search

digiLab-ai / twinLab-Tutorials

A demonstration of twinLab, a tool that makes it easy to incorporate Probabilisitic Machine Learning into engineering workflows
2 stars 0 forks source link

readme

twinLab Tutorials

digiLab twinLab slack

Welcome

Welcome to the twinLab-Tutorials repository, brought to you by the twinLab Product Team at digiLab Solutions Ltd. This repository contains Jupyter Notebooks that can serve as a starting point for using twinLab, a tool to augment engineering workflows with probabilistic machine learning.

If you would like to access a trial of twinLab to harness the power of built-in uncertainty quantification and build real-time emulators of your simulations, sign up for a trial here.

twinLab can be accessed through PyPI, or through GitHub. Feel free to log an issue on our public GitHub if you have a feature request or a bug report, or contact the Product Team at twinlab@digilab.co.uk. Documentation and guidance on how to use twinLab can be found via our documentation.

Similarly, if you spot an error in our tutorials, log an issue in this GitHub repository, or contact the Product Team.

Quick Start

Clone the repository and change directory to the project root:

git clone https://github.com/digiLab-ai/twinLab-Tutorials.git
cd twinLab-Tutorials

Install the dependencies:

poetry install --no-root

Copy the .env.example file to .env:

cp .env.example .env

Ensure that you fill out your twinLab login details in .env.

Finally, run the notebook you would like to see, e.g. Quickstart:

poetry run jupyter notebook notebooks/Quickstart.ipynb

You can find additional resources to run the notebooks in the resources folder, and note that through our documentation you can find additional example datasets as you get acquainted with using twinLab.