lstm

A repo for the project LSTM.

Project Organization

    ├── LICENSE
    ├── 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
    │
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `2024-08-02-jqp-initial-data-exploration`
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    │
    └── src                <- Source code directory for the project
         ├─ __init__.py    <- Makes this src directory a Python module
         ├─ core.py        <- Refactored code goes here
         └─ utils.py       <- Where to store helper functions that do not belong in `core.py`

Installation on Mac and Linux using virtualenv

• in the project directory lstm run virtualenv --python=/usr/local/bin/python3 venv
• activate the environment with source venv/bin/activate
• edit the .env file and add KEY=VALUE for any items you wish to import (NOTE this will NOT be stored in Github. NEVER check this in to a repo!)

Installation using pipenv

• make sure python3 and pipenv is installed on your system
• in the project directory lstm run virtualenv --python=/usr/local/bin/python3 venv
• edit the .env file and add KEY=VALUE for any items you wish to import (NOTE this will NOT be stored in Github. NEVER check this in to a repo!)
• cd to the project directory and run pipenv run python src/core.py or whatever file you wish to run

Running Jupyter Notebooks with pipenv

• pipenv install ipykernel
• pipenv shell
• python -m ipykernel install --user
• jupyter notebook will launch the notebook browser

Project based on the cookiecutter data science project template. #cookiecutterdatascience