Pass omegaconf object to trainer in nlp_checkpoint_port.py

Signed-off-by: Abhishree <[email protected]>

nemo/core/config/pytorch_lightning.py

@@ -54,7 +54,7 @@ class TrainerConfig:
     limit_test_batches: Any = 1.0
     val_check_interval: Any = 1.0
     log_every_n_steps: int = 50
-    accelerator: Optional[str] = None
+    accelerator: Optional[str] = 'auto'
     sync_batchnorm: bool = False
     precision: Any = 32
     num_sanity_val_steps: int = 2
@@ -68,8 +68,8 @@ class TrainerConfig:
     gradient_clip_algorithm: str = 'norm'
     max_time: Optional[Any] = None  # can be one of Union[str, timedelta, Dict[str, int], None]
     reload_dataloaders_every_n_epochs: int = 0
-    devices: Any = None
-    strategy: Any = None
+    devices: Any = 'auto'
+    strategy: Any = 'auto'
     enable_checkpointing: bool = False
     enable_model_summary: bool = True
     inference_mode: bool = True

scripts/nemo_legacy_import/nlp_checkpoint_port.py

@@ -88,7 +88,8 @@ def nemo_convert(argv):
         logger=False,
         enable_checkpointing=False,
     )
-    trainer = pl.Trainer(cfg_trainer)
+    cfg_trainer = OmegaConf.to_container(OmegaConf.create(cfg_trainer))
+    trainer = pl.Trainer(**cfg_trainer)
 
     logging.info("Restoring NeMo model from '{}'".format(nemo_in))
     try: