Snapchat/GiGL

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

35 stars 7 forks 35 watchers Scala Other

gnn graph graph-machine-learning graph-neural-network large-scale machine-learning pytorch-geometric

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3 Open Issues Need Help Last updated: Sep 12, 2025

Open Issues Need Help

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`bootstrap_resource_config.py` is defaulting to bash shell even when executing script inside zshrc about 2 months ago

bug good first issue

Snapchat/GiGL

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

Scala

#gnn#graph#graph-machine-learning#graph-neural-network#large-scale#machine-learning#pytorch-geometric

Add Types to jupyter_magics.py 3 months ago

AI Summary: Add type hints to the `jupyter_magics.py` file to improve code readability and maintainability, removing the existing type ignore.

Complexity: 3/5

good first issue tech debt

Snapchat/GiGL

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

Scala

#gnn#graph#graph-machine-learning#graph-neural-network#large-scale#machine-learning#pytorch-geometric

Deprecate typing imports off Dict, List, etc. 3 months ago

AI Summary: The task is to refactor the GiGL Python codebase to replace imports from the `typing` module (e.g., `List`, `Dict`) with the built-in collection type syntax (e.g., `list[str]`, `dict[str, int]`) for type annotations, leveraging Python 3.9+ features. This involves finding all instances of these imports and updating the type hints accordingly.

Complexity: 3/5

good first issue

Snapchat/GiGL

Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks

Scala

#gnn#graph#graph-machine-learning#graph-neural-network#large-scale#machine-learning#pytorch-geometric