fixed trainer api

pts/dataset/loader.py

@@ -1,4 +1,4 @@
-from typing import Callable, Iterable, Iterator, List, Optional
+from typing import Optional
 import itertools
 
 from torch.utils.data import IterableDataset

pts/model/estimator.py

@@ -80,7 +80,7 @@ class PyTorchEstimator(Estimator):
         training_data: Dataset,
         validation_data: Optional[Dataset] = None,
         num_workers: Optional[int] = None,
-        prefetch_factor: Optional[int] = 2,
+        prefetch_factor: int = 2,
         shuffle_buffer_length: Optional[int] = None,
         **kwargs,
     ) -> TrainOutput:
@@ -139,15 +139,15 @@ class PyTorchEstimator(Estimator):
         training_data: Dataset,
         validation_data: Optional[Dataset] = None,
         num_workers: Optional[int] = None,
-        num_prefetch: Optional[int] = None,
+        prefetch_factor: int = 2,
         shuffle_buffer_length: Optional[int] = None,
         **kwargs,
     ) -> PyTorchPredictor:
         return self.train_model(
             training_data,
             validation_data,
-            num_workers,
-            num_prefetch,
-            shuffle_buffer_length,
+            num_workers=num_workers,
+            prefetch_factor=prefetch_factor,
+            shuffle_buffer_length=shuffle_buffer_length,
             **kwargs,
         ).predictor

pts/trainer.py

@@ -17,8 +17,6 @@ class Trainer:
         epochs: int = 100,
         batch_size: int = 32,
         num_batches_per_epoch: int = 50,
-        num_workers: int = 4,
-        pin_memory: bool = False,
         learning_rate: float = 1e-3,
         weight_decay: float = 1e-6,
         device: Optional[Union[torch.device, str]] = None,
@@ -29,8 +27,6 @@ class Trainer:
         self.learning_rate = learning_rate
         self.weight_decay = weight_decay
         self.device = device
-        self.num_workers = num_workers
-        self.pin_memory = pin_memory
 
     def __call__(
         self,