diff --git a/train.py b/train.py
index 711a0aa234b1..06d05df69956 100644
--- a/train.py
+++ b/train.py
@@ -78,14 +78,16 @@ def train(hyp, opt, device, tb_writer=None):
     is_coco = opt.data.endswith('coco.yaml')
 
     # Logging- Doing this before checking the dataset. Might update data_dict
+    loggers = {'wandb': None}  # loggers dict
     if rank in [-1, 0]:
         opt.hyp = hyp  # add hyperparameters
         run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
         wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
+        loggers['wandb'] = wandb_logger.wandb
         data_dict = wandb_logger.data_dict
         if wandb_logger.wandb:
             weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resuming
-    loggers = {'wandb': wandb_logger.wandb}  # loggers dict
+
     nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
@@ -417,6 +419,7 @@ def train(hyp, opt, device, tb_writer=None):
             fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
+            wandb_logger.end_epoch(best_result=best_fitness == fi)
 
 
 
@@ -447,7 +450,6 @@ def train(hyp, opt, device, tb_writer=None):
                         wandb_logger.log_model(
                             last.parent, opt, epoch, fi, best_model=best_fitness == fi)
                 del ckpt
-        wandb_logger.end_epoch(best_result=best_fitness == fi)
 
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training
@@ -487,10 +489,10 @@ def train(hyp, opt, device, tb_writer=None):
             wandb_logger.wandb.log_artifact(str(final), type='model',
                                             name='run_' + wandb_logger.wandb_run.id + '_model',
                                             aliases=['last', 'best', 'stripped'])
+        wandb_logger.finish_run()
     else:
         dist.destroy_process_group()
     torch.cuda.empty_cache()
-    wandb_logger.finish_run()
     return results
 
 
diff --git a/utils/wandb_logging/wandb_utils.py b/utils/wandb_logging/wandb_utils.py
index 3ed15ff58ed1..1504bfe1255c 100644
--- a/utils/wandb_logging/wandb_utils.py
+++ b/utils/wandb_logging/wandb_utils.py
@@ -16,9 +16,9 @@
 
 try:
     import wandb
+    from wandb import init, finish
 except ImportError:
     wandb = None
-    print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
 
 WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
 
@@ -72,6 +72,9 @@ def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
                 self.data_dict = self.setup_training(opt, data_dict)
             if self.job_type == 'Dataset Creation':
                 self.data_dict = self.check_and_upload_dataset(opt)
+        else:
+            print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
+
 
     def check_and_upload_dataset(self, opt):
         assert wandb, 'Install wandb to upload dataset'