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license: apache-2.0
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---
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Cookiecutter-MLOps |
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============================== |
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A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while |
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having MLOps best practices applied. |
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Instructions |
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------------ |
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1. Clone the repo. |
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2. Run `make dirs` to create the missing parts of the directory structure described below. |
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3. *Optional:* Run `make virtualenv` to create a python virtual environment. Skip if using conda or some other env manager. |
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1. Run `source env/bin/activate` to activate the virtualenv. |
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4. Run `make requirements` to install required python packages. |
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5. Put the raw data in `data/raw`. |
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6. To save the raw data to the DVC cache, run `dvc add data/raw` |
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7. Edit the code files to your heart's desire. |
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8. Process your data, train and evaluate your model using `dvc repro` or `make reproduce` |
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9. To run the pre-commit hooks, run `make pre-commit-install` |
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10. For setting up data validation tests, run `make setup-setup-data-validation` |
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11. For **running** the data validation tests, run `make run-data-validation` |
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12. When you're happy with the result, commit files (including .dvc files) to git. |
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Project Organization |
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------------ |
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├── LICENSE |
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├── Makefile <- Makefile with commands like `make dirs` or `make clean` |
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├── README.md <- The top-level README for developers using this project. |
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├── data |
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│ ├── processed <- The final, canonical data sets for modeling. |
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│ └── raw <- The original, immutable data dump |
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│ |
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├── models <- Trained and serialized models, model predictions, or model summaries |
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│ |
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), |
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│ the creator's initials, and a short `-` delimited description, e.g. |
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│ `1.0-jqp-initial-data-exploration`. |
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├── references <- Data dictionaries, manuals, and all other explanatory materials. |
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. |
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│ └── figures <- Generated graphics and figures to be used in reporting |
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│ └── metrics.txt <- Relevant metrics after evaluating the model. |
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│ └── training_metrics.txt <- Relevant metrics from training the model. |
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│ |
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. |
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│ generated with `pip freeze > requirements.txt` |
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│ |
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported |
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├── src <- Source code for use in this project. |
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│ ├── __init__.py <- Makes src a Python module |
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│ │ |
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│ ├── data <- Scripts to download or generate data |
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│ │ ├── great_expectations <- Folder containing data integrity check files |
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│ │ ├── make_dataset.py |
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│ │ └── data_validation.py <- Script to run data integrity checks |
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│ │ |
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│ ├── models <- Scripts to train models and then use trained models to make |
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│ │ │ predictions |
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│ │ ├── predict_model.py |
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│ │ └── train_model.py |
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│ │ |
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│ └── visualization <- Scripts to create exploratory and results oriented visualizations |
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│ └── visualize.py |
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│ |
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├── .pre-commit-config.yaml <- pre-commit hooks file with selected hooks for the projects. |
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├── dvc.lock <- constructs the ML pipeline with defined stages. |
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└── dvc.yaml <- Traing a model on the processed data. |
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-------- |
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<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p> |
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--- |
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To create a project like this, just go to https://dagshub.com/repo/create and select the **Cookiecutter DVC** project template. |
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Made with 🐶 by [DAGsHub](https://dagshub.com/). |
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