diff --git a/train.py b/train.py
index 30d0145ef7..0940ef3aab 100644
--- a/train.py
+++ b/train.py
@@ -45,6 +45,8 @@
 wandb_run_name = 'gpt2' # 'run' + str(time.time())
 # data
 dataset = 'openwebtext'
+train_data_path = '' # explicit path to train dataset instead of dataset name convension
+val_data_path = '' # explicit path to valiidation dataset instead of dataset name convension
 gradient_accumulation_steps = 5 # used to simulate larger batch sizes
 batch_size = 12 # if gradient_accumulation_steps > 1, this is the micro-batch size
 block_size = 1024
@@ -74,7 +76,10 @@
 compile = True # use PyTorch 2.0 to compile the model to be faster
 # -----------------------------------------------------------------------------
 config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
-exec(open('configurator.py').read()) # overrides from command line or config file
+	
+dir_path = os.path.dirname(os.path.realpath(__file__))
+configurator = os.path.join(dir_path, 'configurator.py')
+exec(open(configurator).read()) # overrides from command line or config file
 config = {k: globals()[k] for k in config_keys} # will be useful for logging
 # -----------------------------------------------------------------------------
 
@@ -106,6 +111,13 @@
 
 # poor man's data loader
 data_dir = os.path.join('data', dataset)
+if not train_data_path:
+    train_data_path = os.path.join(data_dir, 'train.bin')
+if not val_data_path:
+    val_data_path = os.path.join(data_dir, 'val.bin')
+train_data = np.memmap(train_data_path, dtype=np.uint16, mode='r')
+val_data = np.memmap(val_data_path, dtype=np.uint16, mode='r')
+
 train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
 val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
 def get_batch(split):