Use tmp_path_factory for saving results to avoid repeated run issues

tests/test_fit_distributed.py

@@ -116,16 +116,22 @@ def test_fit_model_distributed_batch_size_handling(sample_data, tmp_path):
         assert "Training completed successfully" in stdout
 
 
-def test_fit_model_distributed_reproducibility(sample_data, tmp_path):
+def test_fit_model_distributed_reproducibility(sample_data, tmp_path_factory):
     """Test that training is reproducible with same seed but different with different seeds."""
     X, _, _ = sample_data
-    data_path = tmp_path / "data.pt"
-    torch.save(X, data_path)
 
-    save_path_1 = tmp_path / "model_1.pt"
+    # Create completely separate base directories for each run
+    base_dir_1 = tmp_path_factory.mktemp("run1")
+    base_dir_2 = tmp_path_factory.mktemp("run2")
+    base_dir_3 = tmp_path_factory.mktemp("run3")
+
+    # First run with seed 42
+    data_path_1 = base_dir_1 / "data.pt"
+    save_path_1 = base_dir_1 / "model.pt"
+    torch.save(X, data_path_1)
     args = [
         "--data_path",
-        str(data_path),
+        str(data_path_1),
         "--num_topics",
         str(N_TOPICS),
         "--num_epochs",
@@ -137,10 +143,13 @@ def test_fit_model_distributed_reproducibility(sample_data, tmp_path):
     ]
     run_distributed_training(args)
 
-    save_path_2 = tmp_path / "model_2.pt"
+    # Second run with same seed
+    data_path_2 = base_dir_2 / "data.pt"
+    save_path_2 = base_dir_2 / "model.pt"
+    torch.save(X, data_path_2)
     args = [
         "--data_path",
-        str(data_path),
+        str(data_path_2),
         "--num_topics",
         str(N_TOPICS),
         "--num_epochs",
@@ -152,10 +161,13 @@ def test_fit_model_distributed_reproducibility(sample_data, tmp_path):
     ]
     run_distributed_training(args)
 
-    save_path_3 = tmp_path / "model_3.pt"
+    # Third run with different seed
+    data_path_3 = base_dir_3 / "data.pt"
+    save_path_3 = base_dir_3 / "model.pt"
+    torch.save(X, data_path_3)
     args = [
         "--data_path",
-        str(data_path),
+        str(data_path_3),
         "--num_topics",
         str(N_TOPICS),
         "--num_epochs",
@@ -168,9 +180,9 @@ def test_fit_model_distributed_reproducibility(sample_data, tmp_path):
     run_distributed_training(args)
 
     # Load losses from all runs
-    losses_1 = torch.load(tmp_path / "losses_1.pt", weights_only=True)
-    losses_2 = torch.load(tmp_path / "losses_2.pt", weights_only=True)
-    losses_3 = torch.load(tmp_path / "losses_3.pt", weights_only=True)
+    losses_1 = torch.load(base_dir_1 / "losses.pt", weights_only=True)
+    losses_2 = torch.load(base_dir_2 / "losses.pt", weights_only=True)
+    losses_3 = torch.load(base_dir_3 / "losses.pt", weights_only=True)
 
     # Same seed should give identical results
     assert torch.allclose(torch.tensor(losses_1), torch.tensor(losses_2))