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JD Cloud Developers
JD Cloud Developers
Jan 30, 2026 · Artificial Intelligence

Scaling Generative Recommendation: Inside JD’s 9N-LLM Multi‑Framework Training Engine

This article details JD Retail’s 9N-LLM unified training engine, which integrates TensorFlow and PyTorch across GPU and NPU hardware to tackle the massive data, model size, and reinforcement‑learning complexities of generative recommendation, offering concrete components, performance benchmarks, and future directions.

GPU/NPUPyTorchTensorFlow
0 likes · 26 min read
Scaling Generative Recommendation: Inside JD’s 9N-LLM Multi‑Framework Training Engine
Python Programming Learning Circle
Python Programming Learning Circle
Nov 18, 2025 · Artificial Intelligence

Top 10 Python Libraries Every Computer Vision Engineer Should Know

This article compiles the most commonly used Python libraries for computer vision, covering basic image handling with Pillow, high‑performance processing with OpenCV and Mahotas, advanced tools like Scikit‑Image, TensorFlow Image, PyTorch Vision, SimpleCV, Imageio, Albumentations, and the model zoo timm, each with concise descriptions and practical code snippets.

Deep LearningPyTorchTensorFlow
0 likes · 11 min read
Top 10 Python Libraries Every Computer Vision Engineer Should Know
IT Services Circle
IT Services Circle
Sep 16, 2025 · Artificial Intelligence

Why TensorFlow Is Dying and What the New AI Open‑Source Landscape Looks Like

An in‑depth analysis reveals TensorFlow’s rapid decline, the rise of PyTorch, and how Ant Group’s OpenRank‑driven “Large Model Open‑Source Ecosystem Panorama 2.0” maps shifting trends, from short‑term hype projects to performance‑focused AI infrastructure, highlighting the emerging US‑China dominance in AI open‑source development.

AI ecosystemAI open-sourceModel Serving
0 likes · 15 min read
Why TensorFlow Is Dying and What the New AI Open‑Source Landscape Looks Like
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 23, 2025 · Artificial Intelligence

Build a Handwritten Digit Recognizer with TensorFlow: Step‑by‑Step MNIST Tutorial

Learn the fundamentals of deep learning by building, training, and evaluating a TensorFlow model that recognizes handwritten digits from the MNIST dataset, covering data preparation, network architecture, activation functions, optimizer choices, model compilation, training loops, evaluation metrics, and visualization of predictions.

Image ClassificationKerasMNIST
0 likes · 20 min read
Build a Handwritten Digit Recognizer with TensorFlow: Step‑by‑Step MNIST Tutorial
php Courses
php Courses
May 15, 2025 · Artificial Intelligence

Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path

This article explains why Python has become the leading language for data analysis and machine learning, outlines the essential libraries and frameworks, provides practical code examples, describes typical application scenarios, suggests a staged learning roadmap, and forecasts future trends such as AutoML and federated learning.

AutoMLPyTorchPython
0 likes · 6 min read
Why Python Dominates Data Analysis and Machine Learning: Core Tools, Full‑Stack Solutions, and Learning Path
Python Programming Learning Circle
Python Programming Learning Circle
Apr 15, 2025 · Artificial Intelligence

Automatic Math Expression Grading with Python, CNN and Image Processing

This tutorial explains how to generate synthetic digit fonts, build a convolutional neural network to recognize handwritten arithmetic expressions, segment images using projection methods, evaluate the results with Python's eval function, and overlay feedback symbols on the original image, providing a complete end‑to‑end solution.

CNNImageProcessingMachineLearning
0 likes · 27 min read
Automatic Math Expression Grading with Python, CNN and Image Processing
AI Code to Success
AI Code to Success
Feb 19, 2025 · Artificial Intelligence

How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide

This article walks through practical Keras tutorials for image‑based traffic‑sign classification and text‑based sentiment analysis, covering data preparation, preprocessing, model construction, training, evaluation, deployment, and a concise comparison of Keras with TensorFlow and PyTorch.

Deep LearningImage ClassificationKeras
0 likes · 19 min read
How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide
Ops Development & AI Practice
Ops Development & AI Practice
Feb 14, 2025 · Artificial Intelligence

Large Model Format Showdown: Hugging Face, TensorFlow, ONNX, TorchScript, GGUF

This comprehensive guide examines the leading large‑model storage formats—including Hugging Face Transformers, TensorFlow SavedModel, ONNX, TorchScript, and GGUF—detailing their file structures, serialization methods, strengths, weaknesses, and typical use‑cases, helping developers and researchers select the optimal format for their specific AI workloads.

AI deploymentGGUFModel Formats
0 likes · 21 min read
Large Model Format Showdown: Hugging Face, TensorFlow, ONNX, TorchScript, GGUF
AI Code to Success
AI Code to Success
Feb 14, 2025 · Artificial Intelligence

TensorFlow vs PyTorch: Which Deep Learning Framework Wins for Your Projects?

An in‑depth comparison of TensorFlow and PyTorch examines their computation graph models, deployment tools, API ergonomics, community ecosystems, and performance characteristics, helping developers decide which framework best fits industrial production or fast‑paced research scenarios.

AI DevelopmentDeep LearningPyTorch
0 likes · 8 min read
TensorFlow vs PyTorch: Which Deep Learning Framework Wins for Your Projects?
AI Code to Success
AI Code to Success
Feb 11, 2025 · Artificial Intelligence

Unlocking TensorFlow: From Basics to Building Your First Linear Regression Model

This article introduces TensorFlow's core concepts—tensors, computational graphs, variables, and sessions—covers its wide range of AI applications from traditional machine learning to deep learning in NLP and computer vision, and provides a step‑by‑step Python tutorial for implementing a simple linear regression model.

