Cookiecutter Deep Learning

A cookiecutter template for a DL/ML project. This is just an example based primarely on cookiecutter for data science and on my personal experience. It was compiled from different sources. See more examples here, here and here.

Requirements to use the cookiecutter template:

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• Python 2.7 or 3.5+
• 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

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/andreeas26/cookiecutter-deep-learning.git

The resulting directory structure

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The directory structure of your new project looks like this:

├── LICENSE
│
├── README.md               <- The top-level README for developers using this project.
│
├── configs                 <- Directory for yaml configuration files for model training, scoring, etc
│   └── logging             <- Configuration of python loggers
│
├── data                    <- Provides a place to store data for your project.
│   └── DATA_README.md      <- A readme describing the data and the annotation process if needed.
│
├── docker                  <- Contains one or many Dockerfiles for the project.
│
├── docs                    <- A default Sphinx project; see sphinx-doc.org for details
│
├── experiments             <- Contains all the experiments run during development
│   └── experiment_name     <- It can be automatically set: e.g. YYYY-MM-DD-hh-mm
│       │                 
│       ├── train           <- Trained and serialized models
│       └── evaluation      <- 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`.
│
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated graphics and figures to be used in reporting
│
├── project_name            <- Source code for use in this project.
│   ├── __init__.py         <- Makes src a Python module
│   ├── train.py            <- Defines the actual training loop for the model.
│   ├── evaluate.py         <- Defines the evaluation process for the models.
│   │
│   ├── data                <- Scripts to download or generate data
│   │   └── datasets.py
│   │
│   ├── models              <- Defines collection of ML models for the task unified by the interface in base.py
│   │   │                 
│   │   ├── base.py         
│   │   ├── simple_baseline.py
│   │   └── cnn.py
│   │
│   └── visualization       <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
├── tests                   <- Files necessary for running tests.
├── run.py                  <- Simplifies the execution of one or more of the src scripts 
└── requirements.txt 		<- The requirements file for reproducing the analysis environment, e.g.
                         		generated with `pip freeze > requirements.txt`. It will be used by docker.

Running the tests

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py.test tests