move design docs to a folder

doc/auth_design.md → doc/design/auth_design.md

@@ -72,7 +72,7 @@ that case, we store session data into a reliable storage service like
 The below figure demonstrates overall workflow for authorization and
 authentication.
 
-<img src="figures/sqlflow_auth.png">
+<img src="../figures/sqlflow_auth.png">
 
 Users can access the JupyterHub web page using their own username and password.
 The user's identity will be verified by the [SSO](https://en.wikipedia.org/wiki/Single_sign-on)

doc/cluster_design.md → doc/design/cluster_design.md

@@ -9,7 +9,7 @@ This design document introduced how to support the `Cluster Model` in SQLFLow.
 
 The figure below demonstrates the overall workflow for cluster model training, which include both the pre_train autoencoder model and the clustering model.(Reference https://www.dlology.com/blog/how-to-do-unsupervised-clustering-with-keras/)
 
-<div align=center> <img width="460" height="550" src="figures/cluster_model_train_overview.png"> </div>
+<div align=center> <img width="460" height="550" src="../figures/cluster_model_train_overview.png"> </div>
 
 1. The first part is used to load a pre_trained model. We use the output of the trained encoder layer as the input to the clustering model. 
 2. Then, the clustering model starts training with randomly initialized weights, and generate clusters after multiple iterations.

doc/training_and_validation.md → doc/design/training_and_validation.md

@@ -5,7 +5,7 @@ A common ML training job usually involves two kinds of data sets: training data
 ## Overall
 SQLFlow generates a temporary table following the user-specific table, trains and evaluates a model.
 
-<img src="./figures/training_and_validation.png" width="60%">
+<img src="../figures/training_and_validation.png" width="60%">
 
 Notice, we talk about the **train** process in this post.
 
@@ -125,4 +125,4 @@ In the end, SQLFlow remove the temporary table to release resources.
 
 - If the column sqlflow_random already exists, SQLFlow chooses to quit   
   Notice, *column name started with an underscore is invalid in the hive*
-- Any discussion to implement a better splitting is welcomed
+- Any discussion to implement a better splitting is welcomed