AI TutorialDeep LearningNeural Networks
0 likes · 6 min read
Unlocking TensorFlow: From Basics to Building Your First Linear Regression Model
Python Programming Learning Circle
Python Programming Learning Circle
Nov 27, 2024 · Artificial Intelligence

Open‑Source Bird Species Detection with TensorFlow, MobileNet V2 and OpenCV

A hobbyist builds a Python‑based bird‑recognition system using TensorFlow's SSD OpenImages model, a MobileNet V2 classifier from TensorFlow Hub, and OpenCV, shares the open‑source code on GitHub, discusses early results, challenges like accuracy and non‑maximum suppression, and outlines future improvements.

Bird DetectionComputer VisionOpenCV
0 likes · 8 min read
Open‑Source Bird Species Detection with TensorFlow, MobileNet V2 and OpenCV
JD Retail Technology
JD Retail Technology
Aug 30, 2024 · Artificial Intelligence

GPU Optimization Practices for Training and Inference in JD Advertising Recommendation Systems

The article details JD Advertising's technical challenges and solutions for large‑scale sparse recommendation models, describing GPU‑focused storage, compute and I/O optimizations for both training and low‑latency inference, including distributed pipelines, heterogeneous deployment, batch aggregation, multi‑stream execution, and compiler extensions.

Distributed SystemsGPU OptimizationInference
0 likes · 13 min read
GPU Optimization Practices for Training and Inference in JD Advertising Recommendation Systems
DataFunSummit
DataFunSummit
Aug 8, 2024 · Artificial Intelligence

GPU Throughput and Low‑Latency Optimization Practices in JD Advertising

This article presents JD Advertising's technical practices for improving GPU throughput and reducing latency in large‑scale recommendation scenarios, covering system challenges, storage and compute optimizations for training, low‑latency inference techniques, and compiler extensions to handle massive sparse models.

AIAdvertisingLow latency
0 likes · 13 min read
GPU Throughput and Low‑Latency Optimization Practices in JD Advertising
Python Programming Learning Circle
Python Programming Learning Circle
May 11, 2024 · Artificial Intelligence

A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science

This article introduces and demonstrates more than twenty widely used Python libraries for artificial intelligence, computer vision, natural language processing, and data analysis, providing concise explanations and runnable code snippets that illustrate each library's core functionality and typical use cases.

Artificial IntelligenceData ScienceNumPy
0 likes · 29 min read
A Comprehensive Overview of Popular Python Libraries for Artificial Intelligence and Data Science
JD Cloud Developers
JD Cloud Developers
Apr 30, 2024 · Artificial Intelligence

Build a Handwritten Digit Recognizer in Java with TensorFlow

This article walks through the complete process of creating, training, evaluating, saving, and loading a MNIST handwritten digit recognition model using TensorFlow in Java, comparing it with the equivalent Python implementation and covering required knowledge, environment setup, and code details.

Deep LearningMNISTTensorFlow
0 likes · 34 min read
Build a Handwritten Digit Recognizer in Java with TensorFlow
Didi Tech
Didi Tech
Apr 16, 2024 · Artificial Intelligence

Optimizing DSP Deep Model Latency by Externalizing Feature Processing with EzFeaFly

By externalizing feature processing with the EzFeaFly tool and feeding a dense index/value tensor directly to the GPU, the DSP platform decouples feature transformation from model inference, cutting instance usage by ~40%, reducing inference latency 70‑80%, and achieving over 60% end‑to‑end latency improvement while lowering costs.

DSPGPU AccelerationPython
0 likes · 11 min read
Optimizing DSP Deep Model Latency by Externalizing Feature Processing with EzFeaFly
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 15, 2024 · Artificial Intelligence

Optimizing GPU Inference for CTR Models: Kernel Fusion, Multi‑Stream Execution, and Batch Merging

By fusing sparse‑feature operators, enabling multi‑stream execution, consolidating data copies, and merging inference batches, iQIYI reduced GPU CTR model latency to CPU‑level, boosted throughput over sixfold, and cut operational costs by more than 40%, overcoming launch‑overhead bottlenecks.

CTRGPUInference Optimization
0 likes · 10 min read
Optimizing GPU Inference for CTR Models: Kernel Fusion, Multi‑Stream Execution, and Batch Merging
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 1, 2024 · Artificial Intelligence

Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI

iQIYI’s ad ranking system replaces static, hash‑based embeddings with TFRA dynamic embeddings to efficiently handle massive sparse ID features, eliminates collisions and I/O bottlenecks, isolates memory during hot model swaps, enabling billion‑parameter models that boost revenue by 4.3 % while planning adaptive embedding sizes for future improvements.

AI recommendationAdvertisingSparse Embedding
0 likes · 10 min read
Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI
Baidu Geek Talk
Baidu Geek Talk
Feb 5, 2024 · Artificial Intelligence

Why Static Graphs Outperform Dynamic Graphs in AutoDiff: A Deep Dive

This article explains the fundamental differences between static and dynamic computation graphs, compares their memory and performance characteristics, shows how automatic differentiation works in each paradigm, and provides a step‑by‑step implementation of a toy static‑graph AutoDiff engine with Python code examples.

AutoDiffDeep LearningDynamic Graph
0 likes · 18 min read
Why Static Graphs Outperform Dynamic Graphs in AutoDiff: A Deep Dive
php Courses
php Courses
Oct 30, 2023 · Artificial Intelligence

Implementing AI Features in WeChat Mini Programs Using PHP

This article explains how to integrate artificial intelligence into WeChat Mini Programs by deploying a TensorFlow model on a PHP backend, providing step‑by‑step instructions and sample code for creating an API, handling requests with wx.request, and returning predictions to the client.

