GraphStormGNNTuning

GraphStorm/DGL R-GCN hyperparameter tuning — a HyperparameterTuner search over the GNN training estimator, run in a bring-your-own GraphStorm ECR container.

SageMaker step type	Tuning
Node type	internal (consumes upstream, produces downstream)
Container entry point	graphstorm_gnn_training.py
Build-time requirement	none (SAIS SDK — fatal on load if absent)
Interface file	steps/interfaces/graphstorm_gnn_tuning.step.yaml

Compute

Compute kind	byo_container
Image	BYO container — config.training_image_uri is passed VERBATIM to AppSpecification.ImageUri / AlgorithmSpecification.TrainingImage (no image_uris.retrieve; the framework deps live in the image’s Dockerfile)
Container entrypoint	['python3', '/opt/ml/input/data/code/train.py'] (ContainerEntrypoint bypass — the image runs its own entrypoint instead of the SageMaker toolkit)

Functionality

GraphStorm R-GCN hyperparameter tuning. Wraps the same GraphStorm training estimator the GraphStormGNNTraining step builds (byo_container, verbatim TrainingImage) in a SageMaker HyperparameterTuner, searching the configured search_space over the objective metric (regex- scraped from the container’s stdout via metric_definitions, as no SDK-managed metrics exist for a custom image). Emits N training trials and selects the best; downstream steps read the winner via get_top_model_s3_uri / properties.BestTrainingJob. The estimator, channels, and container entrypoint are identical to GraphStormGNNTraining — only the search wrapper is added.

Inputs (dependencies)

Input	Type	Required	Compatible producers
graph_data	processing_output	yes	GraphConstruction
training_config	hyperparameters	yes	GraphFeatureProcessing, GraphConstruction
code	processing_output	no	DummyDataLoading, ProcessingStep

Outputs

Output	Type
best_model	model_artifacts

Consumers (downstream steps)

No cataloged step lists this step as a compatible source (it may be a terminal/sink step, or consumed via a generic source name).

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