chore: some work on the report

report/report.tex

@@ -113,7 +113,7 @@
 			% TODO fix the inglish in these sentance
 			The models for this system to work as indented should be as small as possible while obtaining the required accuracy required to achieve the task of classification of the classes.
 
-			As the service might need to handle a large number of requests, it needs to be able to handle as many requests as possible. This would require that the models are easy to run, and smaller models are easier to run, therefore the system requires a balance between size and accuracy.
+			As the service might need to handle many requests, it needs to be able to handle as many requests as possible. This would require that the models are easy to run, and smaller models are easier to run; therefore the system requires a balance between size and accuracy.
 
 			% TODO talk about storage
 
@@ -209,7 +209,16 @@
 
 			Since the AutoML approach would be more computational intensive, it would be less desirable to run. Therefore, the approach would be for the database search to happen first, where known possibly good models would be first tested. If a good model is found, then the search stops and if no model is found, the system would resort to AutoML to find a suitable model.
 
+		\subsection{Models Training}
+			% The Training process follows  % TODO have a flow diagram
 
+			The training of the models happens in a secondary Training Process(TP).
+		
+			Once a model candidate is generated, the main process informs the TP of the new model. The TP obtains the dataset and starts training. Once the model finished training, it reports to the main process with the results. The main process then decides if the model matches the requirements. If that the case, then the main process goes to the next steps; otherwise, the system goes for the next model that requires training.
+
+			The TP when training the model decides when the training is finished, this could be when the training time has finished or if the model accuracy is not substantially increasing within the last training rounds.
+
+			During the training process the TP needs to also cache the dataset being used, this is because to create one model, the system might try to generate more than on model and match the best of the generated models with
 	\pagebreak
     \section{Design Choices}
 
@@ -242,19 +251,19 @@
 	\pagebreak
     \section{Appendix}
 
-    \begin{figure}
-        \begin{center}
-			\includegraphics[height=\textheight]{expandable_models_simple}
-        \end{center}
-        \caption{Contains an overall view of the entire system}\label{fig:expandable_models_simple}
-    \end{figure}
+	    \begin{figure}
+	        \begin{center}
+				\includegraphics[height=0.9\textheight]{expandable_models_simple}
+	        \end{center}
+	        \caption{Contains an overall view of the entire system}\label{fig:expandable_models_simple}
+	    \end{figure}
 
-    \begin{figure}
-        \begin{center}
-			\includegraphics[height=\textheight]{expandable_models_generator}
-        \end{center}
-        \caption{Contains an overall view of the model genration system}\label{fig:expandable_models_generator}
-    \end{figure}
+	    \begin{figure}
+	        \begin{center}
+				\includegraphics[height=0.9\textheight]{expandable_models_generator}
+	        \end{center}
+	        \caption{Contains an overall view of the model genration system}\label{fig:expandable_models_generator}
+	    \end{figure}