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.

TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Sionna: An Open-Source Library for Next-Generation Physical Layer Research Sionna™ is an open-source Python library for link-level simulations of digi

NVIDIA Research Projects 313 Dec 22, 2022
A PyTorch Lightning Callback for pushing models to the Hugging Face Hub 🤗⚡️

hf-hub-lightning A callback for pushing lightning models to the Hugging Face Hub. Note: I made this package for myself, mostly...if folks seem to be i

Nathan Raw 27 Dec 14, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
Rethinking Portrait Matting with Privacy Preserving

Rethinking Portrait Matting with Privacy Preserving This is the official repository of the paper Rethinking Portrait Matting with Privacy Preserving.

184 Jan 03, 2023
This is a collection of all challenges in HKCERT CTF 2021

香港網絡保安新生代奪旗挑戰賽 2021 (HKCERT CTF 2021) This is a collection of all challenges (and writeups) in HKCERT CTF 2021 Challenges ID Chinese name Name Score S

10 Jan 27, 2022
On-device speech-to-index engine powered by deep learning.

On-device speech-to-index engine powered by deep learning.

Picovoice 30 Nov 24, 2022
Recognize Handwritten Digits using Deep Learning on the browser itself.

MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the

Harjyot Bagga 7 May 28, 2022
The implementation for "Comprehensive Knowledge Distillation with Causal Intervention".

Comprehensive Knowledge Distillation with Causal Intervention This repository is a PyTorch implementation of "Comprehensive Knowledge Distillation wit

Xiang Deng 10 Nov 03, 2022
hipCaffe: the HIP port of Caffe

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cent

ROCm Software Platform 126 Dec 05, 2022
PolyGlot, a fuzzing framework for language processors

PolyGlot, a fuzzing framework for language processors Build We tested PolyGlot on Ubuntu 18.04. Get the source code: git clone https://github.com/s3te

Software Systems Security Team at Penn State University 79 Dec 27, 2022
JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction

JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction CSCI 544 Final Project done by: Mohammed Alsayed, Shaayan Syed, Mohammad Alali, S

Smit Patel 3 Dec 28, 2022
Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

StyleGAR TODO: add arxiv link Implementation of Inverting Generative Adversarial Renderer for Face Reconstruction TODO: for test Currently, some model

155 Oct 27, 2022
A GPT, made only of MLPs, in Jax

MLP GPT - Jax (wip) A GPT, made only of MLPs, in Jax. The specific MLP to be used are gMLPs with the Spatial Gating Units. Working Pytorch implementat

Phil Wang 53 Sep 27, 2022
Classification models 1D Zoo - Keras and TF.Keras

Classification models 1D Zoo - Keras and TF.Keras This repository contains 1D variants of popular CNN models for classification like ResNets, DenseNet

Roman Solovyev 12 Jan 06, 2023
Tensorflow python implementation of "Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos"

Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos This repository is the official tensorflow python implementation

Yasamin Jafarian 287 Jan 06, 2023
Alpha-Zero - Telegram Group Manager Bot Written In Python Using Pyrogram

✨ Alpha Zero Bot ✨ Telegram Group Manager Bot + Userbot Written In Python Using

1 Feb 17, 2022
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations)

Graph Neural Networks with Learnable Structural and Positional Representations Source code for the paper "Graph Neural Networks with Learnable Structu

Vijay Prakash Dwivedi 180 Dec 22, 2022
Prior-Guided Multi-View 3D Head Reconstruction

Prior-Guided Head MVS This repository includes some reconstruction results of our IEEE TMM 2021 paper, Prior-Guided Multi-View 3D Head Reconstruction.

11 Aug 17, 2022
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022