diff --git a/llmfoundry/command_utils/eval.py b/llmfoundry/command_utils/eval.py
index d628107db2..f622ca182d 100644
--- a/llmfoundry/command_utils/eval.py
+++ b/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():
diff --git a/llmfoundry/command_utils/train.py b/llmfoundry/command_utils/train.py
index b11924004d..8fac739544 100644
--- a/llmfoundry/command_utils/train.py
+++ b/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()
diff --git a/llmfoundry/utils/config_utils.py b/llmfoundry/utils/config_utils.py
index 623aadb5cc..7da60c5a02 100644
--- a/llmfoundry/utils/config_utils.py
+++ b/llmfoundry/utils/config_utils.py
@@ -19,6 +19,7 @@
 )
 
 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
@@ -571,21 +572,14 @@ 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]]: