, and thousands of developers as we celebrate PyTorch. Together learn how AI & accelerated computing are helping humanity solve our most complex challenges.
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<p class="blog-date">March 07, 2025</p>
<h4>
<a href="/blog/pt-fedora-os-communities/">Powering AI with PyTorch, Fedora, and Open Source Communities</a>
</h4>
<p>At DevConf.IN 2025 in Pune, I had the opportunity to host a PyTorch Meetup on February 28th. The session, titled “Powering AI with PyTorch, Fedora, and Open Source Communities” was aimed at introducing PyTorch to students and professionals, explaining why PyTorch+Fedora form an ideal AI development platform. The other key aspect I covered was collaboration between open source communities.
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<p class="blog-date">February 19, 2025</p>
<h4>
<a href="/blog/optimize-llms/">Optimize LLMs for Efficiency & Sustainability</a>
</h4>
<p>The rapid growth of large language model (LLM) applications is linked to rapid growth in energy demand. According to the International Energy Agency (IEA), data center electricity consumption is projected to roughly double by 2026 primarily driven by AI. This is due to the energy-intensive training requirements for massive LLMs – however, the increase in AI Inferencing workloads also plays a role. For example, compared with traditional search queries, a single AI inference can consume about 1...</p>
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<p class="blog-date">February 12, 2025</p>
<h4>
<a href="/blog/datathon-2025/">Solve Real-Word AI Challenges with PyTorch at Datathon 2025: DataOrbit</a>
</h4>
<p>We’re excited to have PyTorch sponsor Datathon 2025: DataOrbit, a place where students can collaborate with a team to solve problems using real-world datasets! This event, hosted by Data Science UCSB in collaboration with Gaucho Sports Analytics and ACM@UCSB, will take place on February 22–23rd, 2025 at UC Santa Barbara, with the incredible opportunity to present your project to a panel of corporate and faculty judges – including the executive director of Pytorch! – for a chance to win prizes...</p>
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<p class="blog-date">January 22, 2025</p>
<h4>
<a href="/blog/bringing-the-pytorch-community-together/">Bringing the PyTorch Community Together</a>
</h4>
<p>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.
</p>
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<p class="blog-date">January 15, 2025</p>
<h4>
<a href="/blog/mlops-workflow/">MLOps Workflow Simplified for PyTorch with Arm and GitHub Collaboration</a>
</h4>
<p>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 ...</p>
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<p class="blog-date">December 18, 2024</p>
<h4>
<a href="/blog/doctr-joins-pytorch-ecosystem/">docTR joins PyTorch Ecosystem: From Pixels to Data, Building a Recognition Pipeline with PyTorch and docTR</a>
</h4>
<p>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.
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<p class="blog-date">December 09, 2024</p>
<h4>
<a href="/blog/vllm-joins-pytorch/">vLLM Joins PyTorch Ecosystem: Easy, Fast, and Cheap LLM Serving for Everyone</a>
</h4>
<p>We’re thrilled to announce that the vLLM project has become a PyTorch ecosystem project, and joined the PyTorch ecosystem family!
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<p class="blog-date">September 08, 2024</p>
<h4>
<a href="/blog/pytorch-shanghai-notes/">PyTorch Shanghai Meetup Notes</a>
</h4>
<p>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 lat...</p>
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<p class="blog-date">May 12, 2024</p>
<h4>
<a href="/blog/enhancing-deep-learning/">Enhancing Deep Learning Workflows: PyTorch Ecosystem Tools</a>
</h4>
<p>Welcome to the thriving PyTorch ecosystem, where a wealth of tools and libraries await, purpose-built to elevate your experience in deep learning as a developer or researcher. The Ecosystem Tools pages host many projects from experts spanning academia, industry, application development, and machine learning.
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<p class="blog-date">May 11, 2024</p>
<h4>
<a href="/blog/zeus/">Deep Learning Energy Measurement and Optimization</a>
</h4>
<p>Zeus is an open-source toolbox for measuring and optimizing the energy consumption of deep learning workloads. Our goal is to make energy optimization based on accurate measurements as easy as possible for diverse deep learning workloads and setups by offering composable tools with minimal assumptions.
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<p class="blog-date">May 11, 2024</p>
<h4>
<a href="/blog/introducing-depyf/">Introducing depyf: mastering torch.compile with ease</a>
</h4>
<p>We are thrilled to introduce depyf, a new project to the PyTorch ecosystem designed to help users understand, learn, and adapt to torch.compile!
