How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training

RecIS is an open‑source, PyTorch‑based unified framework designed for ultra‑large‑scale sparse‑dense computation in recommendation systems, offering a full solution for training models with massive samples, multimodal inputs, and large embeddings, and demonstrating significant performance gains over TensorFlow and TorchRec in production deployments.

PyTorchRecommendation Systemsdeep learning framework

0 likes · 24 min read

How RecIS Revolutionizes Large‑Scale Sparse‑Dense Recommendation Training

Baidu Geek Talk

Apr 14, 2025 · Artificial Intelligence

PaddlePaddle Framework 3.0: Five Core Breakthroughs Reshaping Large Model Development

PaddlePaddle Framework 3.0 delivers five breakthroughs—dynamic‑static unified automatic parallelism, integrated training‑inference pipelines, high‑order scientific differentiation, a neural‑network compiler with automatic operator fusion, and streamlined heterogeneous chip adaptation—drastically reducing development effort, boosting training speed, and expanding compatibility for large‑scale AI models.

AI InfrastructureDistributed TrainingModel Inference Optimization

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PaddlePaddle Framework 3.0: Five Core Breakthroughs Reshaping Large Model Development

Baidu App Technology

Nov 25, 2021 · Game Development

Building an AI-Powered Object Hunt Game with Paddle.js and PaddleClas

The article details how to create the AI‑driven “Object Hunt Battle” game by processing data, designing and training a PP‑LCNet model with PaddleClas, converting it for Paddle.js, and integrating real‑time WebGL inference on mobile devices, achieving sub‑50 ms latency and encouraging developers to explore further.

AI game developmentMobile AIPaddle.js

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Building an AI-Powered Object Hunt Game with Paddle.js and PaddleClas