HubLens › Topics › PaddlePaddle
// topic

PaddlePaddle

11trending in last 90 days·11all-time

// new this month

// ecosystem

Deep Learning9LLM3Machine Learning3GPU3Inference2PaddlePaddle
AI 11

// this week's top 5

01
PaddlePaddle / Paddle
PaddlePaddle is a comprehensive industrial deep learning platform that provides core frameworks, model libraries, and end-to-end development tools. It supports advanced features like unified dynamic and static graphs, automatic parallelism, and high-order differentiation for scientific computing. The platform is designed to facilitate large-scale model training and inference across diverse industrial sectors.
8523,870
02
PaddlePaddle / PaddleFormers
PaddleFormers is a Transformers library built on the Baidu PaddlePaddle framework, designed to provide training interfaces and functional experiences for Large Language Models and Vision-Language Models equivalent to Hugging Face. By integrating tensor parallelism, pipeline parallelism, and automatic mixed precision, the project achieves training performance that surpasses Megatron-LM on mainstream models. Furthermore, it fully supports domestic computing chips and is compatible with the Safetensors format, helping developers efficiently complete the entire process from pre-training to post-training.
7812,991
03
PaddlePaddle / FastDeploy
FastDeploy is an inference deployment toolkit for large language models and vision-language models based on PaddlePaddle, designed to provide out-of-the-box production-grade deployment solutions. This tool supports various mainstream hardware platforms and integrates load-balanced PD separation, unified KV cache transmission, and multiple advanced acceleration technologies. Developers can achieve rapid deployment through OpenAI API-compatible interfaces and optimize inference performance using full quantization format support.
713,681
04
PaddlePaddle / PaddleCustomDevice
PaddleCustomDevice is the custom hardware integration solution provided by the PaddlePaddle framework. Through standardized interface design, this project enables developers to integrate various third-party hardware backends into the PaddlePaddle ecosystem. It currently covers support for mainstream hardware platforms including Ascend, Cambricon, Intel GPU, and Apple MPS.
54104
05
PaddlePaddle / docs
This repository contains the source files for the official PaddlePaddle documentation platform. It organizes content into specific directories for API references, user guides, and tutorials to support developers. The project also provides CI scripts and build instructions to facilitate local documentation generation and community contributions.
39282

// all-time featured (11)