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<p class="blog-date">February 15, 2024</p>
<h4>
<a href="https://medium.com/pytorch/exploring-scientific-machine-learning-pipelines-through-the-simulai-toolkit-9fda42d6c6a0">Exploring scientific machine learning pipelines through the SimulAI toolkit</a>
</h4>
<p>SciML, short for Scientific Machine Learning, encompasses work that merges quantitative sciences with machine learning. It has gained significant traction over the past decade, driven by the widespread availability of specialized hardware (such as GPUs and TPUs) and datasets. Additionally, it has been propelled by the overarching influence of the machine learning wave, now ingrained in the zeitgeist of our times. In this context, we’d like to introduce SimulAI, an open-source toolkit under th...</p>
</div>
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<p class="blog-date">January 29, 2024</p>
<h4>
<a href="https://medium.com/pytorch/colossal-llama-2-low-cost-and-high-quality-domain-specific-llm-solution-using-llama-and-26d2e4b9fd92">Colossal-LLaMA-2: Low Cost and High-quality Domain-specific LLM Solution Using LLaMA and Colossal-AI</a>
</h4>
<p>The most prominent distinction between LLaMA-1 and LLaMA-2 lies in the incorporation of higher-quality corpora, a pivotal factor contributing to significant performance enhancements in LLaMA-2. This, coupled with its commercial availability, extends the potential for creative applications of large models within the open-source community.
</p>
</div>
<a href="https://medium.com/pytorch/colossal-llama-2-low-cost-and-high-quality-domain-specific-llm-solution-using-llama-and-26d2e4b9fd92" class="btn btn-lg with-right-arrow">
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<p class="blog-date">January 25, 2024</p>
<h4>
<a href="https://medium.com/pytorch/3d-rotations-and-spatial-transformations-made-easy-with-roma-356a495a20c4">3D rotations and spatial transformations made easy with RoMa</a>
</h4>
<p>Struggling with quaternions, rotation vectors, right-hand rules and all these stuffs? Try RoMa: an easy-to-to-use, stable and efficient library to deal with rotations and spatial transformations in PyTorch.
</p>
</div>
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<p class="blog-date">January 04, 2024</p>
<h4>
<a href="https://medium.com/pytorch/torchdistill-a-modular-configuration-driven-framework-for-reproducible-deep-learning-and-9e0ecabf2815">torchdistill — a modular, configuration-driven framework for reproducible deep learning and knowledge distillation experiments</a>
</h4>
<p>This article summarizes key features and concepts of torchdistill (v1.0.0). Refer to the official documentation for its APIs and research projects.
</p>
</div>
<a href="https://medium.com/pytorch/torchdistill-a-modular-configuration-driven-framework-for-reproducible-deep-learning-and-9e0ecabf2815" class="btn btn-lg with-right-arrow">
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<p class="blog-date">December 06, 2023</p>
<h4>
<a href="https://medium.com/pytorch/pypose-a-library-for-robot-learning-with-physics-based-optimization-861bc0bb92f1">PyPose: A Library for Robot Learning with Physics-based Optimization</a>
</h4>
<p>We are excited to share our new open-source library PyPose. It is a PyTorch-based robotics-oriented library that provides a set of tools and algorithms for connecting deep learning with physics-based optimization.
</p>
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<p class="blog-date">November 09, 2023</p>
<h4>
<a href="https://medium.com/pytorch/how-activation-checkpointing-enables-scaling-up-training-deep-learning-models-7a93ae01ff2d">How Activation Checkpointing enables scaling up training deep learning models</a>
</h4>
<p>Activation checkpointing is a technique used for reducing the memory footprint at the cost of more compute. It utilizes the simple observation that we can avoid saving intermediate tensors necessary for backward computation if we just recompute them on demand instead.
</p>
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<p class="blog-date">October 26, 2023</p>
<h4>
<a href="https://medium.com/pytorch/torch-compile-explained-ae0def293084">torch.compile, explained</a>
</h4>
<p>Have you ever felt overwhelmed by the complexities of torch.compile? Diving into its workings can feel like black magic, with bytecode and Python internal details that many users fail to understand, hindering them from understanding and debugging torch.compile.
