To fulfill the dependencies run:
-- pip install -r requirements.txt
To process data raw data run:
-- python src\data\make_dataset.py data\raw\corruptmnist data\processed
Or access data through from dvc:
-- dvc pull
To train model and generate training run png run:
-- python src\models\train_model.py
To use trained model for prediction run:
-- python src\models\predict_model.py models\trained_models\model.pt data\processed\testset.pt
Build dockerimage to run training-pipeline:
-- docker build -f trainer.dockerfile . -t trainer:latest --no-cache \
├── 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.
├── .dvc
│ ├── plots <- Data from third party sources.
│ │ └── ...
│ ├── .gitignore
│ └── config
├── .dvcignore <- Ignore file for dvc
├── data.dvc <- Configuration details for dvc
│
├── tests
│ ├── test_data.py <- Test data
│ └── test_model.py <- Test model
├── 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`.
│
├── 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
│ │
│ ├── 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
├── .gitignore <- Ignore file for git
├── test_environment.py<- Test for environment setup.
├── Dockerfile <- dockerfile to be activated by gcp trigger at git push
├── cloudbuild.yaml <- Build file for Dockerfile in gcp
├── trainer.dockerfile <- Local dockerfile not cloud
├── .flake8 <- Configuration file for flake8
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience