gridfm-graphkit 0.0.6

Grid Foundation Model

gridfm-graphkit

This library is brought to you by the GridFM team to train, finetune and interact with a foundation model for the electric power grid.

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Installation

You can install gridfm-graphkit directly from PyPI:

pip install gridfm-graphkit

To contribute or develop locally, clone the repository and install in editable mode:

git clone [e-mail]:gridfm/gridfm-graphkit.git
cd gridfm-graphkit
python -m venv venv
source venv/bin/activate
pip install -e .

For documentation generation and unit testing, install with the optional dev and test extras:

pip install -e .[dev,test]

CLI commands

An interface to train, fine-tune, and evaluate GridFM models using configurable YAML files and MLflow tracking.

gridfm_graphkit <command> [OPTIONS]

Available commands:

• train – Train a new model from scrathc
• finetune – Fine-tune an existing pre-trained model
• evaluate – Evaluate model performance on a dataset
• predict – Run inference and save predictions

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Training Models

gridfm_graphkit train --config path/to/config.yaml

Arguments

Argument	Type	Description	Default
--config	str	Required. Path to the training configuration YAML file.	None
--exp_name	str	Optional. MLflow experiment name.	timestamp
--run_name	str	Optional. MLflow run name.	run
--log_dir	str	Optional. MLflow logging directory.	mlruns
--data_path	str	Optional. Root dataset directory.	data

Examples

Standard Training:

gridfm_graphkit train --config examples/config/case30_ieee_base.yaml --data_path examples/data

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Fine-Tuning Models

gridfm_graphkit finetune --config path/to/config.yaml --model_path path/to/model.pth

Arguments

Argument	Type	Description	Default
--config	str	Required. Fine-tuning configuration file.	None
--model_path	str	Required. Path to a pre-trained model file.	None
--exp_name	str	MLflow experiment name.	timestamp
--run_name	str	MLflow run name.	run
--log_dir	str	MLflow logging directory.	mlruns
--data_path	str	Root dataset directory.	data

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Evaluating Models

gridfm_graphkit evaluate --config path/to/eval.yaml --model_path path/to/model.pth

Arguments

Argument	Type	Description	Default
--config	str	Required. Path to evaluation config.	None
--model_path	str	Path to the trained model file.	None
--exp_name	str	MLflow experiment name.	timestamp
--run_name	str	MLflow run name.	run
--log_dir	str	MLflow logging directory.	mlruns
--data_path	str	Dataset directory.	data

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Running Predictions

gridfm_graphkit predict --config path/to/config.yaml --model_path path/to/model.pth

Arguments

Argument	Type	Description	Default
--config	str	Required. Path to prediction config file.	None
--model_path	str	Path to the trained model file.	None
--exp_name	str	MLflow experiment name.	timestamp
--run_name	str	MLflow run name.	run
--log_dir	str	MLflow logging directory.	mlruns
--data_path	str	Dataset directory.	data
--output_path	str	Directory where predictions are saved.	data

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