diff --git a/demo-notebooks/guided-demos/mnist_fashion.py b/demo-notebooks/guided-demos/mnist_fashion.py
index 2b75a865..85cd6e64 100644
--- a/demo-notebooks/guided-demos/mnist_fashion.py
+++ b/demo-notebooks/guided-demos/mnist_fashion.py
@@ -74,6 +74,7 @@ def train_func_distributed():
 # For GPU Training, set `use_gpu` to True.
 use_gpu = True
 
+# To learn more about configuring S3 compatible storage check out our docs -> https://github.com/project-codeflare/codeflare-sdk/blob/main/docs/s3-compatible-storage.md
 trainer = TorchTrainer(
     train_func_distributed,
     scaling_config=ScalingConfig(
diff --git a/docs/s3-compatible-storage.md b/docs/s3-compatible-storage.md
new file mode 100644
index 00000000..919ce815
--- /dev/null
+++ b/docs/s3-compatible-storage.md
@@ -0,0 +1,61 @@
+# S3 compatible storage with Ray Train examples
+Some of our distributed training examples require an external storage solution so that all nodes can access the same data. <br>
+The following are examples for configuring S3 or Minio storage for your Ray Train script or interactive session.
+
+## S3 Bucket
+In your Python Script add the following environment variables:
+``` python
+os.environ["AWS_ACCESS_KEY_ID"] = "XXXXXXXX"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "XXXXXXXX"
+os.environ["AWS_DEFAULT_REGION"] = "XXXXXXXX"
+```
+Alternatively you can specify these variables in your runtime environment on Job Submission.
+``` python
+submission_id = client.submit_job(
+    entrypoint=...,
+    runtime_env={
+        "env_vars": {
+            "AWS_ACCESS_KEY_ID": os.environ.get('AWS_ACCESS_KEY_ID'),
+            "AWS_SECRET_ACCESS_KEY": os.environ.get('AWS_SECRET_ACCESS_KEY'),
+            "AWS_DEFAULT_REGION": os.environ.get('AWS_DEFAULT_REGION')
+        },
+    }
+)
+```
+In your Trainer configuration you can specify a `run_config` which will utilise your external storage.
+``` python
+trainer = TorchTrainer(
+    train_func_distributed,
+    scaling_config=scaling_config,
+    run_config = ray.train.RunConfig(storage_path="s3://BUCKET_NAME/SUB_PATH/", name="unique_run_name")
+)
+```
+To learn more about Amazon S3 Storage you can find information [here](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html).
+
+## Minio Bucket
+In your Python Script add the following function for configuring your run_config:
+``` python
+import s3fs
+import pyarrow
+
+def get_minio_run_config():
+   s3_fs = s3fs.S3FileSystem(
+       key = os.getenv('MINIO_ACCESS_KEY', "XXXXX"),
+       secret = os.getenv('MINIO_SECRET_ACCESS_KEY', "XXXXX"),
+       endpoint_url = os.getenv('MINIO_URL', "XXXXX")
+   )
+   custom_fs = pyarrow.fs.PyFileSystem(pyarrow.fs.FSSpecHandler(s3_fs))
+   run_config = ray.train.RunConfig(storage_path='training', storage_filesystem=custom_fs)
+   return run_config
+```
+You can update the `run_config` to further suit your needs above.
+Lastly the new `run_config` must be added to the Trainer:
+``` python
+trainer = TorchTrainer(
+    train_func_distributed,
+    scaling_config=scaling_config,
+    run_config = get_minio_run_config()
+)
+```
+To find more information on creating a Minio Bucket compatible with RHOAI you can refer to this [documentation](https://ai-on-openshift.io/tools-and-applications/minio/minio/).<br>
+Note: You must have `sf3s` and `pyarrow` installed in your environment for this method.