Meu primeiro repositório com flask e predições em Python para MLOps.
Instale se precisar CookieCutter: pip install cookiecutter https://drivendata.github.io/cookiecutter-data-science/
Deve ter o git https://git-scm.com/downloads
Clonar os repositório em:
Criar o ambiente virtual, indo até a pasta raiz do repositório e rodar: No windows: venv/Scripts/activate caso não rode por falta de permissão no powershell (recomendado): Get-ExecutionPolicy se estiver 'Restricted' ou 'AllSigned' rode: Set-ExecutionPolicy Unrestricted Escolha opção A (for all)
No Linux:
venv/bin/activate
Para utilizar o projeto deve-se primeiramente criar um ambiente virtual e rodar o 'requirements.txt' usando: pip install -r requirements.txt
Criar as variáveis de ambiente: No Windows: Set-Content -Path BASIC_AUTH_USERNAME -Value 'seuUsuario' Set-Content -Path BASIC_AUTH_PASSWORD -Value 'suaSenha'
teste com:
Set-Location Env:
ls
e veja de estão lá
No Linux:
export BASIC_AUTH_USERNAME=seuUsuario
export BASIC_AUTH_PASSWORD=suaSenha
├── 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 (OUR DATA ARE HERE).
│ └── 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 (OUR models.sav ARE HERE)
│
├── 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`. (OUR NOTEBOOKS ARE HERE)
│
├── 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 src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── app <-flask server generating (OUR MAIM APP IS HERE)
│ │ └── maim.py
│ │
│ ├── 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
│ │ └── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience