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 git@github.com: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]

gridfm-graphkit CLI

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
• predict – Evaluate an existing model
• finetune – Fine-tune a pre-trained model

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Training Models

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

Arguments

Argument	Type	Description	Default
--config	str	Required for standard training. Path to base config YAML.	None
--grid	str	Optional. Path to grid search YAML. Not supported with -c.	None
--exp	str	Optional. MLflow experiment name. Defaults to a timestamp.	None
--data_path	str	Optional. Root dataset directory.	data
-c	flag	Optional. Enable checkpoint mode.	False
--model_exp_id	str	Required if -c is used. MLflow experiment ID.	None
--model_run_id	str	Required if -c is used. MLflow run ID.	None

Examples

Standard Training:

gridfm_graphkit train --config config/train.yaml --exp "run1"

Grid Search Training:

gridfm_graphkit train --config config/train.yaml --grid config/grid.yaml

Training from Checkpoint:

gridfm_graphkit train -c --model_exp_id 123 --model_run_id abc

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Evaluating Models

gridfm_graphkit predict --model_path model.pth --config config/eval.yaml --eval_name run_eval

Arguments

Argument	Type	Description	Default
--model_path	str	Optional. Path to a model file.	None
--model_exp_id	str	Required if --model_path is not used. MLflow experiment ID.	None
--model_run_id	str	Required if --model_path is not used. MLflow run ID.	None
--model_name	str	Optional. Filename inside MLflow artifacts.	best_model
--config	str	Required. Path to evaluation config.	None
--eval_name	str	Required. Name of the evaluation run in MLflow.	None
--data_path	str	Optional. Path to dataset directory.	data

Examples

Evaluate a Logged MLflow Model:

gridfm_graphkit predict --config config/eval.yaml --eval_name run_eval --model_exp_id 1 --model_run_id abc

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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	str	Optional. MLflow experiment name.	None
--data_path	str	Optional. Root dataset directory.	data