Move diagrams after design

Signed-off-by: Andrey Velichkevich <[email protected]>

docs/proposals/2170-kubeflow-training-v2/README.md

@@ -102,11 +102,21 @@ We propose these APIs:
   to configure infrastructure parameters that are required for the **TrainJob**.
   For example, failure policy or gang-scheduling.
 
-The below diagram shows that platform engineers manage `TrainingRuntime` and data scientists create
-`TrainJob`:
+### User Roles Diagram
+
+The below diagram shows how platform engineers manage `TrainingRuntime` and how data scientists
+create `TrainJob`:
 
 ![user-roles](./user-roles.drawio.svg)
 
+`TrainJob` can be created using `kubectl` or Kubeflow Python SDK.
+
+### LLM Fine-Tuning Diagram
+
+The below diagram shows which resources will be created for LLM fine-tuning with PyTorch:
+
+![trainjob-diagram](./trainjob-diagram.drawio.svg)
+
 ### Worker and Node Definition
 
 To better understand what does Nodes and Worker mean in the diagram above,
@@ -410,10 +420,6 @@ spec:
       path: custom-datasets/yelp-review
 ```
 
-The below diagram shows which resources will be created for LLM fine-tuning with PyTorch:
-
-![trainjob-diagram](./trainjob-diagram.drawio.svg)
-
 ### The Trainer Config API
 
 The `TrainerConfig` represents the APIs that data scientists can use to configure trainer settings: