[Roadmap] GraphGym via PyTorch Lightning and Hydra 🚀

🚀 The feature, motivation and pitch

The overall goal of this roadmap is to ensure a tighter connection between PyG core and the GraphGym configuration manager. Furthermore, an additional goal is to not re-invent the wheel in GraphGym and make use of popular open-source frameworks whenever applicable, e.g., for configuration managament, training, logging, and autoML.

As such, this roadmap structures itself into different components such as general improvements (e.g., tighter connection between PyG and GraphGym), PyTorch Lightning integration, and Hydra integration as our configuration tool.

General Roadmap

• Add register functionality to models in PyG core
• Remove any layer/model definition of GraphGym and move it to PyG core
• Expose a graphgym bash script in a bin/ folder - GraphGym usage should not require manually cloning of PyG
• Better and more user-friendly documentation
• Adding HeteroData support
• Adding pooling layers
• ...

PyTorch Lightning Integration

GraphGym training experience can be improved for scalability, mixed precision support, logging and checkpoints with PyTorch Lightning integration.

• Integrate a LightningModule into GraphGym
• Update train method with PL Trainer and the LightningModule implementations
• Refactor load_ckpt and save_ckpt with PL checkpoint save and load method
• Integrate LightningDataset, LightningNodeData and LightningLinkData modules
• ...

Hydra Integration

Users of PyG should be able to write GraphGym configurations by being able to make full use of PyG functionality. In particular, we want to allow access to any dataset, any data transformation pipeline, and any GNN layer/model. For this, we need to follow a structured/composable configuration, e.g., as introduced here

defaults:
  - dataset: KarateClub
  - transform@dataset.transform:
      - NormalizeFeatures
      - AddSelfLoops
  - model: GCN
  - optimizer: Adam
  - lr_scheduler: ReduceLROnPlateau
  - _self_

model:
  in_channels: 34
  out_channels: 4
  hidden_channels: 16
  num_layers: 2

• Use variable interpolation, e.g., model.in_channels = ${dataset.num_features} and model.out_channels = ${dataset.num_classes}
• ...

Weights & Biases Integration (TBD)

• ...

AutoML (TBD)

• ...

cc @pyg-team/biotax-team