An off-line judger supporting distributed problem repositories

Related tags

Deep LearningThaw
Overview

Thaw

中文 | English

Thaw is an off-line judger supporting distributed problem repositories. Everyone can use Thaw release problems with license on GitHub or other open sourse repositories, get problems locally and judge programs automatically.

We request people who write problems add licenses to their problems or clear copyright notice so that problems can be shared easier, and data generator and solution as well, which can support generalization test and help improve the problems.

By making it off-line, distributed and based on GitHub, situations in which a few administrators examine a large number of problems can be prevented. The nice atmosphere on GitHub can also make quanlity of discussion higher.

We also hope to improve traditional methods of judging. Making it off-line helps avoid creating motivation of cheating, and we hope people do not exceedingly pursue the optimization of the program, but concentrate on things like the readability of code. We will support judging with less strict limit of time and memory, and estimating the polynomial time complexity of a program.

We will make it simple to configure and highly hackable too.

Installation

Package will be created and be released on pip and GitHub after enough improvement and tests on 0.0.1.

Manual

See ./docs/manual.md.

Also See sampleproblem about how to write a problem with Thaw for example.

Usage

Sorry for the project being incompleted. But it will be completed as quick as possible.

Below are expected result.

Create a problem:

thaw init .
git init sampleproblem
cd sampleproblem
thaw init hello_world
cd hello_world
vim -p hello_world.zh-CN.md hello_world.en-US.md std.cpp std.py checker.py

Solve a problem:

vim hello_world.cpp
thaw submit hello_world.cpp

Release a problem:

git add .
git commit -m "add hello_world"
git remote add origin https://github.com/username/sampleproblem
git push origin master

Download a problem:

git clone https://github.com/username/sampleproblem

How to contribute

Welcome to join us! You can improve Thaw by send an Issue or a Pull Request.

Or create a GitHub repository to release your problem according to the format, and create open and shared atmosphere of algorithm contest with us. You can add the url of your repository to repositories.dat to make it easy for others to get your problems and spread your problems.

We will create a Gitter group when more people join.

Contributors

Thank peers who improve Thaw together and share problems

License

AGPL

Owner
countercurrent_time
countercurrent_time
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Jina AI 794 Dec 31, 2022
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

1 Jan 23, 2022
SymPy-powered, Wolfram|Alpha-like answer engine totally in your browser, without backend computation

SymPy Beta SymPy Beta is a fork of SymPy Gamma. The purpose of this project is to run a SymPy-powered, Wolfram|Alpha-like answer engine totally in you

Liumeo 25 Dec 21, 2022
OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021)

OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021) Video demo We here provide a video demo from co

20 Nov 25, 2022
PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{CV2018, author = {Donny You ( Donny You 40 Sep 14, 2022

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation"

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use

57 Dec 27, 2022
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

DAGAN This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruct

TensorLayer Community 159 Nov 22, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
Videocaptioning.pytorch - A simple implementation of video captioning

pytorch implementation of video captioning recommend installing pytorch and pyth

Yiyu Wang 2 Jan 01, 2022
Deep Probabilistic Programming Course @ DIKU

Deep Probabilistic Programming Course @ DIKU

52 May 14, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 03, 2023
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution Pytorch implementation of local motion and contrast pr

Xinyi Ying 28 Dec 15, 2022
GANTheftAuto is a fork of the Nvidia's GameGAN

Description GANTheftAuto is a fork of the Nvidia's GameGAN, which is research focused on emulating dynamic game environments. The early research done

Harrison 801 Dec 27, 2022
NHS AI Lab Skunkworks project: Long Stayer Risk Stratification

NHS AI Lab Skunkworks project: Long Stayer Risk Stratification A pilot project for the NHS AI Lab Skunkworks team, Long Stayer Risk Stratification use

NHSX 21 Nov 14, 2022
[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Codes for this paper The Lottery Tickets Hypo

VITA 59 Dec 28, 2022
Gym Threat Defense

Gym Threat Defense The Threat Defense environment is an OpenAI Gym implementation of the environment defined as the toy example in Optimal Defense Pol

Hampus Ramström 5 Dec 08, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

117 Dec 28, 2022
Hooks for VCOCO

Verbs in COCO (V-COCO) Dataset This repository hosts the Verbs in COCO (V-COCO) dataset and associated code to evaluate models for the Visual Semantic

Saurabh Gupta 131 Nov 24, 2022
Deep Residual Networks with 1K Layers

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

Kaiming He 856 Jan 06, 2023