AIAPIPHP
0 likes · 5 min read
Implementing AI Features in WeChat Mini Programs Using PHP
Ximalaya Technology Team
Ximalaya Technology Team
Oct 9, 2023 · Artificial Intelligence

DeepRec-Based High-Dimensional Sparse Feature Support and Real-Time Model Training in Ximalaya AI Cloud

Ximalaya AI Cloud leverages DeepRec’s Embedding Variable to elastically manage high‑dimensional sparse features with low collision, supporting admission/eviction, multi‑level storage and minute‑level incremental model updates, which together boost GPU utilization, halve training time and improve recommendation CTR by 2‑3 % while maintaining latency.

AI cloudDeepRecKubernetes
0 likes · 13 min read
DeepRec-Based High-Dimensional Sparse Feature Support and Real-Time Model Training in Ximalaya AI Cloud
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 22, 2023 · Artificial Intelligence

Building an Image Classification Model with Transformers and TensorFlow: Theory, Code, and Practice

This article explains how to leverage computer‑vision techniques and deep‑learning frameworks such as Transformers and TensorFlow to build a complete image‑classification pipeline, covering the underlying RGB and CNN principles, model architecture, data preparation, training, and inference with runnable Python code.

CNNImage ClassificationPython
0 likes · 15 min read
Building an Image Classification Model with Transformers and TensorFlow: Theory, Code, and Practice
WeiLi Technology Team
WeiLi Technology Team
May 8, 2023 · Artificial Intelligence

How to Run GPT‑2 Locally: Complete Setup and Code Adjustments

This guide explains the GPT‑2 background, required software, environment configuration, code modifications for TensorFlow 2.x, data download, execution commands, and sample test results, providing a full step‑by‑step process for local deployment of the model.

AIGPT-2TensorFlow
0 likes · 7 min read
How to Run GPT‑2 Locally: Complete Setup and Code Adjustments
DataFunTalk
DataFunTalk
Mar 15, 2023 · Artificial Intelligence

Predicting Sunspot Activity with CnosDB and a TensorFlow 1DConv‑LSTM Model

This article demonstrates how to store monthly sunspot numbers in the CnosDB time‑series database and use TensorFlow to build a 1DConv‑LSTM neural network for forecasting sunspot activity, covering data import, database insertion, train‑test splitting, model definition, training, and result visualization.

1DConv LSTMCnosDBPython
0 likes · 11 min read
Predicting Sunspot Activity with CnosDB and a TensorFlow 1DConv‑LSTM Model
Alimama Tech
Alimama Tech
Mar 14, 2023 · Artificial Intelligence

Neural Approximate Nearest Neighbor (NANN): Open‑Source Large‑Scale Retrieval with Arbitrary Complex Models

Alibaba’s open‑source Neural Approximate Nearest Neighbor (NANN) library decouples index learning from model training, enabling any TensorFlow‑based deep model to perform high‑throughput, high‑accuracy HNSW‑based retrieval with GPU multi‑streaming, XLA acceleration, graph optimizations, and adversarial training that mitigates L2‑distance mismatch, all supported by ready‑to‑use benchmarks and demos.

Neural ANNTensorFlowadversarial training
0 likes · 7 min read
Neural Approximate Nearest Neighbor (NANN): Open‑Source Large‑Scale Retrieval with Arbitrary Complex Models
Programmer DD
Programmer DD
Feb 24, 2023 · Artificial Intelligence

How Jeff Dean’s Journey Shaped Google’s AI and Big Data Revolution

Jeff Dean, a Google engineering legend, has mastered over 18 programming languages and pioneered transformative technologies such as MapReduce, Bigtable, Spanner, and TensorFlow, illustrating how his relentless pursuit of scalability and performance has driven the evolution of AI, big data, and modern cloud infrastructure.

AIJeff DeanMapReduce
0 likes · 14 min read
How Jeff Dean’s Journey Shaped Google’s AI and Big Data Revolution
DataFunSummit
DataFunSummit
Nov 19, 2022 · Operations

Large-Scale Supply Chain Inventory Optimization Using Recurrent Neural Networks

This article presents a novel approach that leverages recurrent neural network techniques and TensorFlow to dramatically accelerate simulation and optimization of massive supply‑chain networks, enabling efficient inventory positioning and safety‑stock decisions for networks with hundreds of thousands of items.

Recurrent Neural NetworkSupply ChainTensorFlow
0 likes · 13 min read
Large-Scale Supply Chain Inventory Optimization Using Recurrent Neural Networks
DataFunTalk
DataFunTalk
Oct 31, 2022 · Artificial Intelligence

NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance

This article introduces NVIDIA Merlin's HugeCTR recommendation system framework, covering its three main modules—NV Tabular, HugeCTR, and Triton—detailing model‑parallel embedding handling, CUDA kernel fusion, mixed‑precision training, hierarchical parameter server inference, Sparse Operation Kit for TensorFlow, performance benchmarks, and practical deployment considerations.

EmbeddingGPU AccelerationHugeCTR
0 likes · 19 min read
NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance
Python Programming Learning Circle
Python Programming Learning Circle
Oct 20, 2022 · Artificial Intelligence

Overview of Common Python AI Libraries with Code Examples

This article provides a concise introduction to a wide range of popular Python libraries for artificial intelligence and data science, such as NumPy, OpenCV, scikit-image, Pillow, Scikit-learn, TensorFlow, PyTorch, and many others, accompanied by practical code snippets and performance comparisons.

