With the addition of the Machine Learning Professional plug-in, OpendTect development is further extended by allowing Python as an additional development environment. Starting from within OpendTect, a Python shell, or integrated development environment (IDE), can be launched, and one with direct access to the OpendTect-Python link, libraries and data structures that are a pre-requisite to further research and development. An advantage of Python is its flexibility, and the ability to add new tools and libraries from the command line, so, even this environment can be easily customized with additional analytics libraries. Recently we have added extended support for PyTorch, on top of our already extensive Tensorflow/Keras and scikit-learn support.
We are excited to invite you to join our OpendTect Machine Learning Developers’ Community!
Identifying the most appropriate geoscience Machine Learning techniques, workflows and data access patterns and using them optimally can be challenging for the best of us.