통일된 DataScience 폴더 구조 제공 및 가상환경 작업의 부담감 해소

Related tags

Deep LearningLucas
Overview


Lucas

Hits


coded by linux shell

목차


Patch Note 📜


Team member

Contributors/People

ympark gbhwang cbchun
https://github.com/pym7857 https://github.com/gbhwang https://github.com/bermmie1000
  • You can see team member and github profile
  • You should probably find team member's lastest project



Requirements

  • python 3.xx



Mac버전 CookieCutter (autoenv)

🚫 주의
$> brew install autoenv 로 다운로드 받아서 실행시키면 터미널 고장납니다.
반드시 autoenv Github 에서 git clone 으로 다운받아 주세요. (현재 시점 21.3.24)

⚠️ mac버전만 소개합니다.

1. How to Install autoenv

$ git clone git://github.com/inishchith/autoenv.git ~/.autoenv

2.폴더 진입 시, activate 구현하기

$ echo 'source ~/.autoenv/activate.sh' >> ~/.zshrc
$ source ~/.zshrc

🔔 하단의.env파일은 현재 repo의 cookiecutter에서 자동으로 생성해줍니다. (스킵)

# .env 파일
echo "HELLO autoenv"
{
    source .dev-venv/bin/activate
    echo "virtual env is successfully activated!"
} ||
{
    echo "[virtual env start] is failed!"
}

.env파일 설정 후 첫 폴더 진입시 .env파일을 신뢰하고 실행할지 않을 지에 대한 동의가 나타납니다. autoenv 이 부분은 .env파일이 악의적으로 변경되었을때 사용자에게 알리기 위해서 있기 때문에 즐거운 마음으로 Y를 눌러줍시다.
이제 정상적으로 가상환경이 activate된 것을 확인할 수 있습니다.

3.폴더 탈출 시, deactivate 구현하기

$> vi ~/.zshrc

마지막줄에 다음의 명령어를 추가해줍니다.

export AUTOENV_ENABLE_LEAVE='"enabled"' 

🔔 하단의.env.leave파일은 현재 repo의 cookiecutter에서 자동으로 생성해줍니다. (스킵)

# .env.leave 파일
echo "BYEBYE"
{
    deactivate
    echo "virtual env is successfully deactivated!"
} ||
{
    echo "[virtual env quit] is failed!"
}

.env.leave파일 설정 후 해당 폴더에서 나가면
정상적으로 가상환경이 deactivate 되는 것을 확인할 수 있습니다.

4.Alias 설정하기

echo 'alias cookie="bash [각자 컴퓨터의 상대경로/cookie_cutter_project_dir.sh]"' >> ~/.zshrc
ex) echo 'alias cookie="bash /Users/gbhwang/Desktop/Project/Test/Lucas/mac/cookie_cutter_project_dir.sh"' >> ~/.zshrc

맥 파일경로 확인법을 참고하여
각자 mac폴더안의 cookie_cutter_project_dir.sh 파일의 경로를 확인하여 zshrc에 넣어주시면 됩니다.

이렇게 하면 cookie 명령어 만으로 간단하게 스크립트를 실행시킬 수 있게 됩니다.
위와 같이 설정하면 cookie [프로젝트 생성할 경로] [프로젝트 이름] 명령어로 프로젝트를 생성할 수 있게 됩니다.

5.How to Use

$> cd "where-you-want"
$> git clone https://github.com/LS-ELLO/Lucas.git
$> cd Lucas
$> cd mac

$> cookie [where-you-want] [your-project-name]
ex) $> cookie . test111



Windows버전 CookieCutter (ps-autoenv)

도움 주신 규본님 감사합니다.
ps-autoenv를 사용합니다.

1.How to install ps-autoenv

Powershell 실행 (관리자 권한 실행)

PS> Install-Module ps-autoenv
PS> Add-Content $PROFILE @("`n", "import-module ps-autoenv")

2.Alias 설정하기 (git-bash)

참조

  1. C:/Program Files/Git/etc/profile.d/aliases.sh 파일을 관리자 권한으로 Text Editor에 실행시킵니다.

  2. 다음의 명령어를 추가합니다.
    alias cookie='bash cookie_cutter_project_dir.sh의 상대경로'
    ex) alias cookie='bash D:/Lucas/windows/cookie_cutter_project_dir.sh'

    (aliases.sh)

    # Some good standards, which are not used if the user
    # creates his/her own .bashrc/.bash_profile
    
    # --show-control-chars: help showing Korean or accented characters
    alias ls='ls -F --color=auto --show-control-chars'
    alias ll='ls -l'
    alias cookie='bash [where-your-cookie_cutter_project_dir.sh]'
    
    case "$TERM" in
    ...

3.How to Use

Git Bash 실행

bash> cd "where-this-repo-downloaded"
bash> cd windows
bash> cookie [where-you-want] [your-project-name]
ex) cookie . 1bot

Powershell 실행

PS> Import-Module ps-autoenv
PS> cd "where-your-cookiecutter-project"
ex. PS> cd "C:\Users\ympark4\Documents\1bot"
PS> press 'Y'
🚫 PSSecurityException 오류 발생할때

https://extbrain.tistory.com/118 를 참조해서 해결주세요.



