DEEP Open Catalogue: Massive Online Data Streams (MODS)
DEEP Open Catalog entry: DEEP Open Catalog
Project: This work is part of the DEEP Hybrid-DataCloud project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.
To start using this framework run:
git clone https://github.com/deephdc/mods cd mods pip install -e .
- This project has been tested in Ubuntu 18.04 with Python 3.6. Further package requirements are described in the
- (TBD later)
├── LICENSE ├── README.md <- The top-level README for developers using this project. ├── data │ └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── docker <- Directory for Dockerfile(s) │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials (if many user development), │ and a short `_` delimited description, e.g. │ `1.0-jqp-initial_data_exploration.ipynb`. │ ├── 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 mods can be imported ├── mods <- Source code for use in this project. │ ├── __init__.py <- Makes mods a Python module │ │ │ ├── dataset <- 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 │ │ └── model.py │ │ │ └── tests <- Scripts to perfrom code testing + pylint script │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ └── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the DEEP DS template. #cookiecutter #datascience