PytorchDataset: remove internal tqdm bar (#692)

examples/get_started/torch-loader.py

@@ -13,6 +13,7 @@
 from torch import nn, optim
 from torch.utils.data import DataLoader
 from torchvision.transforms import v2
+from tqdm import tqdm
 
 from datachain import C, DataChain
 from datachain.torch import label_to_int
@@ -23,7 +24,7 @@
 # Define transformation for data preprocessing
 transform = v2.Compose(
     [
-        v2.ToTensor(),
+        v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)]),
         v2.Resize((64, 64)),
         v2.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
     ]
@@ -77,18 +78,18 @@ def forward(self, x):
 
     # Train the model
     for epoch in range(int(NUM_EPOCHS)):
-        for i, data in enumerate(train_loader):
-            inputs, labels = data
-            optimizer.zero_grad()
-
-            # Forward pass
-            outputs = model(inputs)
-            loss = criterion(outputs, labels)
-
-            # Backward pass and optimize
-            loss.backward()
-            optimizer.step()
-
-            print(f"[{epoch + 1}, {i + 1:5d}] loss: {loss.item():.3f}")
-
-    print("Finished Training")
+        with tqdm(
+            train_loader, desc=f"epoch {epoch + 1}/{NUM_EPOCHS}", unit="batch"
+        ) as loader:
+            for data in loader:
+                inputs, labels = data
+                optimizer.zero_grad()
+
+                # Forward pass
+                outputs = model(inputs)
+                loss = criterion(outputs, labels)
+
+                # Backward pass and optimize
+                loss.backward()
+                optimizer.step()
+                loader.set_postfix(loss=loss.item())

src/datachain/lib/pytorch.py

@@ -7,7 +7,6 @@
 from torch.distributed import get_rank, get_world_size
 from torch.utils.data import IterableDataset, get_worker_info
 from torchvision.transforms import v2
-from tqdm import tqdm
 
 from datachain import Session
 from datachain.asyn import AsyncMapper
@@ -112,10 +111,7 @@ def __iter__(self) -> Iterator[Any]:
             from datachain.lib.udf import _prefetch_input
 
             rows = AsyncMapper(_prefetch_input, rows, workers=self.prefetch).iterate()
-
-        desc = f"Parsed PyTorch dataset for rank={total_rank} worker"
-        with tqdm(rows, desc=desc, unit=" rows", position=total_rank) as rows_it:
-            yield from map(self._process_row, rows_it)
+        yield from map(self._process_row, rows)
 
     def _process_row(self, row_features):
         row = []