fix test_main_runs for esm2 and geneformer

scripts/protein/esm2/esm2_pretrain.py

@@ -243,11 +243,11 @@ def main(
     # Configure our custom Checkpointer
     checkpoint_callback = nl_callbacks.ModelCheckpoint(
         save_last=save_last_checkpoint,
-        monitor=metric_to_monitor_for_checkpoints,  # "val_loss",
+        monitor=metric_to_monitor_for_checkpoints,
         save_top_k=save_top_k,
         every_n_train_steps=val_check_interval,
         always_save_context=True,  # Enables the .nemo file-like checkpointing where all IOMixins are under SerDe
-        filename="{epoch}-{step}-{val_loss:.2f}",
+        filename="{epoch}-{step}-{" + metric_to_monitor_for_checkpoints + ":.2f}",
     )
 
     # Setup the logger and train the model

scripts/protein/esm2/test_esm2_pretrain.py

@@ -30,7 +30,6 @@
 from bionemo.testing import megatron_parallel_state_utils
 
 
-@pytest.mark.skip("duplicate unittest")
 @pytest.fixture
 def dummy_protein_dataset(tmp_path):
     """Create a mock protein dataset."""
@@ -62,7 +61,6 @@ def dummy_protein_dataset(tmp_path):
     return db_file
 
 
-@pytest.mark.skip("duplicate unittest")
 @pytest.fixture
 def dummy_parquet_train_val_inputs(tmp_path):
     """Create a mock protein train and val cluster parquet."""
@@ -104,7 +102,7 @@ def test_main_runs(monkeypatch, tmpdir, dummy_protein_dataset, dummy_parquet_tra
             result_dir=result_dir,
             wandb_project=None,
             wandb_offline=True,
-            num_steps=55,
+            num_steps=10,
             warmup_steps=5,
             limit_val_batches=1,
             val_check_interval=1,

sub-packages/bionemo-example_model/tests/bionemo/example_model/lightning/test_lightning_basic.py

@@ -56,7 +56,7 @@ def _train_model_get_ckpt(
         every_n_train_steps=5,
         always_save_context=True,  # Enables the .nemo file-like checkpointing where all IOMixins are under SerDe
         # async_save=False,  # Tries to save asynchronously, previously led to race conditions.
-        filename="{epoch}-{step}-{val_loss:.2f}"
+        filename="{epoch}-{step}-{val_loss:.2f}",
     )
     save_dir = root_dir / name
     tb_logger = TensorBoardLogger(save_dir=save_dir, name=name)

sub-packages/bionemo-geneformer/src/bionemo/geneformer/scripts/train_geneformer.py

@@ -311,11 +311,11 @@ def main(
     # Configure our custom Checkpointer
     checkpoint_callback = nl_callbacks.ModelCheckpoint(
         save_last=save_last_checkpoint,
-        monitor=metric_to_monitor_for_checkpoints,  # "val_loss",
+        monitor=metric_to_monitor_for_checkpoints,
         save_top_k=save_top_k,
         every_n_train_steps=val_check_interval,
         always_save_context=True,  # Enables the .nemo file-like checkpointing where all IOMixins are under SerDe
-        # filename="{epoch}-{step}-{val_loss:.2f}",
+        filename="{epoch}-{step}-{" + metric_to_monitor_for_checkpoints + ":.2f}",
     )
 
     # Setup the logger and train the model

sub-packages/bionemo-geneformer/tests/bionemo/geneformer/scripts/test_train_geneformer.py

@@ -37,7 +37,6 @@ def test_bionemo2_rootdir():
     assert data_path.is_dir(), "Test data directory is supposed to be a directory."
 
 
-@pytest.mark.skip("duplicate unittest")
 def test_main_runs(tmpdir):
     result_dir = Path(tmpdir.mkdir("results"))