TrainerFlow

A trainerflow is an abstraction of a predesigned workflow that trains and evaluate a model on a given dataset for a specific use case. It must contain a unique training mechanism involving loss calculation and a specific sampler(sample something used in loss calculation) .

Once we select the model and the task, the func get_trainerflow will help us select the trainerflow. So the customized trainerflow needed be added in this func.

Included Object:

• task : Task

• model : Model (built through given args.model)

• optimizer : torch.optim.Optimizer

• dataloader(if mini_batch_flag is True) :

  • torch.utils.data.DataLoader

  • dgl.dataloading

Method:

• train()

  • decorated with @abstractmethod, so it must be overridden.

• _full_train_setp()

  • train with a full_batch graph

• _mini_train_step()

  • train with a mini_batch seed nodes graph

• _test_step()

  • evaluate in training/validation/testing

Supported trainerflow

• Node classification flow

  • Supported Model: HAN/MAGNN/GTN…

  • The task: node classification

    • The task.dataset must include the splited[train/valid/test.] mask.

  • The sampler in this flow is supported by dgl.dataloading.

  • The flow is the most common in the GNNs cause most GNNs model are involved in the task semi-supervised node classification. Here the task is to classify the nodes of HIN(Heterogeneous Information Network).

  • Note: we will set the args.out_dim with num_classes if they are not equivalent.

• Dist Mult

  • The same with entity classification except that it is used for link prediction.

  • Supported Model: RGCN/CompGCN/RSHN

  • Supported Task: link prediction

• HetGNN trainerflow

• NSHE trainerflow