semvec/pypi-packages huggingface dataset:
tensorflow-caney 0.5.1
Methods for tensorflow deep learning applications
License: MIT
Requires Python: >=3.8
Keywords: tensorflow-caney, deep-learning, machine-learning

# 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

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

## Development Container

```bash
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](CONTRIBUTING.md).

## References

- [TensorFlow Advanced Segmentation Models](https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models)