Artificial IntelligenceNumPyOpenCV
0 likes · 33 min read
Overview of Common Python AI Libraries with Code Examples
vivo Internet Technology
vivo Internet Technology
Oct 9, 2022 · Artificial Intelligence

vivo Machine Learning Platform: Architecture Design and Practice

vivo’s machine‑learning platform, built for its massive app‑store and e‑commerce ecosystem, streamlines data processing, model training, and deployment through quota‑based resource management, a custom ultra‑large‑scale TensorFlow‑vlps framework, OpenAPI‑driven training, and Jupyter‑integrated interactive development, boosting efficiency for billions of samples and features.

Distributed TrainingMLOpsMachine Learning Platform
0 likes · 12 min read
vivo Machine Learning Platform: Architecture Design and Practice
DaTaobao Tech
DaTaobao Tech
Sep 7, 2022 · Artificial Intelligence

Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation

The team enhanced real‑time recommendation by redesigning TensorFlow graphs—using constant‑folding, a custom CallGraphOP cache, a simplified dense layer, and CUDA‑Graph compatibility—boosting single‑machine throughput ~40%, raising GPU utilization from 30% to 43%, cutting latency and saving roughly 30% of hardware resources.

CUDA GraphGPU performanceModel Optimization
0 likes · 11 min read
Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation
DaTaobao Tech
DaTaobao Tech
Aug 9, 2022 · Artificial Intelligence

Differentiable Programming: Theory, Function Fitting, and Practical Implementations

Differentiable programming augments traditional code with automatic differentiation, enabling gradient‑descent optimization of scientific and UI functions; the article surveys its theory, demonstrates fitting a damping curve via logistic and polynomial models in Julia, Swift, and TensorFlow, and discusses trade‑offs between analytical interpretability and neural‑network flexibility.

Differentiable ProgrammingJavaScriptTensorFlow
0 likes · 30 min read
Differentiable Programming: Theory, Function Fitting, and Practical Implementations
DaTaobao Tech
DaTaobao Tech
Jul 15, 2022 · Artificial Intelligence

Edge AI Model Evaluation and Optimization with TensorFlow, JAX, and TVM

The article demonstrates how to evaluate, compress, and convert deep‑learning models for edge devices using TensorFlow, JAX, and TVM—showing a faster iPhone‑based MNIST training benchmark, FLOPs measurement scripts, TFLite/ONNX/CoreML conversion, TVM compilation with auto‑tuning, and up to 50 % speed improvements on mobile NPU hardware.

JAXTVMTensorFlow
0 likes · 29 min read
Edge AI Model Evaluation and Optimization with TensorFlow, JAX, and TVM
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jul 6, 2022 · Industry Insights

Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph

This article details NetEase Cloud Music’s four‑layer machine‑learning platform architecture, covering resource provisioning with Visual Kubelet and Alibaba Cloud ECI, Ceph storage optimizations, TensorFlow migration, large‑scale graph neural network support, and end‑to‑end workflow tooling that together enable efficient, cost‑effective AI development and deployment.

CephGPUGraph Neural Network
0 likes · 24 min read
Inside NetEase Cloud Music’s MLOps: Scaling AI with VK, ECI, and Ceph
Alibaba Terminal Technology
Alibaba Terminal Technology
Jun 22, 2022 · Artificial Intelligence

How Fast Can Your Smartphone Run ML Models? Exploring Edge AI Optimization

This article examines the computational capabilities of modern mobile devices for machine learning, compares training times on a MacBook and iPhone, explains model evaluation metrics like FLOPs, and provides step‑by‑step guides for converting and optimizing models using TensorFlow, PyTorch, ONNX, JAX, and TVM for edge deployment.

JAXModel OptimizationTVM
0 likes · 29 min read
How Fast Can Your Smartphone Run ML Models? Exploring Edge AI Optimization
Code DAO
Code DAO
May 27, 2022 · Artificial Intelligence

Building an Image Classification Model with CNNs

This article explains how to train a convolutional neural network on a remote GPU for image classification, covering convolution, padding, activation, pooling, dropout, flattening, fully‑connected layers, dataset preparation, model definition, training, and prediction using TensorFlow/Keras.

CNNFood-101GPU training
0 likes · 13 min read
Building an Image Classification Model with CNNs
DataFunSummit
DataFunSummit
May 10, 2022 · Artificial Intelligence

A Practical Survey of Common CTR Prediction Models

This article reviews several widely used click‑through‑rate (CTR) prediction models—including Logistic Regression, XGBoost, Factorization Machines, Wide & Deep, DeepFM, DCN, xDeepFM, and AFM—providing their principles, advantages, disadvantages, and links to TensorFlow implementations for quick reuse and deeper understanding.

CTRModel SurveyTensorFlow
0 likes · 12 min read
A Practical Survey of Common CTR Prediction Models
DataFunTalk
DataFunTalk
May 6, 2022 · Artificial Intelligence

Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation

This article introduces the ESMM (Entire Space Multi‑Task Model) proposed by Alibaba, explaining how it tackles sample selection bias and data sparsity in post‑click conversion rate (CVR) prediction through shared embeddings and implicit pCVR learning, and provides a detailed implementation using the EasyRec framework with code examples.

CVR PredictionESMMTensorFlow
0 likes · 11 min read
Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation
360 Tech Engineering
360 Tech Engineering
Apr 25, 2022 · Artificial Intelligence

Non-Reference Audio Quality Assessment Using a Bidirectional LSTM Deep Learning Model

This article presents a non‑reference audio quality assessment method that leverages a bidirectional LSTM network to predict perceptual scores from spectral features extracted via FFT, describing the system workflow, technical advantages, data preparation, loss design, and TensorFlow implementation details.

