Cookiecutter Data Science for VSCode Extension

The following template presents an iteration coming from the cookie cutter template and includes the key building blocks for making experiments for DVC VSCode extension.

Some of the cookie cutter template features have been dropped out for simplicity.

Setup instructions

1. Install DVC VSCode extension from the marketplace. For that, Inside your IDE, go to Extensions and search for DVC

2. Clone this repository

3. Read the documentation and customize the ´dvc.yaml´ , ´params.yaml´ files.

4. Open a terminal in VSCode

DVC Experiments

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Managing machine learning experiments can be challenging. The ultimate goal of DVC OpenSource extension is to make analysis, collaboration and reproducibility easier.

With the extension , we are able to visualize experiments in the table and plots.

For defining stages We can use CLI or ´dvc.yaml´ file. Please refer here for a template of how to build you own pipeline with DVC and here to customize your parameters. Visit our community gem as a common FAQ that might take place when executing you pipeline.

For defining metrics we use DVCLive python library. As a starting point we will be using log_metrics() and sklearn_plots() methods to setup experiments

For experiments, they can be executed from the CLI or the UI coming from the table. The main CLI command will be using will ´dvc exp run´ and ´dvc repro´ for CLI interface.

The resulting directory structure

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The directory structure of your new project looks like this:

├── 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.
│
├── 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.
│
├── 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
│
├── dvc.yaml           <- includes pipeline stages definition. Linked to params.yaml file 
└── params.yaml        <- define the hyperparameters we want to iterate from that will be useful for the table

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements

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pip install -r requirements.txt

Running the tests

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py.test tests