</p>
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<p class="blog-date">July 06, 2023</p>
<h4>
<a href="https://medium.com/pytorch/unveiling-the-power-of-semi-supervised-learning-the-unified-semi-supervised-learning-benchmark-849f42bbc32a">Unveiling the Power of Semi-Supervised Learning: The Unified Semi-Supervised Learning Benchmark</a>
</h4>
<p>Machine Learning models thrive on high-quality, fully-annotated data. The traditional supervised learning approach typically requires data on the scale of millions, or even billions, to train large foundational models. However, obtaining such a vast amount of labeled data is often tedious and labor-intensive. As an alternative, semi-supervised learning (SSL) aims to enhance model generalization with only a fraction of labeled data, complemented by a considerable amount of unlabeled data. This...</p>
</div>
<a href="https://medium.com/pytorch/unveiling-the-power-of-semi-supervised-learning-the-unified-semi-supervised-learning-benchmark-849f42bbc32a" class="btn btn-lg with-right-arrow">
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<p class="blog-date">June 29, 2023</p>
<h4>
<a href="https://medium.com/pytorch/introducing-torchopt-a-high-performance-differentiable-optimization-library-for-pytorch-37c4c0ef6ae1">Introducing TorchOpt: A High-Performance Differentiable Optimization Library for PyTorch</a>
</h4>
<p>Explore TorchOpt, a PyTorch-based library that revolutionizes differentiable optimization with its unified programming abstraction, high-performance distributed execution runtime, and support for various differentiation modes.”
</p>
</div>
<a href="https://medium.com/pytorch/introducing-torchopt-a-high-performance-differentiable-optimization-library-for-pytorch-37c4c0ef6ae1" class="btn btn-lg with-right-arrow">
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<p class="blog-date">April 04, 2023</p>
<h4>
<a href="https://medium.com/pytorch/profiling-pytorch-language-models-with-octoml-profile-eda7ece6b7bd">Profiling PyTorch language models with octoml-profile</a>
</h4>
<p>The recent launch of PyTorch 2.0 makes it clear that the community is heavily investing in a compiler-powered future for machine learning. The new OctoML Profiler can help any user realize the full potential of these shifts in the ML landscape.
</p>
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<p class="blog-date">February 10, 2023</p>
<h4>
<a href="https://medium.com/pytorch/how-fashable-achieves-soa-realistic-ai-generated-images-using-pytorch-and-azure-machine-learning-2313c4cf5f44">How FASHABLE achieves SoA realistic AI generated images using PyTorch and Azure Machine Learning</a>
</h4>
<p>Fashable is a company born at XNFY Lab (a joint initiative with Microsoft). The company’s main goal is to revolutionize the world of fashion with ethical Artificial Intelligence (AI) technologies built on PyTorch framework. Fashable is focused on developing AI models that generates synthetic contents for the global fashion industry. The Fashion industry has been criticized in recent years because it generates a lot of waste and is responsible for up to 10% of global carbon dioxide output. Fas...</p>
</div>
<a href="https://medium.com/pytorch/how-fashable-achieves-soa-realistic-ai-generated-images-using-pytorch-and-azure-machine-learning-2313c4cf5f44" class="btn btn-lg with-right-arrow">
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<p class="blog-date">January 31, 2023</p>
<h4>
<a href="https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02">Latest Colossal-AI boasts novel automatic parallelism and offers savings up to 46x for Stable Diffusion 2</a>
</h4>
<p>As a new PyTorch Ecosystem Partner, we at HPC-AI Tech look forward to working with the PyTorch community to advance AI technologies through our open source project, Colossal-AI. We are excited to join forces with the PyTorch community in this effort.
</p>
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<a href="https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02" class="btn btn-lg with-right-arrow">
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<p class="blog-date">January 06, 2023</p>
<h4>
<a href="https://medium.com/pytorch/distributed-training-with-pytorch-and-azure-ml-898429139098">Distributed training with PyTorch and Azure ML</a>
</h4>
<p>Suppose you have a very large PyTorch model, and you’ve already tried many common tricks to speed up training: you optimized your code, you moved training to the cloud and selected a fast GPU VM, you installed software packages that improve training performance (for example, by using the ACPT curated environment on Azure ML). And yet, you still wish your model could train faster. Maybe it’s time to give distributed training a try! Continue reading to learn the simplest way to do distributed t...</p>
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