How EasyRec Boosts Recommendation Training and Inference Performance

This article explains the EasyRec recommendation system’s training and inference architecture, detailing optimization techniques such as embedding parallelism, CPU/GPU placement, XLA and TRT fusion, online learning pipelines, network compression, and real‑world deployment results that dramatically improve throughput and latency.

AI InfrastructureEasyRecInference Optimization

0 likes · 15 min read

How EasyRec Boosts Recommendation Training and Inference Performance

DataFunSummit

Nov 22, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec’s recommendation system architecture, detailing training and inference optimizations, embedding parallelism, CPU/GPU placement strategies, online learning pipelines, and network compression techniques that together improve scalability, latency, and cost efficiency.

Distributed SystemsEasyRecInference Optimization

0 likes · 15 min read

EasyRec Recommendation Algorithm Training and Inference Optimization

Sohu Tech Products

Aug 28, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

EasyRec, Alibaba Cloud’s modular recommendation framework, unifies configurable data, embedding, dense, and output layers on MaxCompute, EMR, and DLC, and speeds training with deduplication, EmbeddingParallel sharding, lock‑free hash tables, GPU embeddings, and AMX BF16, while inference benefits from operator fusion, low‑precision AVX/AMX kernels, compact caches, batch merging, and network compression, enabling real‑time online learning and delivering higher recommendation quality at lower cost in e‑commerce.

Alibaba CloudEasyRecInference Optimization

0 likes · 14 min read

DataFunTalk

Apr 28, 2022 · Artificial Intelligence

Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec

This article reviews the evolution of behavior‑sequence modeling methods—from pooling and target‑attention to RNN, capsule, transformer, and graph neural networks—explains their industrial relevance, and demonstrates how to quickly apply these techniques in the EasyRec framework with practical configuration examples.

DINEasyRecSequence Modeling

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Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec