Multi-GPU | BestHub

DataFunSummit

Feb 17, 2025 · Artificial Intelligence

NorthStar Large‑Model Training Framework: Architecture, APIs, Pipeline and Multi‑GPU Strategies

The article introduces the NorthStar large‑model training framework developed by DeWu, detailing its background challenges, pipeline architecture, rich API support, multi‑GPU training modes, multi‑level embedding storage, hardware selection considerations, and a brief Q&A on data versus model parallelism.

AI FrameworkEmbedding StorageLarge Model Training

0 likes · 9 min read

NorthStar Large‑Model Training Framework: Architecture, APIs, Pipeline and Multi‑GPU Strategies

NetEase Media Technology Team

Aug 9, 2023 · Artificial Intelligence

GPU Model Inference Optimization Practices in NetEase News Recommendation System

The article outlines practical GPU inference optimization for NetEase’s news recommendation, covering model analysis with Netron, multi‑GPU parallelism, memory‑copy reduction, batch sizing, TensorRT conversion and tuning, custom plugins, and the GRPS serving framework to achieve significant latency and utilization gains.

GPU inferenceMulti-GPUPerformance

0 likes · 44 min read

GPU Model Inference Optimization Practices in NetEase News Recommendation System

HomeTech

Feb 15, 2022 · Artificial Intelligence

Horovod Distributed Deep Learning Training: Architecture, Performance, and Kubernetes Deployment

This article provides a comprehensive overview of Horovod, Uber's open-source distributed deep learning framework, covering its architecture, communication mechanisms, performance benchmarks, and deployment on Kubernetes and Spark for accelerated multi-GPU training.

GPU AccelerationHorovodMulti-GPU

0 likes · 17 min read

Horovod Distributed Deep Learning Training: Architecture, Performance, and Kubernetes Deployment

Python Programming Learning Circle

Aug 23, 2021 · Artificial Intelligence

Efficient PyTorch Training Pipeline: Tips, Profiling, and Multi‑GPU Strategies

This article presents practical strategies for building high‑performance PyTorch training pipelines, covering bottleneck identification, efficient data loading, RAM‑based datasets, profiling tools, multi‑GPU training with DataParallel and DistributedDataParallel, custom loss implementation, and hardware‑vs‑software trade‑offs to accelerate deep‑learning workloads.

Custom LossDataLoaderMulti-GPU

0 likes · 13 min read

Efficient PyTorch Training Pipeline: Tips, Profiling, and Multi‑GPU Strategies

Liulishuo Tech Team

Mar 25, 2017 · Artificial Intelligence

Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning

This article reviews how Liulishuo applied TensorFlow to implement a Deep Knowledge Tracing (DKT) student model for an adaptive learning system, covering the problem background, model architecture, TensorFlow implementation details, multi‑GPU training, and practical deployment considerations.

Deep Knowledge TracingMulti-GPURNN

0 likes · 12 min read

Building a Student Model with TensorFlow: Deep Knowledge Tracing for Adaptive Learning