The notebooks in this repository provide examples and tutorials to cover some common uses of Radiant MLHub data. All examples utilize Python in Jupyter Notebooks.
To view an overview of the lessons available in this repository, visit this tutorial overview.
Each subdirectory contains its own requirements.txt file that contains all the dependencies needed to be able to run the subdirectory's notebook(s). To run a given set of notebooks locally:
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Create & activate a virtual environment of your choice.
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Change to the subdirectory you wish to run:
cd tutorials/<TARGET DIRECTORY>
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Install dependencies:
pip install -r requirements.txt
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Run Jupyter Notebook server:
jupyter notebook
You can access the full documentation of Radiant MLHub API and Python Client here.
If you find these guides useful and would like to contribute, make a pull request or send us an email at ml@radiant.earth.