BestHub
Sign in
Tagged articles
8 articles
Page 1 of 1
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
May 11, 2026 · Backend Development

Boost Performance: Using DataLoader in Spring Boot for Efficient Batch Processing

This article explains how to integrate the Java‑DataLoader library into a Spring Boot 3.5.0 application, covering dependency setup, entity and repository definitions, service methods, DataLoader configuration, testing, contextual loading, and custom two‑level caching to achieve high‑performance batch data fetching.

Batch LoadingDataLoaderGraphQL
0 likes · 12 min read
Boost Performance: Using DataLoader in Spring Boot for Efficient Batch Processing
Data STUDIO
Data STUDIO
Dec 9, 2025 · Artificial Intelligence

20 Core PyTorch Concepts to Accelerate Your AI Projects

This article walks through twenty essential PyTorch concepts—from basic Tensor creation and manipulation, through autograd and neural‑network construction, to data loading, GPU acceleration, model saving, and practical training tricks—providing concrete code examples and clear explanations for developers eager to build and deploy AI models.

AutogradDataLoaderDeep Learning
0 likes · 16 min read
20 Core PyTorch Concepts to Accelerate Your AI Projects
AI Algorithm Path
AI Algorithm Path
Mar 16, 2025 · Artificial Intelligence

Speed Up Your PyTorch Model Training: Practical Tips and Tricks

This article walks through concrete techniques to accelerate PyTorch training, covering mixed‑precision with torch.cuda.amp, profiling with torch.profiler, DataLoader tuning, torch.compile, distributed strategies like DataParallel and DDP, gradient accumulation, and advanced libraries such as Lightning, Apex, and DeepSpeed, plus model‑level optimizations and monitoring tips.

DataLoaderDistributed TrainingProfiling
0 likes · 12 min read
Speed Up Your PyTorch Model Training: Practical Tips and Tricks
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 29, 2024 · Artificial Intelligence

InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference

The guide walks through fine‑tuning InternLM‑Chat‑7B with XTuner, showing how to build a DataLoader from a HuggingFace Dataset, convert a LoRA .pth checkpoint to HuggingFace format, merge the adapter into the base model, run inference, and adapt the process for custom datasets and 4‑bit quantization experiments.

DataLoaderFineTuningInternLM
0 likes · 27 min read
InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference
HomeTech
HomeTech
Oct 12, 2022 · Backend Development

Practical Experience of Introducing GraphQL as a BFF Layer in Backend Development

This article shares a backend team's practical experience of adopting GraphQL with Express and Apollo Server as a BFF layer, covering motivations, implementation steps, schema merging, proxy configuration, HTTP caching strategies, DataLoader optimization, code generation, and integration testing to improve performance and developer efficiency.

Apollo ServerDataLoaderExpress
0 likes · 14 min read
Practical Experience of Introducing GraphQL as a BFF Layer in Backend Development
Taobao Frontend Technology
Taobao Frontend Technology
Sep 28, 2021 · Backend Development

How DataLoader Solves the GraphQL N+1 Problem: Deep Dive into Batch & Cache Mechanics

This article explains the GraphQL N+1 performance issue, demonstrates how DataLoader batches and caches database calls to eliminate redundant queries, walks through its core TypeScript implementation—including batch scheduling, cache handling, and load methods—while providing practical examples and integration tips for real‑world GraphQL servers.

BatchingDataLoaderGraphQL
0 likes · 28 min read
How DataLoader Solves the GraphQL N+1 Problem: Deep Dive into Batch & Cache Mechanics
Python Programming Learning Circle
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 LossDataLoaderDeep Learning
0 likes · 13 min read
Efficient PyTorch Training Pipeline: Tips, Profiling, and Multi‑GPU Strategies