diff --git a/HelloDeepSpeed/README.md b/HelloDeepSpeed/README.md
index 9bc7adac0..bbc8761de 100644
--- a/HelloDeepSpeed/README.md
+++ b/HelloDeepSpeed/README.md
@@ -31,7 +31,7 @@ One of the most important parts of training ML models is for the experiments to
 
 * Save all the hyperparameters associated with the experiment (be it taken from a config or parsed from the command line)
 
-* Seed your random generators
+* Seed your random generators. Some useful tips can be found [here](https://pytorch.org/docs/stable/notes/randomness.html?highlight=reproducibility).
 
 * Specify all the packages and their versions. This can be a `requirements.txt` file, a conda `env.yaml` file or a `pyproject.toml` file. If you want complete reproducibility, you can also include a `Dockerfile` to specify the environment to run the experiment in.
 
diff --git a/HelloDeepSpeed/train_bert.py b/HelloDeepSpeed/train_bert.py
index 14d61f00c..45e536298 100644
--- a/HelloDeepSpeed/train_bert.py
+++ b/HelloDeepSpeed/train_bert.py
@@ -6,6 +6,7 @@
 from functools import partial
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
 
+import random
 import datasets
 import fire
 import loguru
@@ -788,4 +789,7 @@ def train(
 
 
 if __name__ == "__main__":
+    torch.manual_seed(42)
+    np.random.seed(0)
+    random.seed(0)
     fire.Fire(train)
diff --git a/HelloDeepSpeed/train_bert_ds.py b/HelloDeepSpeed/train_bert_ds.py
index 421d03daf..dfb5f272a 100644
--- a/HelloDeepSpeed/train_bert_ds.py
+++ b/HelloDeepSpeed/train_bert_ds.py
@@ -10,6 +10,7 @@
 from functools import partial
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
 
+import random
 import datasets
 import fire
 import loguru
@@ -802,4 +803,7 @@ def train(
 
 
 if __name__ == "__main__":
+    torch.manual_seed(42)
+    np.random.seed(0)
+    random.seed(0)
     fire.Fire(train)