From 4eabd623fe8e280927ff6986e38183a0d3a7ce76 Mon Sep 17 00:00:00 2001
From: TerenceLiu <terenceliu1012@gmail.com>
Date: Thu, 16 Jun 2022 19:50:54 +0800
Subject: [PATCH] Add 'How-To-Project.md'

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+# Guideline of Project Management
+
+There are multiple articles have illustruated that the project(code) management is important:
+1. [neptune.ai - how to organize deep learning projects](https://neptune.ai/blog/how-to-organize-deep-learning-projects-best-practices)
+2. [深度学习科研，如何高效进行代码和实验管理？](https://www.zhihu.com/question/269707221/answer/350542551)
+
+In this guideline, we will mainly show you to how to generate a tidy folder structure.
+
+## cookiecutter-data-science
+
+[Cookiecutter Data Science](https://github.com/drivendata/cookiecutter-data-science) is a logical, resonably standarized, but flexible project structure for doing and sharing data science work. To use it, you may follow these procedures:
+
+0. initilize your python environment: `conda create -n template python=3.x` `conda activate template`
+1. installation: `python -m pip install cookiecutter`
+2. initlize your project `cookiecutter -c v1 https://github.com/drivendata/cookiecutter-data-science` (with following options)
+  1. your project name
+  2. your repo name
+  3. author name
+  4. description
+  5. open source license
+  5. s3 bucket (optional)
+  6. python interpreter
+
+The folder structure is as follows:
+
+```bash
+├── LICENSE
+├── Makefile           <- Makefile with commands like `make data` or `make train`
+├── 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    <- Makes src a Python module
+│   │
+│   ├── data           <- Scripts to download or generate data
+│   │   └── make_dataset.py
+│   │
+│   ├── features       <- Scripts to turn raw data into features for modeling
+│   │   └── build_features.py
+│   │
+│   ├── models         <- Scripts to train models and then use trained models to make
+│   │   │                 predictions
+│   │   ├── predict_model.py
+│   │   └── train_model.py
+│   │
+│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
+│       └── visualize.py
+│
+└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io
+```
+
+
+Then, you can add the project to your git repo.