pytorch-caney 0.1.0

Methods for pytorch deep learning applications

pytorch-caney

Python package for lots of Pytorch tools.

Documentation

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

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. pytorch-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.

module load singularity # if a module needs to be loaded
singularity build --sandbox pytorch-caney-container docker://nasanccs/pytorch-caney:latest

Why Caney?

"Caney" means longhouse in Taíno.

Contributors

• Jordan Alexis Caraballo-Vega, [e-mail]
• Caleb Spradlin, [e-mail]

Contributing

Please see our guide for contributing to pytorch-caney.

SatVision

name	pretrain	resolution	#params
SatVision-B	MODIS-1.9-M	192x192	84.5M

SatVision Datasets

name	bands	resolution	#chips
MODIS-Small	7	128x128	1,994,131

Pre-training with Masked Image Modeling

To pre-train the swinv2 base model with masked image modeling pre-training, run:

torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg <config-file> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --enable-amp

For example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run:

singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container
Singularity> export PYTHONPATH=$PWD:$PWD/pytorch-caney
Singularity> torchrun --nproc_per_node 4 pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg pytorch-caney/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml --dataset MODIS --data-paths /explore/nobackup/projects/ilab/data/satvision/pretraining/training_* --batch-size 128 --output . --enable-amp

This example script runs the exact configuration used to make the SatVision-base model pre-training with MiM and the MODIS pre-training dataset.

singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container
Singularity> cd pytorch-caney/examples/satvision
Singularity> ./run_satvision_pretrain.sh

Fine-tuning Satvision-base

To fine-tune the satvision-base pre-trained model, run:

torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/finetuning/finetune.py --cfg <config-file> --pretrained <path-to-pretrained> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --enable-amp

See example config files pytorch-caney/examples/satvision/finetune_satvision_base_*.yaml to see how to structure your config file for fine-tuning.

Testing

For unittests, run this bash command to run linting and unit test runs. This will execute unit tests and linting in a temporary venv environment only used for testing.

git clone [e-mail]:nasa-nccs-hpda/pytorch-caney.git
cd pytorch-caney; bash test.sh

or run unit tests directly with container or anaconda env

git clone [e-mail]:nasa-nccs-hpda/pytorch-caney.git
singularity build --sandbox pytorch-caney-container docker://nasanccs/pytorch-caney:latest
singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container
cd pytorch-caney; python -m unittest discover pytorch_caney/tests

git clone [e-mail]:nasa-nccs-hpda/pytorch-caney.git
cd pytorch-caney; conda env create -f requirements/environment_gpu.yml;
conda activate pytorch-caney
python -m unittest discover pytorch_caney/tests

References

• Pytorch Lightning
• Swin Transformer
• SimMIM