*PyGAS* is the practical realization of our *<ins>G</ins>NN<ins>A</ins>uto<ins>S</ins>cale* (GAS) framework, which scales arbitrary message-passing GNNs to large graphs.
*PyGAS* is the practical realization of our *<ins>G</ins>NN<ins>A</ins>uto<ins>S</ins>cale* (GAS) framework, which scales arbitrary message-passing GNNs to large graphs.
*PyGAS* allows for training and inference of GNNs with a constant GPU memory footprint, while it does not drop any input data in comparison to related scalability approaches.
GAS prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations, leading to constant GPU memory consumption in respect to input node size without dropping any data.
Our approach based on historical node embeddings is provably able to keep the existing expressiveness properties of the underlying message passing implementation.
As a result, our approach is provably able to maintain the expressive power of the original GNN.
*PyGAS* is implemented in [PyTorch](https://pytorch.org/) and utilizes the [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric)(PyG) library.
*PyGAS* is implemented in [PyTorch](https://pytorch.org/) and utilizes the [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric)(PyG) library.
It provides an easy-to-use interface to convert common and custom GNNs from PyG into its scalable variant:
It provides an easy-to-use interface to convert common and custom GNNs from PyG into its scalable variant:
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***`torch_geometric_autoscale/`** contains the source code of *PyGAS*
***`torch_geometric_autoscale/`** contains the source code of *PyGAS*
***`examples/`** contains examples to demonstrate how to apply GAS in practice
***`examples/`** contains examples to demonstrate how to apply GAS in practice
***`small_benchmark/`**contains the experiments to validate that GAS resembles full-batch performance
***`small_benchmark/`**includes the experiments to evaluate performance on *small-scale* graphs
***`large_benchmark/`**contains the experiments on large-scale graphs
***`large_benchmark/`**includes the experiments to evaluate performance on *large-scale* graphs
We use [**Hydra**](https://hydra.cc/) to manage hyperparameter configurations.
We use [**Hydra**](https://hydra.cc/) to manage hyperparameter configurations.
