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
TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network Created by Seunghoon Hong, Junhyuk Oh,

42 Jun 29, 2022
A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)

EfficientNet PyTorch Quickstart Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with: from efficientnet_pytorch impor

Luke Melas-Kyriazi 7.2k Jan 06, 2023
Contrastive Language-Image Pretraining

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

OpenAI 11.5k Jan 08, 2023
TensorFlow 2 implementation of the Yahoo Open-NSFW model

TensorFlow 2 implementation of the Yahoo Open-NSFW model

Bosco Yung 101 Jan 01, 2023
covid question answering datasets and fine tuned models

Covid-QA Fine tuned models for question answering on Covid-19 data. Hosted Inference This model has been contributed to huggingface.Click here to see

Abhijith Neil Abraham 19 Sep 09, 2021
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
RP-GAN: Stable GAN Training with Random Projections

RP-GAN: Stable GAN Training with Random Projections This repository contains a reference implementation of the algorithm described in the paper: Behna

Ayan Chakrabarti 20 Sep 18, 2021
Have you ever wondered how cool it would be to have your own A.I

Have you ever wondered how cool it would be to have your own A.I. assistant Imagine how easier it would be to send emails without typing a single word, doing Wikipedia searches without opening web br

Harsh Gupta 1 Nov 09, 2021
IsoGCN code for ICLR2021

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

horiem 39 Nov 25, 2022
Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Exercises and project documentation for the 3. Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Simona Mircheva 1 Jan 13, 2022
The implementation of the lifelong infinite mixture model

Lifelong infinite mixture model 📋 This is the implementation of the Lifelong infinite mixture model 📋 Accepted by ICCV 2021 Title : Lifelong Infinit

Fei Ye 5 Oct 20, 2022
Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

Yandex 662 Nov 20, 2022
Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022
Code used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"

Train longer, generalize better - Big batch training This is a code repository used to generate the results appearing in "Train longer, generalize bet

Elad Hoffer 145 Sep 16, 2022
[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation

Self-Supervised Learning of Image Scale and Orientation Estimation (BMVC 2021) This is the official implementation of the paper "Self-Supervised Learn

Jongmin Lee 17 Nov 10, 2022
AntiFuzz: Impeding Fuzzing Audits of Binary Executables

AntiFuzz: Impeding Fuzzing Audits of Binary Executables Get the paper here: https://www.usenix.org/system/files/sec19-guler.pdf Usage: The python scri

Chair for Sys­tems Se­cu­ri­ty 88 Dec 21, 2022
Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Updated Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol. Introduction This balenaCloud (previously

Remko 1 Oct 17, 2021
Application of K-means algorithm on a music dataset after a dimensionality reduction with PCA

PCA for dimensionality reduction combined with Kmeans Goal The Goal of this notebook is to apply a dimensionality reduction on a big dataset in order

Arturo Ghinassi 0 Sep 17, 2022
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022