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
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Jupyter notebooks showing best practices for using cx_Oracle, the Python DB API for Oracle Database

Python cx_Oracle Notebooks, 2022 The repository contains Jupyter notebooks showing best practices for using cx_Oracle, the Python DB API for Oracle Da

Christopher Jones 13 Dec 15, 2022
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
Get started with Machine Learning with Python - An introduction with Python programming examples

Machine Learning With Python Get started with Machine Learning with Python An engaging introduction to Machine Learning with Python TL;DR Download all

Learn Python with Rune 130 Jan 02, 2023
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Aritra Roy Gosthipaty 23 Dec 24, 2022
A Python implementation of global optimization with gaussian processes.

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

fernando 6.5k Jan 02, 2023
A Closer Look at Reference Learning for Fourier Phase Retrieval

A Closer Look at Reference Learning for Fourier Phase Retrieval This repository contains code for our NeurIPS 2021 Workshop on Deep Learning and Inver

Tobias Uelwer 1 Oct 28, 2021
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

Edson-Niu 60 Nov 29, 2022
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

CIFS This repository provides codes for CIFS (ICML 2021). CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Sel

Hanshu YAN 19 Nov 12, 2022
Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Neural Circuit Policies Enabling Auditable Autonomy Online access via SharedIt Neural Circuit Policies (NCPs) are designed sparse recurrent neural net

8 Jan 07, 2023
Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) We consider how a user of a web servi

joisino 20 Aug 21, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
Baseline inference Algorithm for the STOIC2021 challenge.

STOIC2021 Baseline Algorithm This codebase contains an example submission for the STOIC2021 COVID-19 AI Challenge. As a baseline algorithm, it impleme

Luuk Boulogne 10 Aug 08, 2022
LIAO Shuiying 6 Dec 01, 2022
USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

116 Jan 04, 2023
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

6 Dec 19, 2022
Experiments with the Robust Binary Interval Search (RBIS) algorithm, a Query-Based prediction algorithm for the Online Search problem.

OnlineSearchRBIS Online Search with Best-Price and Query-Based Predictions This is the implementation of the Robust Binary Interval Search (RBIS) algo

S. K. 1 Apr 16, 2022
Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Sparse Steerable Convolution (SS-Conv) Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and

25 Dec 21, 2022