BiLSTMSignal ProcessingTensorFlow
0 likes · 7 min read
Non-Reference Audio Quality Assessment Using a Bidirectional LSTM Deep Learning Model
360 Quality & Efficiency
360 Quality & Efficiency
Apr 22, 2022 · Artificial Intelligence

Audio Quality Assessment Using a BiLSTM Deep Learning Model

This article presents a no‑reference audio quality assessment system that leverages a bidirectional LSTM network to extract spectral features via FFT and predict perceptual scores, describing the architecture, technical advantages, data preparation, loss design, and TensorFlow implementation.

BiLSTMDeep LearningSignal Processing
0 likes · 8 min read
Audio Quality Assessment Using a BiLSTM Deep Learning Model
DataFunTalk
DataFunTalk
Mar 31, 2022 · Artificial Intelligence

Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations

This article provides an in‑depth overview of the TensorFlow ecosystem, covering Keras modeling productivity tools, classic model examples, AutoKeras and KerasTuner for automated search, data preprocessing pipelines, performance profiling, model optimization, and multiple deployment strategies for servers, browsers, and edge devices.

AutoMLKerasModel Deployment
0 likes · 20 min read
Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations
Meituan Technology Team
Meituan Technology Team
Mar 24, 2022 · Artificial Intelligence

Booster GPU Training Architecture for Recommendation Systems at Meituan: Design, Optimization, and Deployment

Meituan’s Booster architecture co‑designs algorithm and system to run TensorFlow recommendation training on multi‑GPU A100 servers, optimizing data fetching, embedding pipelines, custom kernels and communication fusion, delivering 2–4× cost‑performance over CPU, over threefold GPU throughput, and seamless deployment via a single‑line API.

Booster architectureGPU trainingTensorFlow
0 likes · 36 min read
Booster GPU Training Architecture for Recommendation Systems at Meituan: Design, Optimization, and Deployment
Meituan Technology Team
Meituan Technology Team
Mar 3, 2022 · Artificial Intelligence

GPU Optimization Practices for Meituan Delivery Search and Recommendation Model Inference

Meituan’s delivery search and recommendation service migrated from separate CPU‑only models to a unified multi‑task model running on a heterogeneous CPU‑GPU architecture, applying system‑level placement, All‑On‑GPU lookup, FP16 mixed precision, operator fusion, TensorRT and TVM compilation, which together delivered roughly a four‑fold increase in inference throughput while maintaining cost.

GPUTVMTensorFlow
0 likes · 24 min read
GPU Optimization Practices for Meituan Delivery Search and Recommendation Model Inference
MaGe Linux Operations
MaGe Linux Operations
Jan 30, 2022 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?

This article compares PyTorch and TensorFlow in 2022 across model availability, deployment ease, and ecosystem support, using data from HuggingFace, research papers, and industry tools, and offers tailored recommendations for industry engineers, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningModel Deployment
0 likes · 20 min read
PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?
Python Programming Learning Circle
Python Programming Learning Circle
Dec 27, 2021 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework to Choose?

An in‑depth 2022 comparison of PyTorch and TensorFlow evaluates model availability, deployment ease, and ecosystem support, showing PyTorch dominates research while TensorFlow excels in deployment, and offers tailored recommendations for industry professionals, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningPyTorch
0 likes · 20 min read
PyTorch vs TensorFlow in 2022: Which Framework to Choose?
DataFunTalk
DataFunTalk
Dec 23, 2021 · Artificial Intelligence

Deep Customization and Optimization of TensorFlow for Large-Scale Sparse Training at Meituan

This article details Meituan's internal, heavily customized TensorFlow 1.x implementation that addresses large‑scale sparse parameter support, distributed training challenges, communication bottlenecks, and pipeline optimizations, achieving over ten‑fold scalability improvements and significant per‑node performance gains in recommendation system workloads.

Distributed TrainingSparse ParametersTensorFlow
0 likes · 32 min read
Deep Customization and Optimization of TensorFlow for Large-Scale Sparse Training at Meituan
Code DAO
Code DAO
Dec 20, 2021 · Artificial Intelligence

Exploring Latent Space with a Variational Autoencoder in TensorFlow

This article explains the theory behind variational autoencoders, details their KL‑divergence loss, provides a complete TensorFlow implementation, and demonstrates reconstruction, latent‑space visualization, and novel image generation through sampling and interpolation.

Image GenerationKL divergenceLatent Space
0 likes · 13 min read
Exploring Latent Space with a Variational Autoencoder in TensorFlow
Code DAO
Code DAO
Dec 20, 2021 · Artificial Intelligence

Building Efficient Data Pipelines with TensorFlow’s tf.data API

This article explains how to use TensorFlow’s tf.data API to construct high‑performance, flexible data pipelines—from loading images or tensors, applying transformations and data augmentation, to batching, shuffling, caching, prefetching, and feeding the pipeline directly into model.fit for training.

PythonTensorFlowdata loading
0 likes · 9 min read
Building Efficient Data Pipelines with TensorFlow’s tf.data API
Code DAO
Code DAO
Dec 19, 2021 · Artificial Intelligence

Exploring Latent Space with TensorFlow Autoencoders (Part 1)

This tutorial walks through building a TensorFlow 2.0 autoencoder from scratch, preparing the FashionDB dataset, visualizing raw images, projecting them into PCA and t‑SNE spaces, constructing encoder and decoder layers, training the model, and visualizing the resulting latent space to reveal image clusters.

