PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

Overview


English | 简体中文

Build Status Documentation Status Documentation Status Release License

Welcome to the PaddlePaddle GitHub.

PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms. PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 2.3 million developers. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.

Installation

Latest PaddlePaddle Release: v2.0

Our vision is to enable deep learning for everyone via PaddlePaddle. Please refer to our release announcement to track the latest features of PaddlePaddle.

Install Latest Stable Release:

# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu

More infomation about installation, please view Quick Install

Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 12 hours to train models online per day. If you can insist on that for five consecutive days, then you will receive an extra 48 hours. Click here to start.

FOUR LEADING TECHNOLOGIES

  • Agile Framework for Industrial Development of Deep Neural Networks

    The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.

  • Support Ultra-Large-Scale Training of Deep Neural Networks

    PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billions of features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved the real-time model updating with more than 1 trillion parameters. Click here to learn more

  • Accelerated High-Performance Inference over Ubiquitous Deployments

    PaddlePaddle is not only compatible with other open-source frameworks for models training, but also works well on the ubiquitous developments, varying from platforms to devices. More specifically, PaddlePaddle accelerates the inference procedure with the fastest speed-up. Note that, a recent breakthrough of inference speed has been made by PaddlePaddle on Huawei's Kirin NPU, through the hardware/software co-optimization. Click here to learn more

  • Industry-Oriented Models and Libraries with Open Source Repositories

    PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications. Click here to learn more

Documentation

We provide English and Chinese documentation.

  • Guides

    You might want to start from how to implement deep learning basics with PaddlePaddle.

  • Practice

    So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator.

  • API Reference

    Our new API enables much shorter programs.

  • How to Contribute

    We appreciate your contributions!

Communication

  • Github Issues: bug reports, feature requests, install issues, usage issues, etc.
  • QQ discussion group: 778260830 (PaddlePaddle).
  • Forums: discuss implementations, research, etc.

Copyright and License

PaddlePaddle is provided under the Apache-2.0 license.

Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures.

NLP_0-project Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures1. We are a "democratic" and c

3 Mar 16, 2022
[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

[NeurIPS 2021] Galerkin Transformer: linear attention without softmax Summary A non-numerical analyst oriented explanation on Toward Data Science abou

Shuhao Cao 159 Dec 20, 2022
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Aaron (Yinghao) Li 282 Jan 01, 2023
GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

GANimation: Anatomically-aware Facial Animation from a Single Image [Project] [Paper] Official implementation of GANimation. In this work we introduce

Albert Pumarola 1.8k Dec 28, 2022
codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

Self-paced Deep Regression Forests with Consideration on Ranking Fairness This is official codes for paper Self-paced Deep Regression Forests with Con

Learning in Vision 4 Sep 11, 2022
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Completer: Incomplete Multi-view Clustering via Contrastive Prediction This repo contains the code and data of the following paper accepted by CVPR 20

XLearning Group 72 Dec 07, 2022
FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction

FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction. It uses a customized encoder decoder architecture with spatio-temporal convolutions and channel ga

Tarun K 280 Dec 23, 2022
MolRep: A Deep Representation Learning Library for Molecular Property Prediction

MolRep: A Deep Representation Learning Library for Molecular Property Prediction Summary MolRep is a Python package for fairly measuring algorithmic p

AI-Health @NSCC-gz 83 Dec 24, 2022
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022
Package for extracting emotions from social media text. Tailored for financial data.

EmTract: Extracting Emotions from Social Media Text Tailored for Financial Contexts EmTract is a tool that extracts emotions from social media text. I

13 Nov 17, 2022
Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha

Mohamad Shahbazi 33 Dec 06, 2022
CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View Rethinking Semantic Segmentation: A Prototype View, Tianfei Zhou, Wenguan Wang, Ender Konukoglu and

Tianfei Zhou 239 Dec 26, 2022
A TensorFlow implementation of Neural Program Synthesis from Diverse Demonstration Videos

ViZDoom http://vizdoom.cs.put.edu.pl ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is pri

Hyeonwoo Noh 1 Aug 19, 2020
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Do you want a RL agent nicely moving on Atari? Rainbow is all you need! This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains bo

Jinwoo Park (Curt) 1.4k Dec 29, 2022
An official PyTorch implementation of the TKDE paper "Self-Supervised Graph Representation Learning via Topology Transformations".

Self-Supervised Graph Representation Learning via Topology Transformations This repository is the official PyTorch implementation of the following pap

Hsiang Gao 2 Oct 31, 2022
Visualizing Yolov5's layers using GradCam

YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di

Pooya Mohammadi Kazaj 200 Jan 01, 2023
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

Knut(Ke) Chen 134 Jan 01, 2023
A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA). Reading comprehension is a task to produ

Runqi Yang 394 Nov 08, 2022