diff --git a/.github/workflows/e2e_testing.yml b/.github/workflows/e2e_testing.yml
index 0a6c083a..87952292 100644
--- a/.github/workflows/e2e_testing.yml
+++ b/.github/workflows/e2e_testing.yml
@@ -24,6 +24,7 @@ jobs:
       NUMBA_BOUNDSCHECK: 1
       NUMBA_DEVELOPER_MODE: 1
       NUMBA_FULL_TRACEBACKS: 1
+      TQDM_MININTERVAL: 10  # avoid lots of tqdm outputs
     steps:
       - uses: actions/checkout@v4
       - name: Conda info
diff --git a/alphadia/fdrexperimental.py b/alphadia/fdrexperimental.py
index 901258eb..c8d4cc20 100644
--- a/alphadia/fdrexperimental.py
+++ b/alphadia/fdrexperimental.py
@@ -368,7 +368,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
         y_test = torch.from_numpy(y_test).float().to(self.device)
 
         batch_count = 0
-        for epoch in tqdm(range(self.epochs), mininterval=5):
+        for epoch in tqdm(range(self.epochs)):
             train_loss_sum = 0.0
             test_loss_sum = 0.0
 
@@ -1122,7 +1122,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
 
         batch_count = 0
 
-        for epoch in tqdm(range(self.epochs), mininterval=5):
+        for epoch in tqdm(range(self.epochs)):
             # shuffle batches
             order = np.random.permutation(num_batches)
             batch_start_list = batch_start_list[order]
diff --git a/alphadia/outputaccumulator.py b/alphadia/outputaccumulator.py
index 63010c63..4d0ef135 100644
--- a/alphadia/outputaccumulator.py
+++ b/alphadia/outputaccumulator.py
@@ -553,8 +553,7 @@ def ms2_quality_control(
                 precursor_df["frag_stop_idx"],
                 strict=True,
             )
-        ),
-        mininterval=5,
+        )
     ):
         # get XIC correlations and intensities for the precursor
         fragment_correlation_view = fragment_correlation_df.iloc[start_idx:stop_idx]
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 343be3a2..69eaeed7 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -287,7 +287,7 @@ def _order_intensities(
             The reordered fragment intensity dataframe.
         """
         reordered = unordered_frag_df.copy()
-        for i in tqdm(range(len(reordered_precursor_df)), mininterval=5):
+        for i in tqdm(range(len(reordered_precursor_df))):
             new_start_idx = reordered_precursor_df.iloc[i]["frag_start_idx"]
             new_end_idx = reordered_precursor_df.iloc[i]["frag_stop_idx"]