AutoencoderLatent SpacePCA
0 likes · 13 min read
Exploring Latent Space with TensorFlow Autoencoders (Part 1)
Meituan Technology Team
Meituan Technology Team
Dec 9, 2021 · Artificial Intelligence

Deep Customization of TensorFlow for Large-Scale Sparse Training at Meituan

Meituan heavily customized TensorFlow 1.x for large‑scale sparse training, replacing variable embeddings with hash tables, improving load balancing, using RDMA communication, pipeline‑embedding graphs, high‑performance hash tables, and operator merges, achieving over ten‑fold scalability, up to 51% operator speedups, and enabling billions‑parameter models on CPU clusters with future GPU expansion.

Distributed TrainingSparse ParametersTensorFlow
0 likes · 31 min read
Deep Customization of TensorFlow for Large-Scale Sparse Training at Meituan
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 16, 2021 · Mobile Development

Highlights from the Google Developer Summit: Android 12, Flutter, TensorFlow, ARCore, and Web Updates

The online Google Developer Summit in China covered Android 12's UI, performance, and privacy improvements, Flutter's cross‑platform dominance, TensorFlow use cases, ARCore enhancements, WebAssembly and PWA advances, and Play's new Data Safety requirements, providing a concise technical recap for developers.

AndroidGoogle Developer SummitMobile Development
0 likes · 6 min read
Highlights from the Google Developer Summit: Android 12, Flutter, TensorFlow, ARCore, and Web Updates
DataFunTalk
DataFunTalk
Nov 6, 2021 · Artificial Intelligence

Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation

The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.

AdvertisingDistributed SystemsFederated Learning
0 likes · 18 min read
Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation
Youzan Coder
Youzan Coder
Nov 5, 2021 · Artificial Intelligence

AI-Powered Image Recognition for Fresh Produce Retail: System Design and Implementation

An AI‑driven image‑recognition system using TensorFlow Lite cameras on checkout scales replaces barcode PLU lookup with hierarchical product categories, caches offline selections for incremental model updates, and delivers instant, offline‑capable identification, dramatically speeding fresh produce checkout, cutting labor costs, and offering a reusable framework for other retail sectors.

AIRetailTensorFlow
0 likes · 8 min read
AI-Powered Image Recognition for Fresh Produce Retail: System Design and Implementation
Alimama Tech
Alimama Tech
Nov 3, 2021 · Artificial Intelligence

Curvature Learning Framework (CurvLearn): A TensorFlow‑Based Library for Non‑Euclidean Deep Learning

CurvLearn is a TensorFlow-based open-source library enabling deep learning on curved manifolds (hyperbolic, spherical, mixed) with manifold implementations, Riemannian operations, optimizers, and distributed training, and it has been applied to recommendation, graph, and NLP tasks while providing custom ANN tools and practical training tips.

Curvature LearningDeep LearningManifold Optimization
0 likes · 13 min read
Curvature Learning Framework (CurvLearn): A TensorFlow‑Based Library for Non‑Euclidean Deep Learning
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 24, 2021 · Artificial Intelligence

How to Build a 3D CNN for CT Scan Classification with TensorFlow

This tutorial walks through constructing, training, and evaluating a 3D convolutional neural network in TensorFlow to classify CT scans for viral pneumonia, covering data preprocessing, dynamic learning rates, early stopping, and single‑scan prediction with full code examples.

3D CNNCT scan classificationDeep Learning
0 likes · 15 min read
How to Build a 3D CNN for CT Scan Classification with TensorFlow
Meituan Technology Team
Meituan Technology Team
Sep 9, 2021 · Artificial Intelligence

GPU Optimization Practices for CTR Models at Meituan

Meituan accelerates CTR model inference by fusing operators with TVM, optimizing CPU‑GPU data transfers, manually tuning high‑frequency subgraphs, and dynamically offloading workloads, achieving up to ten‑fold throughput gains on Tesla T4 GPUs while keeping latency stable and only modestly increasing beyond 128 QPS, though compilation remains slow and large‑model support needs improvement.

CTRDeep LearningGPU
0 likes · 16 min read
GPU Optimization Practices for CTR Models at Meituan
Python Programming Learning Circle
Python Programming Learning Circle
Aug 24, 2021 · Artificial Intelligence

Top 10 Python Libraries for Machine Learning

An overview of ten widely used Python machine‑learning libraries—including TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their core features, typical applications, and why they are essential tools for data scientists and AI developers.

KerasNumPyPyTorch
0 likes · 15 min read
Top 10 Python Libraries for Machine Learning
Tencent Architect
Tencent Architect
Aug 4, 2021 · Artificial Intelligence

How We Accelerated Feature Hashing for Ad Ranking on GPUs

This article explains how Tencent's Light platform reduced the massive overhead of feature hashing in ad‑ranking by moving integer‑to‑string conversion and hash computation to the GPU, introducing custom contiguous string tensors, and achieving up to 12× speed‑up on V100 GPUs.

GPU OptimizationTensorFlowad ranking
0 likes · 14 min read
How We Accelerated Feature Hashing for Ad Ranking on GPUs
Tencent Architect
Tencent Architect
Jul 29, 2021 · Artificial Intelligence

Performance Optimization of Advertising Coarse‑Ranking Training on the Light Framework

This article analyzes the bottlenecks of advertising coarse‑ranking training on the Light framework and presents a series of optimizations—including parallel data download, thread‑queue buffering, integer‑to‑string conversion with fmt, and zlib replacement with czlib—that together achieve up to 58% QPS improvement and notable CPU efficiency gains.

AdvertisingCPU/GPU efficiencyData Parallelism
0 likes · 11 min read
Performance Optimization of Advertising Coarse‑Ranking Training on the Light Framework
MaGe Linux Operations
MaGe Linux Operations
Jul 8, 2021 · Artificial Intelligence

TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?

