AI Project Template

TL;DR: To create a new AI project with Flask API from this template using cookiecutter, run:

pip install cookiecutter
cookiecutter https://github.com/sujitahirrao/ai-project-template

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(Information below is liberally copied from https://drivendata.github.io/cookiecutter-data-science/, with required modifications.)

A logical, reasonably standardized, but flexible project template for creating new Flask APIs using cookiecutter.

Why use this project structure?

We're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards — ultimately, the code quality is about correctness and reproducibility.

Code quality is important! Once started, it is not a process that lends itself to thinking carefully about the structure of your code or project layout, so it's best to start with a clean, logical structure and stick to it throughout. We think it's a pretty big win all around to use a fairly standardized setup like this one. Here's why:

Other people will thank you

A well-defined, standard project structure means that a newcomer can begin to understand an analysis without digging in to extensive documentation. It also means that they don't necessarily have to read 100% of the code before knowing where to look for very specific things.

Well organized code tends to be self-documenting in that the organization itself provides context for your code without much overhead. People will thank you for this because they can:

• Collaborate more easily with you on this analysis
• Learn from your analysis about the process and the domain
• Feel confident in the conclusions at which the analysis arrives

You will thank you

Ever tried to reproduce an analysis that you did a few months ago or even a few years ago? You may have written the code, but it's now impossible to decipher whether you should use make_figures.py.old, make_figures_working.py or new_make_figures01.py to get things done. Here are some questions we've learned to ask with a sense of existential dread:

• Are we supposed to go in and join the column X to the data before we get started or did that come from one of the notebooks?
• Come to think of it, which notebook do we have to run first before running the plotting code: was it "process data" or "clean data"?
• Where did the shapefiles get downloaded from for the geographic plots?
• Et cetera, times infinity.

These types of questions are painful and are symptoms of a disorganized project. A good project structure encourages practices that make it easier to come back to old work, for example separation of concerns, abstracting analysis as a DAG, and engineering best practices like version control.

Nothing here is binding

"A foolish consistency is the hobgoblin of little minds" — Ralph Waldo Emerson (and PEP 8!)

Disagree with a couple of the default folder names? Working on a project that's a little nonstandard and doesn't exactly fit with the current structure? Prefer to use a different package than one of the (few) defaults?

Go for it! This is a lightweight structure, and is intended to be a good starting point for many projects. Or, as PEP 8 put it:

Consistency within a project is more important. Consistency within one module or function is the most important. ... However, know when to be inconsistent -- sometimes style guide recommendations just aren't applicable. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don't hesitate to ask!

Requirements to use the cookiecutter template:

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• Python 3.x
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/sujitahirrao/flask-api-template

The resulting directory structure

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The directory structure of your new project looks like this:

├── 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.
│
├── logs                    <- Log API requests (e.g. input, response, stdout, stderr information)
│
├── 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-sa-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`
│
├── src                     <- Source code for use in this project.
│   └── {{ cookiecutter.package_name }}
│       ├── __init__.py     <- Makes src a Python module
│       │
│       ├── config.py       <- Configuration file for some global variables
│       │
│       ├── 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.py
│       │   └── train.py
│       │
│       └── visualization   <- Scripts to create exploratory and results oriented visualizations
│           └── visualize.py
│
├── tests                   <- Test scripts for unit testing (e.g. using pytest), 
│                              performance and load testing of the API
│
├── api.py                  <- Flask API script
│
└── .gitignore

Installing development requirements

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pip install -r requirements.txt

Running the tests

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pytest tests

Build documentation using Sphinx

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cd docs/
make html