Set initial epoch and set to 0

farm/train.py

@@ -120,8 +120,8 @@ def __init__(
         checkpoint_on_sigkill=False,
         checkpoint_every=None,
         checkpoint_root_dir=None,
-        from_epoch=1,
-        from_step=1,
+        from_epoch=0,
+        from_step=0,
     ):
         """
         :param optimizer: An optimizer object that determines the learning strategy to be used during training
@@ -270,6 +270,9 @@ def _load_checkpoint(cls, path, data_silo):
         trainer_checkpoint = torch.load(path / "trainer")
         trainer_state_dict = trainer_checkpoint["trainer_state_dict"]
 
+        # Just setting seeds is not sufficient to have deterministic results when resuming
+        # training from a checkpoint. Additionally, the previous states of Random Number
+        # Generators also need to be restored from the saved checkpoint.
         numpy_rng_state = trainer_checkpoint["numpy_rng_state"]
         numpy.random.set_state(numpy_rng_state)
         rng_state = trainer_checkpoint["rng_state"]