Fix test suite failure when no tf wheel

doc/changelog.rst

@@ -32,16 +32,19 @@ A full list of changes and detailed notes can be found below:
 
 Detailed notes
 
+- Update full test suite to no longer require a tensorflow wheel to be available
+  at test time. (PR291_)
 - Deprecated launcher-specific orchestrators, constants, and ML utilities
 were removed. (PR289_)
 - Relax the coloredlogs version to be greater than 10.0 (PR288_)
 - Update the Github Actions runner image from `macos-10.15`` to `macos-12``. The
-former began deprecation in May 2022 and was finally removed in May 2023 (PR285_)
+former began deprecation in May 2022 and was finally removed in May 2023. (PR285_)
 - The Fortran tutorials had not been fully updated to show how to handle return/error
-codes. These have now all been updated (PR284_)
+codes. These have now all been updated. (PR284_)
 - Orchestrator and Colocated DB now accept a list of interfaces to bind to. The
 argument name is still `interface` for backward compatibility reasons. (PR281_)
 
+.. _PR291: https://github.com/CrayLabs/SmartSim/pull/291
 .. _PR289: https://github.com/CrayLabs/SmartSim/pull/289
 .. _PR288: https://github.com/CrayLabs/SmartSim/pull/288
 .. _PR285: https://github.com/CrayLabs/SmartSim/pull/285

tests/backends/test_dbmodel.py

@@ -44,6 +44,17 @@
     from tensorflow.keras.layers import Conv2D, Input
 except ImportError:
     should_run_tf = False
+else:
+
+    class Net(keras.Model):
+        def __init__(self):
+            super(Net, self).__init__(name="cnn")
+            self.conv = Conv2D(1, 3, 1)
+
+        def call(self, x):
+            y = self.conv(x)
+            return y
+
 
 should_run_tf &= "tensorflow" in installed_redisai_backends()
 
@@ -54,18 +65,35 @@
     import torch.nn.functional as F
 except ImportError:
     should_run_pt = False
+else:
+    # Simple MNIST in PyTorch
+    class PyTorchNet(nn.Module):
+        def __init__(self):
+            super(PyTorchNet, self).__init__()
+            self.conv1 = nn.Conv2d(1, 32, 3, 1)
+            self.conv2 = nn.Conv2d(32, 64, 3, 1)
+            self.dropout1 = nn.Dropout(0.25)
+            self.dropout2 = nn.Dropout(0.5)
+            self.fc1 = nn.Linear(9216, 128)
+            self.fc2 = nn.Linear(128, 10)
 
-should_run_pt &= "torch" in installed_redisai_backends()
-
+        def forward(self, x):
+            x = self.conv1(x)
+            x = F.relu(x)
+            x = self.conv2(x)
+            x = F.relu(x)
+            x = F.max_pool2d(x, 2)
+            x = self.dropout1(x)
+            x = torch.flatten(x, 1)
+            x = self.fc1(x)
+            x = F.relu(x)
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            output = F.log_softmax(x, dim=1)
+            return output
 
-class Net(keras.Model):
-    def __init__(self):
-        super(Net, self).__init__(name="cnn")
-        self.conv = Conv2D(1, 3, 1)
 
-    def call(self, x):
-        y = self.conv(x)
-        return y
+should_run_pt &= "torch" in installed_redisai_backends()
 
 
 def save_tf_cnn(path, file_name):
@@ -95,39 +123,6 @@ def create_tf_cnn():
     return serialize_model(model)
 
 
-# Simple MNIST in PyTorch
-try:
-
-    class PyTorchNet(nn.Module):
-        def __init__(self):
-            super(PyTorchNet, self).__init__()
-            self.conv1 = nn.Conv2d(1, 32, 3, 1)
-            self.conv2 = nn.Conv2d(32, 64, 3, 1)
-            self.dropout1 = nn.Dropout(0.25)
-            self.dropout2 = nn.Dropout(0.5)
-            self.fc1 = nn.Linear(9216, 128)
-            self.fc2 = nn.Linear(128, 10)
-
-        def forward(self, x):
-            x = self.conv1(x)
-            x = F.relu(x)
-            x = self.conv2(x)
-            x = F.relu(x)
-            x = F.max_pool2d(x, 2)
-            x = self.dropout1(x)
-            x = torch.flatten(x, 1)
-            x = self.fc1(x)
-            x = F.relu(x)
-            x = self.dropout2(x)
-            x = self.fc2(x)
-            output = F.log_softmax(x, dim=1)
-            return output
-
-
-except Exception:
-    should_run_pt = False
-
-
 def save_torch_cnn(path, file_name):
     n = PyTorchNet()
     example_forward_input = torch.rand(1, 1, 28, 28)