PaddlePaddle / Paddle
PaddlePaddle is a comprehensive industrial deep learning platform that provides a complete ecosystem of frameworks, model libraries, and development tools. It supports advanced capabilities such as automatic parallelism, unified training and inference, and high-order differentiation for scientific computing. The platform is designed to facilitate AI commercialization across various sectors by offering a flexible, high-performance architecture for diverse model development.
92
PaddlePaddle / Paddle
PaddlePaddle is a comprehensive industrial deep learning platform that provides core frameworks, model libraries, and end-to-end development tools. It supports advanced features like unified dynamic and static graphs, automatic parallelism, and high-order differentiation for scientific computing. The platform is designed to facilitate large-scale model training and inference across diverse industrial sectors.
85
PaddlePaddle / PaddleFormers
PaddleFormers is a Transformers library built on the Baidu PaddlePaddle framework, designed to provide training interfaces and functional experiences for Large Language Models and Vision-Language Models equivalent to Hugging Face. By integrating tensor parallelism, pipeline parallelism, and automatic mixed precision, the project achieves training performance that surpasses Megatron-LM on mainstream models. Furthermore, it fully supports domestic computing chips and is compatible with the Safetensors format, helping developers efficiently complete the entire process from pre-training to post-training.
78
PaddlePaddle / PaddleX
PaddleX 3.0 is a low-code development tool built on the PaddlePaddle framework, integrating a vast array of out-of-the-box pre-trained models to support full-process development. Through a minimalist Python API and a graphical interface, the tool enables rapid implementation from model training to inference deployment. Furthermore, it is widely compatible with mainstream domestic and international hardware, helping developers efficiently complete industrial practices.
72
PaddlePaddle / FastDeploy
FastDeploy is an inference deployment toolkit for large language models and vision-language models based on PaddlePaddle, designed to provide out-of-the-box production-grade deployment solutions. This tool supports various mainstream hardware platforms and integrates load-balanced PD separation, unified KV cache transmission, and multiple advanced acceleration technologies. Developers can achieve rapid deployment through OpenAI API-compatible interfaces and optimize inference performance using full quantization format support.
71
PaddlePaddle / FastDeploy
FastDeploy is an inference deployment toolkit for large language models and vision-language models based on PaddlePaddle, aiming to provide out-of-the-box production-grade deployment solutions. The toolkit supports various mainstream hardware platforms and integrates core technologies such as load-balanced PD separation, unified KV cache transmission, and full quantization format support. By being compatible with OpenAI API and vLLM interfaces, it helps developers efficiently implement model inference and online service deployment.
68
PaddlePaddle / PaddleCustomDevice
PaddleCustomDevice is the custom hardware integration solution provided by the PaddlePaddle framework. Through standardized interface design, this project enables developers to integrate various third-party hardware backends into the PaddlePaddle ecosystem. It currently covers support for mainstream hardware platforms including Ascend, Cambricon, Intel GPU, and Apple MPS.
54
PaddlePaddle / docs
This repository contains the source files for the official PaddlePaddle documentation platform. It organizes content into specific directories for API references, user guides, and tutorials to support developers. The project also provides CI scripts and build instructions to facilitate local documentation generation and community contributions.
39
PaddlePaddle / PaddleCustomDevice
PaddleCustomDevice is a custom hardware integration solution provided by the PaddlePaddle deep learning framework. This project aims to help developers efficiently integrate various third-party hardware backends into the PaddlePaddle ecosystem. Currently, it supports a variety of mainstream hardware platforms, including Ascend, Cambricon, Intel GPU, and Apple MPS.
38
PaddlePaddle / PaConvert
This tool is officially maintained by Paddle and aims to achieve efficient automated migration from PyTorch code to PaddlePaddle code. It supports one-click conversion of over 1,600 PyTorch APIs and 200 torchvision APIs, maintaining an average conversion rate of over 95% in tests. The conversion process is operated via the command line, preserves the style and structure of the original code, and provides detailed conversion logs and summaries.
34
PaddlePaddle / community
The PaddlePaddle community serves as a central hub for developers to contribute to the framework through code improvements, documentation, and presentations. It provides structured governance, specialized working groups, and various mentorship programs to support active participation. Contributors are recognized through official certifications, release notes, and inclusion in the project's authorship records.
29

// use cases by project

Paddle
  • 01Automatic distributed parallel training for large-scale models
  • 02High-order automatic differentiation for scientific computing applications
  • 03Heterogeneous multi-chip adaptation through a standardized, pluggable architecture
Paddle
  • 01Unified dynamic and static graph training with automatic parallelism
  • 02Integrated large model training and inference workflows
  • 03High-order differentiation for scientific computing and differential equations
PaddleFormers
  • 01Supports the full-process training of 100+ mainstream Large Language Models and Vision-Language Models
  • 02Provides various efficient fine-tuning and alignment capabilities including CPT, SFT, and DPO
  • 03Deeply adapts to domestic computing platforms such as Kunlunxin, Iluvatar CoreX, and MetaX
PaddleX
  • 01Provides 33 model pipelines covering key areas such as OCR, object detection, image classification, and document parsing.
  • 02Supports high-performance inference, service-oriented deployment, and edge deployment to meet the flexible needs of various application scenarios.
  • 03Adapts to various mainstream hardware including NVIDIA GPUs, Kunlunxin, and Ascend, achieving seamless cross-platform switching and efficient operation.
FastDeploy
  • 01Load-balanced PD separation and dynamic instance role switching
  • 02Compatibility with OpenAI API interfaces and the vLLM ecosystem
  • 03High-performance inference and full quantization support for multi-hardware platforms

// comparisons

FastDeploy vs ncnnFastDeploy vs unregistry

// related topics

Deep Learning (9)LLM (3)Machine Learning (3)GPU (3)Inference (2)