The resulting directory structure

The directory structure of your new project looks like this:

├── LICENSE
├── Makefile
├── README.md          ← The top-level README for developers using this project.
├── data
│   ├── external       ← Data from third party sources.
│   ├── interim        ← Intermediate data that has been transformed.
│   ├── processed      ← The final, canonical data sets for modeling.
│   └── raw            ← The original, immutable data dump.
├── docs               ← A default Sphinx project; see sphinx-doc.org for details
├── models             ← Trained and serialized models, model predictions, or model summaries
├── notebooks          ← Jupyter notebooks. Naming convention is a number (for ordering), the creator's initials, and a short `-` delimited description, e.g. `1.0-jqp-initial-data-exploration`.
├── references         ← Data dictionaries, manuals, and all other explanatory materials.
├── reports            ← Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        ← Generated graphics and figures to be used in reporting
├── requirements.txt   ← The requirements file for reproducing the analysis environment, e.g. generated with `pip freeze > requirements.txt`
├── setup.py           ← makes project pip installable (pip install -e .) so src can be imported
├── src                ← Source code for use in this project.
│   ├── __init__.py  
│   ├── dataread      
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── features       
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── models     
│   │   └── __init__.py
│   │   └── example.py
│   │
│   ├── visualization    
│   │   └── __init__.py
│   │   └── example.py
├── App               
│   ├── android       
│   ├── ios           
│   ├── lib            
│   │   └── models
│   │   └── main.dart
│
└── .gitignore        



Owner
ello
ello
Allows including an action inside another action (by preprocessing the Yaml file). This is how composite actions should have worked.

actions-includes Allows including an action inside another action (by preprocessing the Yaml file). Instead of using uses or run in your action step,

Tim Ansell 70 Nov 04, 2022
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
An implementation of a discriminant function over a normal distribution to help classify datasets.

CS4044D Machine Learning Assignment 1 By Dev Sony, B180297CS The question, report and source code can be found here. Github Repo Solution 1 Based on t

Dev Sony 6 Nov 09, 2021
The Official Implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose [NIPS 2021].

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Release Notes The offical PyTorch implementation of Neural View Sy

Angtian Wang 20 Oct 09, 2022
基于PaddleOCR搭建的OCR server... 离线部署用

开头说明 DangoOCR 是基于大家的 CPU处理器 来运行的,CPU处理器 的好坏会直接影响其速度, 但不会影响识别的精度 ,目前此版本识别速度可能在 0.5-3秒之间,具体取决于大家机器的配置,可以的话尽量不要在运行时开其他太多东西。需要配合团子翻译器 Ver3.6 及其以上的版本才可以使用!

胖次团子 131 Dec 25, 2022
Deep Learning for humans

Keras: Deep Learning for Python Under Construction In the near future, this repository will be used once again for developing the Keras codebase. For

Keras 57k Jan 09, 2023
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

Nicola Dall'Asen 10 May 24, 2022
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Xinyan Zhao 29 Dec 26, 2022
Object tracking and object detection is applied to track golf puts in real time and display stats/games.

Putting_Game Object tracking and object detection is applied to track golf puts in real time and display stats/games. Works best with the Perfect Prac

Max 1 Dec 29, 2021
Unofficial pytorch implementation of the paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution"

DFSA Unofficial pytorch implementation of the ICCV 2021 paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution" (p

2 Nov 15, 2021
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories. Metrics provides i

Ben Hamner 1.6k Dec 26, 2022
Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

zychen 78 Dec 30, 2022
This is an official implementation for "Video Swin Transformers".

Video Swin Transformer By Ze Liu*, Jia Ning*, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin and Han Hu. This repo is the official implementation of "V

Swin Transformer 981 Jan 03, 2023
Quick program made to generate alpha and delta tables for Hidden Markov Models

HMM_Calc Functions for generating Alpha and Delta tables from a Hidden Markov Model. Parameters: a: Matrix of transition probabilities. a[i][j] = a_{i

Adem Odza 1 Dec 04, 2021
[AAAI22] Reliable Propagation-Correction Modulation for Video Object Segmentation

Reliable Propagation-Correction Modulation for Video Object Segmentation (AAAI22) Preview version paper of this work is available at: https://arxiv.or

Xiaohao Xu 70 Dec 04, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022
Studying Python release adoptions by looking at PyPI downloads

Analysis of version adoptions on PyPI We get PyPI download statistics via Google's BigQuery using the pypinfo tool. Usage First you need to get an acc

Julien Palard 9 Nov 04, 2022
Manipulation OpenAI Gym environments to simulate robots at the STARS lab

Manipulator Learning This repository contains a set of manipulation environments that are compatible with OpenAI Gym and simulated in pybullet. In par

STARS Laboratory 5 Dec 08, 2022
Reinforcement learning library in JAX.

Reinforcement learning library in JAX.

Yicheng Luo 96 Oct 30, 2022
ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021

ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation - SIGGRAPH 2021 Dataset Code Demos Authors: He Zhang, Yuting Ye, Tak

HE ZHANG 194 Dec 06, 2022