An in‑depth comparison of TensorFlow 2.x and PyTorch 1.8 highlights new features, deployment options like TensorFlow Lite and PyTorch Mobile, coding style differences, and practical guidance on choosing the right deep‑learning library for various projects and skill levels.

Deep LearningPyTorchTensorFlow
0 likes · 6 min read
TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?
WeChat Backend Team
WeChat Backend Team
Jun 7, 2021 · Artificial Intelligence

How WeChat’s TFCC Boosts Deep Learning Inference Performance Across Platforms

The TFCC framework, developed by WeChat's backend team, delivers high‑performance, easy‑to‑use, and universal deep‑learning inference by supporting numerous ONNX and TensorFlow operations, optimizing model structures, constants, and operators, and providing a versatile runtime and math library for both CPU and GPU platforms.

Deep LearningFrameworkInference
0 likes · 8 min read
How WeChat’s TFCC Boosts Deep Learning Inference Performance Across Platforms
Python Programming Learning Circle
Python Programming Learning Circle
May 29, 2021 · Artificial Intelligence

Comparing PyTorch 1.8 and TensorFlow 2.5: New Features, Use Cases, and Choosing the Right Framework

This article reviews the latest releases of PyTorch 1.8 and TensorFlow 2.5, outlining their new functionalities, ecosystem tools such as TensorFlow.js, Lite, and TFX, as well as PyTorch Mobile and Lightning, and provides guidance on selecting the most suitable framework for different deep‑learning projects.

Artificial IntelligenceDeep LearningPyTorch
0 likes · 7 min read
Comparing PyTorch 1.8 and TensorFlow 2.5: New Features, Use Cases, and Choosing the Right Framework
Tencent Advertising Technology
Tencent Advertising Technology
May 28, 2021 · Artificial Intelligence

Insights from the Tencent Advertising Algorithm Competition: Model Framework and Optimization Strategies

The article shares a Tencent competition champion’s practical TensorFlow‑based video ad solution, detailing data handling, model architecture, optimization tricks, multimodal fusion techniques, and experimental observations to help participants improve performance in the 2021 Tencent Advertising Algorithm Contest.

MultimodalTensorFlowadvertising algorithm
0 likes · 7 min read
Insights from the Tencent Advertising Algorithm Competition: Model Framework and Optimization Strategies
iQIYI Technical Product Team
iQIYI Technical Product Team
May 14, 2021 · Artificial Intelligence

Performance Optimization of TensorFlow Feature Columns in Recommendation Systems

The article details how iQIYI doubled online inference speed and cut p99 latency by over 50% in TensorFlow‑based CTR recommendation models by replacing costly string‑based integer hashing, removing redundant dense‑sparse conversions, and deduplicating user features for efficient broadcasting, demonstrating that modest Feature Column tweaks can yield major production gains.

Feature ColumnsTensorFlowmachine learning
0 likes · 11 min read
Performance Optimization of TensorFlow Feature Columns in Recommendation Systems
Sohu Tech Products
Sohu Tech Products
May 12, 2021 · Artificial Intelligence

Zero‑Basis Food Sound Recognition with ASR: Theory, Workflow, and Complete Python Code

This article introduces the fundamentals of automatic speech recognition (ASR) for food‑sound classification, explains key audio representations and modeling approaches, and provides a fully runnable Python implementation using librosa, TensorFlow/Keras, and classic machine‑learning tools to train and predict on the Tianchi competition dataset.

ASRAudio ClassificationCNN
0 likes · 11 min read
Zero‑Basis Food Sound Recognition with ASR: Theory, Workflow, and Complete Python Code
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 15, 2021 · Artificial Intelligence

Recommendation System Architecture and Engineering Overview

This article presents a comprehensive overview of a recommendation system, covering its business background, purpose, detailed engineering architecture—including data sources, computation, storage, online learning, service and access layers—and discusses key challenges, module design, and practical reflections.

AB testingTensorFlowdata engineering
0 likes · 14 min read
Recommendation System Architecture and Engineering Overview
Tencent Cloud Developer
Tencent Cloud Developer
Dec 14, 2020 · Artificial Intelligence

Game AI SDK: Overview, Architecture, and Usage

Tencent’s open‑source Game AI SDK provides a versatile automation testing platform—supporting a wide range of game genres and mobile/PC apps—by integrating environment simulation, configurable tools, image‑recognition modules, and deep‑learning algorithms (DQN and IM) into a unified, user‑friendly workflow for training and executing AI agents.

Automation testingDeep LearningSDK
0 likes · 17 min read
Game AI SDK: Overview, Architecture, and Usage
58 Tech
58 Tech
Oct 28, 2020 · Artificial Intelligence

Optimizing Resource Utilization of 58.com Deep Learning Platform: Practices and Techniques

This article details how 58.com’s end‑to‑end deep‑learning platform was optimized for higher CPU and GPU inference performance using Intel MKL, OpenVINO, mixed TensorFlow deployment, GPU virtualization, and a Prometheus‑Grafana monitoring system, achieving a 37% reduction in GPU usage and a 146% increase in average GPU utilization.

GPU virtualizationIntel MKLKubernetes
0 likes · 12 min read
Optimizing Resource Utilization of 58.com Deep Learning Platform: Practices and Techniques
DataFunTalk
DataFunTalk
Oct 23, 2020 · Artificial Intelligence

Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie

This article presents DeepMatch, a behavior‑sequence based deep learning recall model enhanced with play‑rate and intent‑type embeddings, describes its self‑attention architecture, factorized embedding parameterization, multitask loss design, distributed TensorFlow training tricks, and demonstrates significant offline and online improvements in music recommendation performance.

