Efficient GPU Training

To train deep detectors efficiently, we usually use CUDA to accelerate the detector training on GPU. PyGOD provides gpu parameter for DeepDetector. During initialization, we can set gpu to the index of the GPU that is available. By default, gpu=-1, which means train the detector on CPU. Here is an example of initialize DOMINANT with the first GPU (index of 0):


However, training deep detectors on large-scale graphs can be memory-intensive, especially on the detectors relying on adjacency matrix reconstruction. At this time, full batch training may result in out-of-memory (OOM) error. As such, we divide the large graph into minibatches, and train the detector on each batch. PyGOD provides batch_size parameter for DeepDetector, where users are able to adjust the size of each batch for various GPU memory. We recommend users setting batch_size to largest value that will not cause OOM. For instance, we would like to train DOMINANT with the batches of 64 nodes:

DOMINANT(gpu=0, batch_size=64)

Unlike other data modalities, the output of each node in graphs rely on its neighbors. In PyGOD implementation, we adopt the data loader torch_geometric.loader.NeighborLoader in PyG to load both the center nodes and the neighbor nodes for minibatches. But the computation on neighbor nodes will lead to significant overhead and reduce the efficiency in the detector training. Thus, we neighbor sampling is crucial to reduce the overhead. PyGOD provides num_neigh parameter for DeepDetector. We can specify how many neighbors are sampled at each layer of the detector. The default value of num_neigh is -1, indicating sample all neighbors of the center node. If we want to sample 5 neighbors at each layer, we can initialize DOMINANT like:

DOMINANT(gpu=0, batch_size=64, num_neigh=5)

We can also sample different number of neighbors at each layer by setting num_neigh as a list, but the length of the list has to match with the number of layers num_layers:

DOMINANT(gpu=0, batch_size=64, num_layers=2, num_neigh=[5, 3])

To learn more, read PyG’s tutorial on Scaling GNNs via Neighbor Sampling.