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A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

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d2m - Data to model

A machine learning pipeline for trustworthy and green models, enabling responsible AI:

  • Explainable AI, using SHAP, LIME or both.
  • Uncertainty estimation, using Bayesian dropout for neural networks.
  • Carbon emissions tracking and reporting, using CodeCarbon.

d2m lets you easily create and evaluate machine learning models for tabular and time series data, with built-in data profiling and feature engineering.

Usage

Tested on:

  • Linux
  • macOS
  • Windows with WSL 2
  1. Clone/download this repository.
  2. Place your datafiles (csv) in a folder with the name of your dataset (DATASET) inside assets/data/raw/, so the path to the files is assets/data/raw/[DATASET]/.
  3. Update params.yaml with the name of your dataset (DATASET), the target variable, and other configuration parameters.
  4. Build Docker container:
docker build -t d2m -f Dockerfile .
  1. Run the container:
docker run -p 5000:5000 -it -v $(pwd)/assets:/usr/d2m/assets -v $(pwd)/.dvc:/usr/d2m/.dvc d2m
  1. Open the website at localhost:5000 to use the graphical user interface.

Creating models on the command line

  1. Copy params.yaml from the host to the container (find CONTAINER_NAME by running docker ps):
docker cp params.yaml  [CONTAINER_NAME]:/usr/d2m/params.yaml
  1. Inside the interactive session in the container, run:
docker exec [CONTAINER_NAME] dvc repro

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A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

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