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The PyTorch Korea User Group will host its inaugural offline conference, PyTorch Day Korea 2026, on November 21, 2026, in Seoul, featuring tracks on building, serving, and running AI models, with a call for proposals.
PyTorch Day Japan 2026 will take place in Tokyo on December 10, with a call for proposals open until September 27, covering topics in open source AI, the PyTorch ecosystem, and related fields.
A PyTorch-enabled, GPU-accelerated clustering pipeline optimizes financial portfolio grouping at scale, delivering hard labels, soft factor loadings, and structural-break signals efficiently for instruments up to a million.
The repository provides prompt encoding and a minimal PyTorch inference implementation for the DeepSeek-V4.1-Flash AI model, including components like vision encoder, MoE, and Hyper-Connections under an MIT License.
The author is experimenting with an adaptive memory governor for PyTorch to prevent CUDA OOM errors on 8GB GPUs, sharing code and seeking community feedback.
The PyTorch Conference North America is scheduled for October 20-21 in San Jose, featuring keynote speaker Jana van Greunen from Meta, who will discuss how Meta's AI workloads advance PyTorch across hardware, research, and production. Registration is open with various pricing tiers and deadlines.
The tweet highlights collaboration among open source communities, including speakers from Linux Foundation, Ant Group, Huawei, and TCC, at events like KubeCon, OpenInfraSummit, and PyTorch Con China 2026, to build an interoperable AI ecosystem.
Trong Vinh Nguyen explains how Viettel created a Token-as-a-Service platform by layering open-source ecosystems from Open Infradev, Cloud Native Foundation, and PyTorch Foundation to unify GPU management for multi-agent workloads.
At PyTorchCon China, the PyTorch TAC Accelerator Integration Working Group shared its achievements and future roadmap, including refined device-agnostic APIs and expanded multi-backend test matrix to address AI computing hardware heterogeneity.
HyperParallel is a PyTorch-based distributed acceleration library optimized for Ascend SuperPoD, demonstrating higher training throughput than standard PyTorch FSDP2 with Muon while maintaining loss convergence, as showcased in a demo at #PyTorchCon China.
NVIDIA releases CUDA Python 1.0, a stable foundation for Python GPU libraries that enables seamless interoperability between PyTorch, CuPy, and other tools, reducing version conflicts.
The Responsible AI Track at PyTorch Conference North America 2026 covers governance, privacy, security, safety, compliance, and ethical AI development within the PyTorch ecosystem.
More than 250 organizations from China have joined the PyTorch Foundation, with Alibaba Cloud, Cambricon, and Ant Group receiving membership tiers, and representatives will give keynotes at the PyTorch Conference China in Shanghai.
Cambricon joins the PyTorch Foundation as a Platinum Member, presenting at PyTorch Conference China 2026 on building device-agnostic PyTorch infrastructure for multi-backend ecosystems and contributing to open-source AI development.
Cambricon, an AI chip pioneer, joins the PyTorch Foundation as a Platinum Member to enhance open-source AI infrastructure through hardware-software co-optimization and upstream contributions.
Red Hat and Hugging Face hosted an evening in Bengaluru focused on the infrastructure and systems shaping the open ML stack, with discussions on profiling, distributed training, and building India's AI infrastructure builders.
The article introduces ParaRNN, an open-source PyTorch/Triton library that speeds up training of non-linear RNNs using parallel Newton-Raphson scans.
PyTorch Conference North America 2026 will take place in San Jose on October 20-21, featuring a poster by PyTorch Korea ambassadors Jiho Kim and Junghwan Park on scaling local developer communities through translation and mentorship, with registration now open.
A final call for early-bird ticket registration for the PyTorch Conference North America, offering $200 savings and event details for attendees before the price increase deadline.
This article, based on a YouTube video, provides a detailed guide on implementing Google DeepMind's Embedding Gemma efficient embedding model from scratch in PyTorch, covering its architecture, training process, and multi-scale representation learning.