Evaluating small neural networks for general-purpose lossy data compression
| config | ||
| results | ||
| src | ||
| .gitignore | ||
| .python-version | ||
| benchmark.py | ||
| job.pbs | ||
| main.py | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
neural compression
Example usage:
python main.py --debug train --dataset enwik9 --data-root ~/data/datasets/ml --method optuna --model transformer --model-save-path ~/data/ml-models/test-transformer.pt
python benchmark.py --debug train --dataset enwik9 --data-root ~/data/datasets/ml --method optuna --model cnn --model-save-path ~/data/ml-models/test-cnn.pt
Running locally
uv sync --all-extras
Running on the Ghent University HPC
See the Infrastructure docs for more information about the clusters.
module swap cluster/joltik # Specify the (GPU) cluster, {joltik,accelgor,litleo}
qsub job.pbs # Submit job
qstat # Check status