tensorflow-caney

Python package for lots of TensorFlow tools.

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Documentation

• Latest: https://nasa-nccs-hpda.github.io/tensorflow-caney

Objectives

• Library to process remote sensing imagery using GPU and CPU parallelization.
• Machine Learning and Deep Learning image classification and regression.
• Agnostic array and vector-like data structures.
• User interface environments via Notebooks for easy to use AI/ML projects.
• Example notebooks for quick AI/ML start with your own data.

Installation

The following library is intended to be used to accelerate the development of data science products for remote sensing satellite imagery, or any other applications. tensorflow-caney can be installed by itself, but instructions for installing the full environments are listed under the requirements directory so projects, examples, and notebooks can be run.

Note: PIP installations do not include CUDA libraries for GPU support. Make sure NVIDIA libraries are installed locally in the system if not using conda/mamba.

Production Container

module load singularity
singularity build --sandbox /lscratch/$USER/container/tensorflow-caney docker://nasanccs/tensorflow-caney:latest

Development Container

module load singularity
singularity build --sandbox /lscratch/$USER/container/tensorflow-caney docker://nasanccs/tensorflow-caney:dev

Why Caney?

"Caney" means longhouse in Taíno.

Contributors

• Jordan Alexis Caraballo-Vega, jordan.a.caraballo-vega@nasa.gov
• Caleb Spradlin, caleb.s.spradlin@nasa.gov

Contributing

Please see our guide for contributing to tensorflow-caney.

References

• TensorFlow Advanced Segmentation Models