diff --git a/src/ms_pred/dag_pred/iceberg_elucidation.py b/src/ms_pred/dag_pred/iceberg_elucidation.py
index 42130fb..d42f6d3 100644
--- a/src/ms_pred/dag_pred/iceberg_elucidation.py
+++ b/src/ms_pred/dag_pred/iceberg_elucidation.py
@@ -273,7 +273,10 @@ def iceberg_prediction(
         df.to_csv(save_dir / f'cands_df_{exp_name}.tsv', sep='\t', index=False)
 
         # run iceberg to generate in-silico spectrum
-        cmd = (f'''{python_path} src/ms_pred/dag_pred/predict_smis.py \\
+        # Resolve predict_smis.py relative to this package, not the current working
+        # directory, so it also works when ms_pred is pip-installed (no src/ checkout).
+        predict_script = Path(__file__).resolve().parent / "predict_smis.py"
+        cmd = (f'''{python_path} {predict_script} \\
                --batch-size {batch_size} \\
                --num-workers {num_workers} \\
                --dataset-labels {save_dir / f"cands_df_{exp_name}.tsv"} \\
diff --git a/src/ms_pred/dag_pred/predict_smis.py b/src/ms_pred/dag_pred/predict_smis.py
index 57965cf..9153d82 100644
--- a/src/ms_pred/dag_pred/predict_smis.py
+++ b/src/ms_pred/dag_pred/predict_smis.py
@@ -78,7 +78,7 @@ def predict():
     save_dir = Path(kwargs["save_dir"])
     debug = kwargs["debug"]
     common.setup_logger(save_dir, log_name="joint_pred.log", debug=debug)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)
@@ -195,7 +195,8 @@ def producer_func(batch):
             else:
                 device = "cpu"
             model.to(device)
-            torch.cuda.set_device(gpu_id) # avoids error in pe_embedding under multithreading. 
+            if gpu and avail_gpu_num > 0:
+                torch.cuda.set_device(gpu_id)  # avoids error in pe_embedding under multithreading
 
             # for batch in batched_entries:
             smis, spec_names, colli_eng_vals, adducts, instruments, precursor_mzs, h5_names = list(zip(*batch))