Adds additional instructions for running within jupyter notebooks

docs/how-tos/use-in-jupyter-notebook.rst

@@ -4,14 +4,27 @@ Run in a Jupyter Notebook
 
 It's natural to run Hamilton within a jupyter notebook. There are a few ways to go about this:
 
-#. Store all your code in the jupyter notebook, iterating on the functions themselves as well as the driver \
+
+1. Store all your code in the jupyter notebook, iterating on the functions themselves as well as the driver \
 (see example below).
-#. Store your code externally, and import it into the notebook. Utilize the driver to run the code, iteratively adding \
+
+2. Store your code externally, and import it into the notebook. Utilize the driver to run the code, iteratively adding \
 functions to the driver as you settle on transform logic.
-#. Execute/manage your code externally, and use the notebook to visualize the DAG/interact with it.
 
+3. Execute/manage your code externally, and use the notebook to visualize the DAG/interact with it.
 How you do this is entirely up to you! We're likely going to add more first-class jupyter notebook support.
 
+Note that you can set up automatic reloading of modules (useful for (2) and (3)) using the following code:
+
+.. code-block:: python
+
+    %load_ext autoreload
+    %autoreload 1
+    %aimport my_functions
+
+
+More explanation in the notebook example below.
+
 The `Hello World <https://github.com/DAGWorks-Inc/hamilton/blob/main/examples/hello_world/my_notebook.ipynb>`__ \
 example notebook shows you how you would approach (1), and it should be easy to extend to the other approaches.