diff --git a/src/baskerville/trainer.py b/src/baskerville/trainer.py
index 75e538e..edeb0df 100644
--- a/src/baskerville/trainer.py
+++ b/src/baskerville/trainer.py
@@ -17,7 +17,8 @@
 
 import numpy as np
 import tensorflow as tf
-
+import tempfile
+from baskerville.helpers.gcs_utils import is_gcs_path, upload_folder_gcs
 from baskerville import metrics
 
 
@@ -119,6 +120,15 @@ def __init__(
         self.batch_size = self.train_data[0].batch_size
         self.compiled = False
 
+        # if log_dir is in gcs then create a local temp dir
+        if is_gcs_path(self.log_dir):
+            folder_name = "/".join(self.log_dir.split("/")[3:])
+            self.log_dir = tempfile.mkdtemp() + "/" + folder_name
+            self.gcs_log_dir = log_dir
+            self.gcs = True
+        else:
+            self.gcs = False
+
         # early stopping
         self.patience = self.params.get("patience", 20)
 
@@ -498,6 +508,10 @@ def eval_step1_distr(xd, yd):
                     print(" - valid_r2: %.4f" % valid_r2[di].result().numpy(), end="")
                     early_stop_stat = valid_r[di].result().numpy()
 
+                    # upload to gcs
+                    if self.gcs:
+                        upload_folder_gcs(train_log_dir, self.gcs_log_dir)
+                        upload_folder_gcs(valid_log_dir, self.gcs_log_dir)
                     # checkpoint
                     managers[di].save()
                     model.save(
@@ -697,6 +711,11 @@ def eval_step_distr(xd, yd):
                     end="",
                 )
 
+                # upload to gcs
+                if self.gcs:
+                    upload_folder_gcs(train_log_dir, self.gcs_log_dir)
+                    upload_folder_gcs(valid_log_dir, self.gcs_log_dir)
+
                 # checkpoint
                 manager.save()
                 seqnn_model.save("%s/model_check.h5" % self.out_dir)