Radiant MLHub Tutorials

This repository contains introductions and Jupyter Notebook examples on how to access Radiant MLHub API.

You can start by reading the introductory guide, or jump into using the Jupyter Notebook examples and interact with the API.

Run in Hosted Environment

Note that the examples involving downloading assets will download those assets to the remote environment and not to your local file system. To download these assets locally you must run the notebooks locally (see next section).

Run on Binder

You can run the Jupyter Notebook examples using Binder by clicking on the "launch binder" badge above or this link. The Binder environment will automatically install any dependencies required by the notebooks.

Run in Google Colab

You can run the Jupyter Notebook examples using Google Colab by clicking on the "Open in Colab" badge above or this link. Colab does not have a mechanism for automatically installing dependencies to a notebook environment like Binder, so you will need to install all dependencies within the notebook as follows:

%pip install radiant-mlhub~=0.1.2 tifffile==2019.7.26.2 pandas~=1.2.0 matplotlib~=3.3.4 scikit-image~=0.18.1

Run Locally

To run the notebooks locally:

1. Create & activate a virtual environment of your choice

2. Install dependencies:

   pip install -r requirements_dev.txt

3. Run Jupyter Notebook server:

   # Example notebooks are in the notebooks/ directory
   jupyter notebook notebooks/

Documentation

You can access the full documentation of Radiant MLHub API here.

Contribute

If you find these guides useful and would like to contribute, make a pull request or send us an email at [email protected].