DEPRECATED: PLEASE USE https://github.com/gnn-tracking/hyperparameter_optimization2 INSTEAD
This repository hosts submission scripts and framework for hyperparameter optimization of the models defined in the main library. Part of this are fully parameterized versions of the models.
- Uses ray tune as overarching framework. For deployment on SLURM managed HPC nodes, ray workers are deployed as SLURM batch jobs (as further described here)
- Optuna is used to power the search
- Results are reported to wandb/weights & biases
First, follow the instructions from the main library to set up the conda environment and install the package
pip install -e .
git submodule update --init --recursive
- Use or adapt one of the tuning scripts in
scripts/
- ray-tune-slurm-demo: Simple project to try out some of the capabilities of ray tune and wandb, especially with batch submission
- wandb-osh: package to trigger wandb syncs on compute nodes without internet
- additional stoppers for ray tune: package with additional early stopping conditions for trials used in our HPO