logger

llmfoundry/command_utils/eval.py

@@ -157,7 +157,7 @@ def evaluate_model(
 
     if should_log_config:
         log.info('Evaluation config:')
-        log_config(logged_config)
+        log_config(trainer.logger, logged_config)
 
     log.info(f'Starting eval for {model_name}...')
     if torch.cuda.is_available():

llmfoundry/command_utils/train.py

@@ -573,7 +573,7 @@ def train(cfg: DictConfig) -> Trainer:
 
     if train_cfg.log_config:
         log.info('Logging config')
-        log_config(logged_cfg)
+        log_config(trainer.logger, logged_cfg)
     log_dataset_uri(logged_cfg)
     torch.cuda.empty_cache()
     gc.collect()

llmfoundry/utils/config_utils.py

@@ -23,6 +23,7 @@
 )
 
 import mlflow
+from composer.loggers import Logger
 from composer.utils import dist, parse_uri
 from omegaconf import MISSING, DictConfig, ListConfig, MissingMandatoryValue
 from omegaconf import OmegaConf as om
@@ -575,21 +576,14 @@ def process_init_device(model_cfg: Dict[str, Any], fsdp_config: Optional[Dict]):
     return init_context
 
 
-def log_config(cfg: Dict[str, Any]) -> None:
+def log_config(logger: Logger, cfg: Dict[str, Any]) -> None:
     """Logs the current config and updates the wandb and mlflow configs.
 
     This function can be called multiple times to update the wandb and MLflow
     config with different variables.
     """
     print(om.to_yaml(cfg))
-    loggers = cfg.get('loggers', None) or {}
-    if 'wandb' in loggers:
-        import wandb
-        if wandb.run:
-            wandb.config.update(cfg)
-
-    if 'mlflow' in loggers and mlflow.active_run():
-        mlflow.log_params(params=cfg)
+    logger.log_hyperparameters(cfg)
 
 
 def _parse_source_dataset(cfg: Dict[str, Any]) -> List[Tuple[str, str, str]]: