genco
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GENCO checkpoints for PFΔ, OPFData, and datakit. • 4 items • Updated
Reproduction guide: GENCO §5.2.
GENCO optimal-power-flow checkpoints on OPFData (paper): IEEE 14, 30, 57, and 118, and GOC 500 and 2000, seeds 42, 3, and 17.
Use graphkit branch genco-paper-repro. We do not guarantee that these checkpoints work on main, which is under development.
git clone -b genco-paper-repro https://github.com/gridfm/gridfm-graphkit.git
cd gridfm-graphkit
pip install -e .
pip install "huggingface_hub[cli]"
TORCH_CUDA_VERSION=$(python -c "import torch; print(torch.__version__ + ('+cpu' if torch.version.cuda is None else ''))")
pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH_CUDA_VERSION}.html
export MLFLOW_ALLOW_FILE_STORE=true
Run the commands below from that repo root. Graphkit loads {data_path}/{network}/raw/*.parquet. Configs are in scripts/opfdata/configs. Copy splits/ to scripts/opfdata/splits/ before evaluating. Each checkpoint folder has best_model_state_dict.pt and normalizer_stats.pt.
| Grid | Dataset | network folder |
|---|---|---|
| IEEE 14 | gridfm/opfdata_case14_ieee | case14_ieee |
| IEEE 30 | gridfm/opfdata_case30_ieee | case30_ieee |
| IEEE 57 | gridfm/opfdata_case57_ieee | case57_ieee |
| IEEE 118 | gridfm/opfdata_case118_ieee | case118_ieee |
| GOC 500 | gridfm/opfdata_case500_goc | case500_goc |
| GOC 2000 | gridfm/opfdata_case2000_goc | case2000_goc |
mkdir -p data/case118_ieee/raw scripts/opfdata/splits
hf download gridfm/opfdata_case118_ieee --repo-type dataset --local-dir data/case118_ieee/raw
hf download gridfm/genco-opfdata --include "case118_ieee/seed42/**" --include "splits/**" --local-dir genco-opfdata
cp genco-opfdata/splits/*.pt scripts/opfdata/splits/
gridfm_graphkit evaluate \
--config scripts/opfdata/configs/HGNSQ_penalty_11_OPFData_case118_default.yaml \
--data_path data \
--model_path genco-opfdata/case118_ieee/seed42/best_model_state_dict.pt \
--normalizer_stats genco-opfdata/case118_ieee/seed42/normalizer_stats.pt \
--batch_size 512
Repeat for the other grids and seeds. Download each dataset into data/<network>/raw.
{case14_ieee,case30_ieee,case57_ieee,case118_ieee,case500_goc,case2000_goc}/seed{42,3,17}/
best_model_state_dict.pt
normalizer_stats.pt
metrics.csv
HGNSQ_penalty_11_OPFData_<case>_<default|3|17>.yaml
splits/
train.pt val.pt test.pt shuffled_indices.pt
mlflow/train/
mlflow/eval/