Move data_creator to train and validation (intel-analytics#2602)

* move data_creator to train and validation

* address comments

* update doc

python/orca/example/learn/horovod/pytorch_estimator.py

@@ -60,24 +60,26 @@ def scheduler_creator(optimizer, config):
     return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)
 
 
-def data_creator(config):
-    """Returns training dataloader, validation dataloader."""
+def train_data_creator(config):
     train_dataset = LinearDataset(2, 5, size=config.get("data_size", 1000))
-    val_dataset = LinearDataset(2, 5, size=config.get("val_size", 400))
     train_loader = torch.utils.data.DataLoader(
         train_dataset,
         batch_size=config.get("batch_size", 32),
     )
+    return train_loader
+
+
+def validation_data_creator(config):
+    val_dataset = LinearDataset(2, 5, size=config.get("val_size", 400))
     validation_loader = torch.utils.data.DataLoader(
         val_dataset,
         batch_size=config.get("batch_size", 32))
-    return train_loader, validation_loader
+    return validation_loader
 
 
 def train_example():
     trainer1 = PyTorchHorovodEstimator(
         model_creator=model_creator,
-        data_creator=data_creator,
         optimizer_creator=optimizer_creator,
         loss_creator=nn.MSELoss,
         scheduler_creator=scheduler_creator,
@@ -89,8 +91,10 @@ def train_example():
 
     # train 5 epochs
     for i in range(5):
-        stats = trainer1.train()
-        print(stats)
+        stats = trainer1.train(train_data_creator)
+        print("train stats: {}".format(stats))
+        val_stats = trainer1.validate(validation_data_creator)
+        print("validation stats: {}".format(val_stats))
 
 
 parser = argparse.ArgumentParser()