- Python 3.5+
- Cookiecutter Python package >= 1.4.0
pip install cookiecutterA simple, unstructured, and unopinionated project for data science that is deployable as an OpenShift s2i Application.
To generate your project run the cookiecutter and follow the prompts
cookiecutter https://github.com/opendatahub-io/odh-s2i-project-cookiecutter --checkout simpleAfter you've generated your project, the following project file structure will be created.
├── README.md
├── LICENSE
├── requirements.txt <- Used to install packages for s2i application
├── 0_start_here.ipynb <- Instructional notebook
├── 1_run_flask.ipynb <- Notebook for running flask locally to test
├── 2_test_flask.ipynb <- Notebook for testing flask requests
├── .gitignore <- standard python gitignore
├── .s2i <- hidden folder for advanced s2i configuration
│ └── environment <- s2i environment settings
├── gunicorn_config.py <- configuration for gunicorn when run in OpenShift
├── prediction.py <- the predict function called from Flask
└── wsgi.py <- basic Flask application
Follow the project README.md to create an s2i friendly data science project you can work on in Open Data Hub and deploy to OpenShift.
S2I buildable version of cookiecutter-data-science, "A logical, reasonably standardized, but flexible project structure for doing and sharing data science work."
To generate your project run the cookiecutter and follow the prompts
cookiecutter https://github.com/rh-aiservices-bu/odh-s2i-project-cookiecutter --checkout cdsAfter you've generated your project, the following project file structure will be created.
├── 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`.
│ ├── 0_start_here.ipynb <- Instructional notebook
│ ├── 1_run_flask.ipynb <- Notebook for testing flask requests
│ └── 2_test_flask.ipynb <- Notebook for testing flask requests
│
├── 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 use inside Jupyter notebooks and
│ also used to install packages for s2i application
│
├── 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 <- the predict function called from Flask
│ │ └── 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
├── .gitignore <- standard python gitignore
├── .s2i <- hidden folder for advanced s2i configuration
│ └── environment <- s2i environment settings
├── gunicorn_config.py <- configuration for gunicorn when run in OpenShift
└── wsgi.py <- basic Flask application
Follow the project README.md to create an s2i friendly data science project you can work on in Open Data Hub and deploy to OpenShift.