Deep LearningSelf-AttentionTensorFlow
0 likes · 15 min read
Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 16, 2020 · Artificial Intelligence

How TensorNet Supercharges Sparse Feature Training on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework optimized for massive sparse‑feature models in advertising and recommendation, dramatically reducing parameter sync overhead, enabling near‑infinite feature dimensions, cutting training time from hours to minutes, and boosting inference performance by up to 35%.

AITensorFlowTensorNet
0 likes · 10 min read
How TensorNet Supercharges Sparse Feature Training on TensorFlow
360 Tech Engineering
360 Tech Engineering
Sep 14, 2020 · Artificial Intelligence

TensorNet: A Distributed Training Framework Optimized for Large-Scale Sparse Feature Models on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework that tackles the challenges of massive data and billions of sparse parameters in advertising and recommendation systems by enabling near‑infinite sparse feature dimensions, drastically reducing synchronization overhead, and delivering up to 35% inference speed improvements.

AI InfrastructureDistributed TrainingTensorFlow
0 likes · 8 min read
TensorNet: A Distributed Training Framework Optimized for Large-Scale Sparse Feature Models on TensorFlow
360 Quality & Efficiency
360 Quality & Efficiency
Aug 7, 2020 · Artificial Intelligence

Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks

This article analyses the drawbacks of using fully‑connected layers in convolutional neural networks for image tasks, proposes fully‑convolutional alternatives with 1×1 convolutions and strategic max‑pooling, provides TensorFlow code examples, compares model sizes and performance, and discusses deployment considerations for variable‑size inputs.

CNNFully Convolutional NetworkImage Classification
0 likes · 7 min read
Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks
Didi Tech
Didi Tech
Jul 24, 2020 · Artificial Intelligence

DLFlow: An End-to-End Deep Learning Solution for Big Data Offline Tasks

DLFlow, an end‑to‑end framework from Didi’s user‑profile team, merges Spark and TensorFlow to automate feature preprocessing, large‑scale distributed training, and massive prediction for big‑data offline tasks, offering configuration‑driven pipelines, task scheduling, and easy deployment that dramatically speeds model development.

Deep LearningModel DevelopmentSpark
0 likes · 9 min read
DLFlow: An End-to-End Deep Learning Solution for Big Data Offline Tasks
AntTech
AntTech
Jul 13, 2020 · Artificial Intelligence

ElasticDL: An Open‑Source Distributed Deep Learning Framework with Elastic Scheduling

ElasticDL is an open‑source distributed deep learning framework built on TensorFlow 2.x and Kubernetes that simplifies programming by letting users define models with the Keras API, while providing elastic scheduling, fault tolerance, and significant performance gains demonstrated through extensive benchmarks.

Distributed Deep LearningElasticDLKubernetes
0 likes · 19 min read
ElasticDL: An Open‑Source Distributed Deep Learning Framework with Elastic Scheduling
Alibaba Terminal Technology
Alibaba Terminal Technology
Jul 2, 2020 · Artificial Intelligence

Build an Image Style‑Transfer Service with Pipcook and CycleGAN

This tutorial walks you through using Pipcook to create an image style‑transfer pipeline with CycleGAN, covering dataset preparation, pipeline configuration, model training on GPU, and inference code that converts apples to oranges and vice versa, all illustrated with example images.

CycleGANNode.jsPipcook
0 likes · 11 min read
Build an Image Style‑Transfer Service with Pipcook and CycleGAN
Python Programming Learning Circle
Python Programming Learning Circle
Jun 11, 2020 · Artificial Intelligence

Step-by-Step Guide to Building a Movie Recommendation System with TensorFlow

This tutorial walks through collecting and cleaning the MovieLens dataset, constructing rating and record matrices, normalizing ratings, defining a collaborative‑filtering model in TensorFlow, training it with Adam optimizer, evaluating performance, and finally generating personalized movie recommendations for a chosen user.

TensorFlowcollaborative filteringdata preprocessing
0 likes · 10 min read
Step-by-Step Guide to Building a Movie Recommendation System with TensorFlow
Cloud Native Technology Community
Cloud Native Technology Community
Jun 5, 2020 · Artificial Intelligence

Automating a Data‑Science Workflow on Kubernetes: From GitHub Issue Mining to an MLP Bug Classifier

This article describes how to collect, clean, and analyze 90,000+ GitHub issues and pull requests from the Kubernetes repository using Kubeflow, TensorFlow, and a fully automated CI/CD pipeline, then build, train, and serve a simple MLP model that classifies release‑note texts as bugs or non‑bugs.

KubeflowKubernetesTensorFlow
0 likes · 19 min read
Automating a Data‑Science Workflow on Kubernetes: From GitHub Issue Mining to an MLP Bug Classifier
Architect
Architect
May 29, 2020 · Artificial Intelligence

Integrating Flink with TensorFlow for End-to-End Machine Learning Pipelines

This article explains how to combine the Flink data‑processing engine with TensorFlow to create a unified, end‑to‑end machine‑learning workflow, covering background, challenges, the Flink‑AI‑extended architecture, ML framework and operator abstractions, and both batch and streaming training and prediction modes.

AI integrationDistributed TrainingFlink
0 likes · 9 min read
Integrating Flink with TensorFlow for End-to-End Machine Learning Pipelines
Tencent Advertising Technology
Tencent Advertising Technology
May 5, 2020 · Artificial Intelligence

How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through the complete process of using the TI-ONE SDK—including data preparation, dependency installation, session initialization, TensorFlow estimator configuration, job submission, and result monitoring—to train a machine‑learning model for the 2020 Tencent Advertising Algorithm Competition.

SDKTI-ONETencent
0 likes · 7 min read
How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition