Add extra steps to readme and makefile

model_data/simulation_system/Makefile

@@ -13,8 +13,13 @@ env:
 	. .training_env/bin/activate
 	pip install --upgrade pip
 	pip install -r requirements/training/training-dev.txt && pre-commit install
+
+	echo " --- TO ACTIVATE THE ENVIRONMENT --- "
 	echo "Run source .training_env/bin/activate to activate the virtual environment"
 
+.PHONY: check-all
+check-all: pre-commit run -a
+
 .PHONY: build
 build:
 	docker-compose build

model_data/simulation_system/README.md

@@ -3,6 +3,13 @@
 Starter Readme:
 Steps for pipeline:
 
+- (WIP) Set up the training development environment
+    - Change directory to this folder (simulation_system)
+    - Run the following command `make env PYTHON_VERSION=3.10.12`
+    - This will install the specified python version using `pyenv` and select this version as the global python version
+    - It will install all training packages as specified in the training-dev.txt requirements file, including the pre-commit hooks
+    - Run `source .training_env/bin/activate` to use this environment
+
 - (WIP) Use Makefile to start up mock up s3 service
     - By running `make init`, this will run the `docker-compose build` and `docker-compose up -d`, which spins up a S3 service
     - This docker compose is running in detached mode `-d`, so will no output anything to the terminal
@@ -27,7 +34,7 @@ Steps for pipeline:
 
 - Once model build is finished, you can run the `prediction.py` file to generate prediction
     - By default, the prediction pipeline will select the best model based on **mean absolute error** from the model registry
-        - This can be overwritten by specifying a model_path, which will load an alternative model 
+        - This can be overwritten by specifying a model_path, which will load an alternative model
     - There are two ways of getting data into the pipeline:
         - Using the `--data` argument:
             - This is a JSON string which can be passed as `python3 predictions.py --data '{"TOTAL_FLOOR_AREA": 1}'`
@@ -55,12 +62,16 @@ Steps for pipeline:
     - Add precommit hooks (linters, branch names, etc)
     - Sphinx documentation
     - Sort out local mock up services
-    - Sort out Model Registry 
+    - Sort out Model Registry
     - Sort out Data version control
+    - pre-commit hooks:
+        - The types of hooks that we want (safety, bandit, iso8 etc)
+        - The customisations we require
+    - Add sphinx documentation
 - Data Science:
-    - Implement a metrics class, to hold all metric 
+    - Implement a metrics class, to hold all metric
     - Rebuild metrics script (Could be a one off but good to have)
-    - Determine metrics 
+    - Determine metrics
     - Implement and test custom model (Tensorflow Decision Trees etc)
 - Orchestration:
-    - Lambda handler for the pipeline
+    - Lambda handler for the pipeline