A Machine Learning data pipeline for the Change detection for Burned area Delineation (ChaBuD) challenge at the ECML/PKDD 2023 conference.
Launch into a JupyterLab environment on
Binder | SageMaker Studio Lab | Planetary Computer |
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To help out with development, start by cloning this repo-url
git clone <repo-url>
Then I recommend using mamba to install the dependencies. A virtual environment will also be created with Python and JupyterLab installed.
cd chabud2023
mamba env create --file environment.yml
Activate the virtual environment first.
mamba activate chabud
Finally, double-check that the libraries have been installed.
mamba list
This is for those who want full reproducibility of the virtual environment. Create a virtual environment with just Python and conda-lock installed first.
mamba create --name chabud python=3.11 conda-lock=2.0.0
mamba activate chabud
Generate a unified conda-lock.yml
file
based on the dependency specification in environment.yml
. Use only when
creating a new conda-lock.yml
file or refreshing an existing one.
conda-lock lock --mamba --file environment.yml
Installing/Updating a virtual environment from a lockile. Use this to sync your
dependencies to the exact versions in the conda-lock.yml
file.
conda-lock install --mamba --name chabud conda-lock.yml
See also https://conda.github.io/conda-lock/output/#unified-lockfile for more usage details.
mamba activate chabud
python -m ipykernel install --user --name chabud # to install virtual env properly
jupyter kernelspec list --json # see if kernel is installed
jupyter lab &
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 and log metrics to WandB:
python trainer.py fit --trainer.max_epochs=100 \
--trainer.logger=WandbLogger \
--trainer.logger.entity=devseed \
--trainer.logger.project=chabud2023
To generate the CSV file of predicted masks on the validation set for submission to https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023.
python trainer.py test --model.submission_filepath=submission.csv
More options can be found using python trainer.py fit --help
, or at the
LightningCLI docs.