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Basic Use

Basic Use#

Running jupyter lab#

mamba activate claymodel
python -m ipykernel install --user --name claymodel  # to install virtual env properly
jupyter kernelspec list --json                       # see if kernel is installed
jupyter lab &

Running the model#

The neural network model can be ran via LightningCLI v2. To check out the different options available, and look at the hyperparameter configurations, run:

python trainer.py --help
python trainer.py test --print_config

To quickly test the model on one batch in the validation set:

python trainer.py validate --trainer.fast_dev_run=True

To train the model for a hundred epochs:

python trainer.py fit --trainer.max_epochs=100

To generate embeddings from the pretrained model’s encoder on 1024 images (stored as a GeoParquet file with spatiotemporal metadata):

python trainer.py predict --ckpt_path=checkpoints/last.ckpt \
                          --data.batch_size=1024 \
                          --data.data_dir=s3://clay-tiles-02 \
                          --trainer.limit_predict_batches=1

More options can be found using python trainer.py fit --help, or at the LightningCLI docs.

Advanced#

See Readme on model root for more details.

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