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.
task : :ref:`pipeline-task`
model : :ref:`pipeline-models` (built through given args.model)
optimizer : torch.optim.Optimizer
dataloader(if mini_batch_flag is True) :
- torch.utils.data.DataLoader
- dgl.dataloading
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
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