Allow user to set a training seed via env vars for repeat result testing

run.py

@@ -1,11 +1,16 @@
 import os
 import sys
-from typing import Union, OrderedDict
 from dotenv import load_dotenv
 # Load the .env file if it exists
 load_dotenv()
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = os.getenv("HF_HUB_ENABLE_HF_TRANSFER", "1")
 os.environ["NO_ALBUMENTATIONS_UPDATE"] = "1"
+seed = None
+if "SEED" in os.environ:
+    try:
+        seed = int(os.environ["SEED"])
+    except ValueError:
+        print(f"Invalid SEED value: {os.environ['SEED']}. SEED must be an integer.")
 
 sys.path.insert(0, os.getcwd())
 # must come before ANY torch or fastai imports
@@ -14,11 +19,21 @@ sys.path.insert(0, os.getcwd())
 # turn off diffusers telemetry until I can figure out how to make it opt-in
 os.environ['DISABLE_TELEMETRY'] = 'YES'
 
+# set torch to trace mode
+import torch
+    
 # check if we have DEBUG_TOOLKIT in env
 if os.environ.get("DEBUG_TOOLKIT", "0") == "1":
-    # set torch to trace mode
-    import torch
     torch.autograd.set_detect_anomaly(True)
+
+if seed is not None:
+    import random
+    import numpy as np
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
 import argparse
 from toolkit.job import get_job
 from toolkit.accelerator import get_accelerator