RHOAIENG-3771 - Reduce execution time of E2E tests

By reducing number of epochs and number of training
samples in each epoch it was possible to reduce
test execution time from more than 10 minutes to
less than 2 minutes.

Author: jiripetrlik

test/e2e/mnist.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.callbacks.progress import TQDMProgressBar
 from torch import nn
 from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader, random_split, RandomSampler
 from torchmetrics import Accuracy
 from torchvision import transforms
 from torchvision.datasets import MNIST
@@ -158,7 +158,7 @@ def setup(self, stage=None):
             )
 
     def train_dataloader(self):
-        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)
+        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE, sampler=RandomSampler(self.mnist_train, num_samples=1000))
 
     def val_dataloader(self):
         return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)
@@ -178,10 +178,11 @@ def test_dataloader(self):
 trainer = Trainer(
     accelerator="auto",
     # devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
-    max_epochs=5,
+    max_epochs=3,
     callbacks=[TQDMProgressBar(refresh_rate=20)],
     num_nodes=int(os.environ.get("GROUP_WORLD_SIZE", 1)),
     devices=int(os.environ.get("LOCAL_WORLD_SIZE", 1)),
+    replace_sampler_ddp=False,
     strategy="ddp",
 )
 

test/odh/resources/mnist.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.callbacks.progress import TQDMProgressBar
 from torch import nn
 from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader, random_split, RandomSampler
 from torchmetrics import Accuracy
 from torchvision import transforms
 from torchvision.datasets import MNIST
@@ -158,7 +158,7 @@ def setup(self, stage=None):
             )
 
     def train_dataloader(self):
-        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)
+        return DataLoader(self.mnist_train, batch_size=BATCH_SIZE, sampler=RandomSampler(self.mnist_train, num_samples=1000))
 
     def val_dataloader(self):
         return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)
@@ -178,10 +178,11 @@ def test_dataloader(self):
 trainer = Trainer(
     accelerator="auto",
     # devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs
-    max_epochs=2,
+    max_epochs=3,
     callbacks=[TQDMProgressBar(refresh_rate=20)],
     num_nodes=int(os.environ.get("GROUP_WORLD_SIZE", 1)),
     devices=int(os.environ.get("LOCAL_WORLD_SIZE", 1)),
+    replace_sampler_ddp=False,
     strategy="ddp",
 )