Merge pull request #2071 from recommenders-team/miguel/fix_tf

Fixing TF to < 2.16

recommenders/models/fastai/fastai_utils.py

@@ -62,18 +62,22 @@ def score(
 
     # score the pytorch model
     x = torch.column_stack((u, m))
+
     if torch.cuda.is_available():
         x = x.to("cuda")
         learner.model = learner.model.to("cuda")
+
     pred = learner.model.forward(x).detach().cpu().numpy()
     scores = pd.DataFrame(
         {user_col: test_df[user_col], item_col: test_df[item_col], prediction_col: pred}
     )
     scores = scores.sort_values([user_col, prediction_col], ascending=[True, False])
+
     if top_k is not None:
         top_scores = scores.groupby(user_col).head(top_k).reset_index(drop=True)
     else:
         top_scores = scores
+
     return top_scores
 
 

setup.py

@@ -53,7 +53,7 @@
     "gpu": [
         "fastai>=2.7.11,<3",
         "nvidia-ml-py>=11.525.84",
-        "tensorflow>=2.8.4,!=2.9.0.*,!=2.9.1,!=2.9.2,!=2.10.0.*,<=2.15.0",
+        "tensorflow>=2.8.4,!=2.9.0.*,!=2.9.1,!=2.9.2,!=2.10.0.*,<2.16",  # Fixed TF due to constant security problems and breaking changes #2073
         "tf-slim>=1.1.0",  # No python_requires in its setup.py
         "torch>=2.0.1,<3",
     ],

tests/functional/examples/test_notebooks_gpu.py

@@ -247,7 +247,9 @@ def test_wide_deep_functional(
             os.path.join("tests", "resources", "deeprec", "slirec"),
             10,
             400,
-            {"auc": 0.7183},  # Don't do logloss check as SLi-Rec uses ranking loss, not a point-wise loss
+            {
+                "auc": 0.7183
+            },  # Don't do logloss check as SLi-Rec uses ranking loss, not a point-wise loss
             42,
         )
     ],
@@ -278,7 +280,7 @@ def test_slirec_quickstart_functional(
     results = read_notebook(output_notebook)
 
     assert results["auc"] == pytest.approx(expected_values["auc"], rel=TOL, abs=ABS_TOL)
-    
+
 
 @pytest.mark.gpu
 @pytest.mark.notebooks
@@ -567,7 +569,7 @@ def test_dkn_quickstart_functional(notebooks, output_notebook, kernel_name):
         notebook_path,
         output_notebook,
         kernel_name=kernel_name,
-        parameters=dict(EPOCHS=5, BATCH_SIZE=500),
+        parameters=dict(EPOCHS=5, BATCH_SIZE=200),
     )
     results = read_notebook(output_notebook)