Fix toc (flyteorg#850)

* Changed 'packages' to 'dependencies'

Signed-off-by: SmritiSatyanV <[email protected]>

* update dynamics.py

Remove the second TOC that is rendered
Change the heading level
Signed-off-by: SmritiSatyanV [email protected]

cookbook/core/control_flow/dynamics.py

@@ -139,25 +139,25 @@ def wf(s1: str, s2: str) -> int:
 
 # %%
 # Dynamic Workflows from Execution POV
-# ------------------------------------
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # What Is a Dynamic Workflow?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# """""""""""""""""""""""""""
 #
 # A workflow whose directed acyclic graph (DAG) is computed at run-time is a :ref:`dynamic workflow <Dynamic Workflows>`. The tasks in a dynamic workflow are executed at runtime using dynamic inputs.
 #
 # Think of a dynamic workflow as a combination of a task and a workflow. It is used to dynamically decide the parameters of a workflow at runtime. It is both compiled and executed at run-time. You can define a dynamic workflow using the ``@dynamic`` decorator.
 #
 # Why Use Dynamic Workflows?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# """"""""""""""""""""""""""
 #
 # Flexibility
-# """""""""""
+# ***********
 #
 # Dynamic workflows simplify your pipelines, providing the flexibility to design workflows based on your project’s requirements, which can’t be achieved using static workflows.
 #
 # Lower Pressure on etcd
-# """""""""""""""""""""""
+# **********************
 #
 # The workflow CRD and the states associated with static workflows are stored in etcd, which is the Kubernetes database. This database stores Flyte workflow CRD as key-value pairs and keeps track of the status of each node’s execution.
 # A limitation of etcd is that the aggregate of the size of the workflow and the status of the nodes shouldn't exceed 2 MB.
@@ -166,7 +166,7 @@ def wf(s1: str, s2: str) -> int:
 # You can create large dynamic workflows or multiple dynamic workflows that are nested in a static workflow that saves storage space on etcd for memory-intensive jobs.
 #
 # How Is a Dynamic Workflow Executed?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# """"""""""""""""""""""""""""""""""""
 #
 # FlytePropeller executes the dynamic task in its k8s pod and results in a compiled Flyte DAG which is made available in the FlyteConsole.
 # FlytePropeller uses the information obtained by executing the dynamic task to schedule and execute every node within the dynamic task.
@@ -176,7 +176,7 @@ def wf(s1: str, s2: str) -> int:
 # It is named so because the workflow is yet to be executed and all the subsequent outputs are futures.
 #
 # How Does Flyte Handle Dynamic Workflows?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# """""""""""""""""""""""""""""""""""""""""
 #
 # A dynamic workflow is modeled as a task in the backend, but the body of the function is executed to produce a workflow at run-time. In both dynamic and static workflows, the output of tasks are Promise objects.
 #
@@ -186,7 +186,7 @@ def wf(s1: str, s2: str) -> int:
 # :ref:`Here<Predicting House Price in Multiple Regions Using XGBoost and Dynamic Workflows>` is an example of house price prediction using dynamic workflows.
 #
 # Where Are Dynamic Workflows Used?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# """""""""""""""""""""""""""""""""
 #
 # Dynamic workflow comes into the picture when you need to:
 #
@@ -196,7 +196,7 @@ def wf(s1: str, s2: str) -> int:
 # #. Tune hyperparameters during execution
 #
 # Dynamic versus Map Tasks
-# ^^^^^^^^^^^^^^^^^^^^^^^^
+# """""""""""""""""""""""""
 #
 # Dynamic tasks have overhead for large fan-out tasks because they store metadata for the entire workflow. In contrast, map tasks are efficient for these large fan-out tasks since they don’t store the metadata, as a consequence of which overhead is less apparent.
 #