Mark pytorch tests as p100 exempt

tests/test_fastai.py

@@ -3,6 +3,8 @@
 import fastai
 from fastai.tabular.all import *
 
+from common import p100_exempt
+
 
 class TestFastAI(unittest.TestCase):
   # Basic import
@@ -22,6 +24,7 @@ def test_torch_tensor(self):
 
     self.assertTrue(torch.all(a == b))
 
+  @p100_exempt
   def test_tabular(self):
     dls = TabularDataLoaders.from_csv(
         "/input/tests/data/train.csv",

tests/test_pytorch.py

@@ -4,7 +4,7 @@
 import torch.nn as tnn
 import torch.autograd as autograd
 
-from common import gpu_test
+from common import gpu_test, p100_exempt
 
 
 class TestPyTorch(unittest.TestCase):
@@ -16,6 +16,7 @@ def test_nn(self):
         linear_torch(data_torch)
 
     @gpu_test
+    @p100_exempt
     def test_linalg(self):
         A = torch.randn(3, 3).t().to('cuda')
         B = torch.randn(3).t().to('cuda')
@@ -24,6 +25,7 @@ def test_linalg(self):
         self.assertEqual(3, result.shape[0])
 
     @gpu_test
+    @p100_exempt
     def test_gpu_computation(self):
         cuda = torch.device('cuda')  
         a = torch.tensor([1., 2.], device=cuda)
@@ -33,6 +35,7 @@ def test_gpu_computation(self):
         self.assertEqual(torch.tensor([3.], device=cuda), result)
 
     @gpu_test
+    @p100_exempt
     def test_cuda_nn(self):
         # These throw if cuda is misconfigured
         tnn.GRUCell(10,10).cuda()

tests/test_pytorch_lightning.py

@@ -5,6 +5,8 @@
 import torch.nn.functional as F
 from torch.utils.data import DataLoader, TensorDataset
 
+from common import p100_exempt
+
 
 class LitDataModule(pl.LightningDataModule):
 
@@ -59,6 +61,7 @@ class TestPytorchLightning(unittest.TestCase):
     def test_version(self):
         self.assertIsNotNone(pl.__version__)
 
+    @p100_exempt
     def test_mnist(self):
         dm = LitDataModule()
         model = LitClassifier()