Simples test with machine learning model serving with bentoML
├── data
│ ├── external -- folder with features from other databases (if any)
│ ├── interim -- intermediate data (pre-processed)
│ ├── processed -- processed data (features to be used)
│ └── raw -- raw data
├── references -- domain knowledge reference material
├── models -- storage of models
├── notebooks -- storage of experimental notebooks
├── reports -- data visualizations and other project outputs (storytelling)
│ └── figures
├── setup.py -- setup file for the module to be installable
├── bentoml-test -- folder where the scripts are stored
│ ├── features -- feature engineering scripts
│ └── models -- model training scripts
├── tests -- folder with test scripts
├── scripts -- folder with bash scripts used for setup the project
├── README.md -- description of what the project consists of, how to reproduce it and how to contribute
├── Dockerfile -- Describes the docker image.
├── .dockerign -- Describes assets to be ignore by docker.
├── params.yml -- file with all parameters used in the project, to facilitate documentation and reproduction
└── pyproject.toml -- file that specify all code dependencies
To install run
$ scripts/setup_env
After installing you could access the virtual enviroment with poetry shell
Create a github repository and add the remote origin to do yout project version control.
...
Install all project dependencies (prod and dev)
$ poetry install
Create your own branch, do your contribution and certifies that the code is complaint with the code standards:
$ poetry run check-code-quality
Now, open the pull request and enjoy code review :)