pyscaffoldext-dsproject-vscode

This extension is forked from the awesome PyScaffold DSProject extension.

This version of the extension extends the functionality by adding a scaffold component for using the VS Code container development integration.

To benefit from this setup, you must use VS Code with Python and Remote - Containers extensions installed. Docker should also be installed on the host machine.

Usage

Just install this package with pip install pyscaffoldext-dsproject-vscode and note that putup -h shows a new option --dsproject-vscode. Creating a data science project with VS Code container development integration is then as easy as:

putup --dsproject-vscode my_ds_project

After the project is created, set the following softlinks from the project root:

ln -s path.env .env

If you want to develop locally, create this softlink:

ln -s .devcontainer/devcontainer.local.json .devcontainer/devcontainer.json

Alternatively, if you want to develop using a remote docker server, use this:

ln -s .devcontainer/devcontainer.remote.json .devcontainer/devcontainer.json

If you have an application that will run inside the dev container that requires a port, specify the list of ports under the forwardPorts attribute in the following files:

.devcontainer/devcontainer.local.json
.devcontainer/devcontainer.remote.json

Remote docker server

To use a remote docker server, specify the docker.host attribute in the .vscode/settings.json following the template in the file.

Edit the value of the REMOTE_PATH attribute in the .env file with the absolute path of the project in the remote server.

pyscaffoldext-dsproject

PyScaffold extension tailored for Data Science projects. This extension is inspired by cookiecutter-data-science and enhanced in many ways. The main differences are that it

1. advocates a proper Python package structure that can be shipped and distributed,
2. uses a conda environment instead of something virtualenv-based and is thus more suitable for data science projects,
3. more default configurations for Sphinx, py.test, pre-commit, etc. to foster clean coding and best practices.

Also consider using dvc to version control and share your data within your team. Read this blogpost to learn how to work with JupyterLab notebooks efficiently by using a data science project structure like this.

The final directory structure looks like:

├── AUTHORS.rst             <- List of developers and maintainers.
├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── 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.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yaml        <- The conda environment file for reproducibility.
├── 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 description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- Use `python setup.py develop` to install for development or
|                              or create a distribution with `python setup.py bdist_wheel`.
├── src
│   └── PYTHON_PKG          <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

See a demonstration of the initial project structure under dsproject-demo and also check out the the documentation of PyScaffold for more information.

Usage

Just install this package with pip install pyscaffoldext-dsproject and note that putup -h shows a new option --dsproject. Creating a data science project is then as easy as:

putup --dsproject my_ds_project

Note

This project has been set up using PyScaffold 3.2. For details and usage information on PyScaffold see https://pyscaffold.org/.