Apply isort and black reformatting

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

nemo/collections/llm/tools/auto_configurator/autoconfig/search_config.py

@@ -42,7 +42,7 @@ def search_config(cfg: dict):
     :param Optional[str] hydra_args: hydra override arguments in string format.
     :return: None
     """
-    
+
     # Read config
     nodes = cfg.get("num_nodes")
     gpus_per_node = cfg.get("gpus_per_node")
@@ -57,7 +57,7 @@ def search_config(cfg: dict):
     seq_length = cfg.get("seq_length")
     log_dir = cfg.get("log_dir")
     custom_cfg = None
-    
+
     print(cfg)
     print(model_name)
     assert model_name in SUPPORTED_MODELS, f"model must be set to one of {SUPPORTED_MODELS}/<model_size>"

nemo/collections/llm/tools/auto_configurator/main_copy.py

@@ -25,17 +25,22 @@ def get_args():
     parser.add_argument('--gpus_per_node', required=False, default=8, type=int, help="Number of GPUs per node")
     parser.add_argument('--gpu_memory_gb', required=False, default=80, type=int, help="GPU memory size")
     parser.add_argument('--max_training_days', required=False, default=2, type=int, help="Path to data file")
-    parser.add_argument('--max_minutes_per_run', required=False, default=30, type=int, help="Max minutes per job on cluster")
+    parser.add_argument(
+        '--max_minutes_per_run', required=False, default=30, type=int, help="Max minutes per job on cluster"
+    )
     parser.add_argument('--model_type', required=True, type=str, help="Model size in billions")
     parser.add_argument('--model_size_in_b', required=True, type=int, help="Model size in billions")
     parser.add_argument('--vocab_size', required=False, default=32000, type=int, help="Size of tokenizer vocab")
     parser.add_argument('--tflops_per_gpu', required=False, default=140, type=int, help="Estimated tflops per GPU")
-    parser.add_argument('--num_tokens_in_b', required=False, default=300, type=int, help="Number of tokens in dataset in billions")
+    parser.add_argument(
+        '--num_tokens_in_b', required=False, default=300, type=int, help="Number of tokens in dataset in billions"
+    )
     parser.add_argument('--seq_length', required=False, default=2048, type=int, help="Model sequence length")
     parser.add_argument('--log_dir', required=True, type=str, help="Path to results directory")
 
     return parser.parse_args()
 
+
 def main():
     """
     Main function in the entire pipeline, it reads the config using hydra and calls search_config.