This repository provides a Docker-based development environment for your project. Follow the steps below to set up and start developing inside a Docker container using VSCode.
Make sure you have the following installed on your machine:
- Docker
- Visual Studio Code
- VSCode Dev Containers extension
- Clone this repository to your local machine.
- Open the project in VSCode.
- VSCode will detect the presence of a .devcontainer folder and suggest to reopen the project in a container. Click "Reopen in Container" when prompted.
- Wait for VSCode to build the Docker image and start the container. This might take a few minutes on the first run.
- Once the container is running, you are now inside the development environment. You can see the terminal is now running inside the container.
If you need to customize the Docker development environment, modify the .devcontainer/devcontainer.json, the Dockerfile.dev, or the requirements.txt. After making changes, restart the container to apply the modifications.
├── .devcontainer <- Folder for storing development container configuration files.
│
├── data
│ ├── 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-mg-initial-data-exploration`.
│
├── src <- Source code for use in this project.
│ ├── **init**.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or clean data
│ │ └── generate_dataset.py
│ │
│ ├── features <- Scripts to turn data into features for modeling
│ │ └── generate_features.py
│ │
│ ├── models <- Scripts to train and evaluate models
│ │ ├── train_models.py
│ │ └── evaluate_models.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ │ └── generate_visualizations.py
│
├── tests <- Tests for the source code used in this project.
│ Run using: pytest.
│
├── .example-env <- Example environment file for setting up your project environment.
│ Create a .env file based on this and fill out appropriate variables.
│
├── .gitignore <- File to specify intentionally untracked files to ignore.
│
├── Dockerfile.dev <- The Dockerfile for reproducing the analysis environment.
│
├── main.py <- The main file for running a Python API using FastAPI.
│ Run using: uvicorn main:app --host 0.0.0.0 --port 5000 --reload.
│
├── README.md <- The top-level README for developers using this project.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment.
│
├── run_pipelines.py <- The main file for running data, feature, and model pipelines.