question: "Error: 'MyDataModule' object is not iterable" when calling trainer.predict(model, data)
context: Hi!
  I've trained a model successfully. I now want to have a look at the model predictions. I've overridden the predict_dataloader method in my DataModule:
  def predict_dataloader(self):
          pred_loader = torch.utils.data.DataLoader(
              self.val_ds, batch_size=1, num_workers=4)
          return pred_loader
  
  Then, I initialize the model using my checkpoint and call the predict method:
  checkpoint_dir = os.path.join(root_dir, "logs2/epoch=199-val_loss=0.26-val_dice=1.67.ckpt")
  
  # initialize the data module 
  data = KeriDataModule(data_dir=data_dir, pix_dim=(0.6, 0.6, 0.937))
  
  # initialize the LightningModule (from checkpoint)
  net = Net.load_from_checkpoint(checkpoint_path=checkpoint_dir)
  
  # initialize Lightning's trainer
  trainer = pl.Trainer(gpus=[0])
  
  results = trainer.predict(net, data)
  
  Unfortunately, I get the following error
  Predicting: 0it [43:22, ?it/s]
  
  GPU available: True, used: True
  TPU available: False, using: 0 TPU cores
  LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
  
  
  Predicting: 0it [00:00, ?it/s]
  
  ---------------------------------------------------------------------------
  TypeError                                 Traceback (most recent call last)
  <ipython-input-12-4bd0b31315bd> in <module>
       10 trainer = pl.Trainer(gpus=[0])
       11 
  ---> 12 results = trainer.predict(net, data)
       13 print(results)
       14 
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in predict(self, model, dataloaders, datamodule, return_predictions)
      629         self.data_connector.attach_data(model, predict_dataloaders=dataloaders, datamodule=datamodule)
      630 
  --> 631         results = self._run(model)
      632 
      633         assert self.state.stopped
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
      754 
      755         # dispatch `start_training` or `start_evaluating` or `start_predicting`
  --> 756         self.dispatch()
      757 
      758         # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
      793             self.accelerator.start_evaluating(self)
      794         elif self.predicting:
  --> 795             self.accelerator.start_predicting(self)
      796         else:
      797             self.accelerator.start_training(self)
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in start_predicting(self, trainer)
      100 
      101     def start_predicting(self, trainer: 'pl.Trainer') -> None:
  --> 102         self.training_type_plugin.start_predicting(trainer)
      103 
      104     def pre_dispatch(self, trainer: 'pl.Trainer') -> None:
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_predicting(self, trainer)
      150     def start_predicting(self, trainer: 'pl.Trainer') -> None:
      151         # double dispatch to initiate the predicting loop
  --> 152         self._results = trainer.run_stage()
      153 
      154     def training_step(self, *args, **kwargs):
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
      804             return self.run_evaluate()
      805         if self.predicting:
  --> 806             return self.run_predict()
      807         return self.run_train()
      808 
  
  ~/miniconda3/envs/monai-lightning-latest/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_predict(self)
     1071             dataloader = self.accelerator.process_dataloader(dataloader)
     1072             dl_max_batches = self.predict_loop.max_batches[dataloader_idx]
  -> 1073             for batch_idx, batch in enumerate(dataloader):
     1074                 if batch is None:
     1075                     continue
  
  TypeError: 'KeriDataModule' object is not iterable
  
  I don't understand what I messed up ^^'. Any help / tip would be greatly appreciated :)
answer: Sorry! We did not add support for trainer.predict(model, datamodule). We'll do it asap!
  You need to do trainer.predict(model, datamodule=datamodule)