Forecaster-Toolbox-Run-Server.md

+## Run Server
+
+You can run the server in various modes using Python to run the `forecaster_service.py` script:
+
+```
+usage: forecaster_service.py [-h] [-dh DB_HOST] [-dp DB_PORT] [-dn DB_DBNAME]
+                             [--debug]
+                             HOST PORT SERVER_MODE
+
+positional arguments:
+  HOST           Server HOST (e.g. "localhost")
+  PORT           Server PORT (e.g. "5000")
+  SERVER_MODE    builtin, waitress
+
+optional arguments:
+  -h, --help     show this help message and exit
+  -dh DB_HOST    MongoDB HOST (e.g. "localhost") (default: localhost)
+  -dp DB_PORT    MongoDB PORT (e.g. "27017") (default: 27017)
+  -dn DB_DBNAME  Database NAME (default: forecaster_service)
+  --debug        Run builtin server in debug mode (default: False)
+```
+
+`HOST`, `PORT`, and `SERVER_MODE` arguments are **mandatory**. You can set them according to your needs.
+
+`DB_HOST`, `DB_PORT`, and `DB_DBNAME` arguments are **optional** and assume that there is a MongoDB instance running either on a local machine or remotely. In case that there is no such MongoDB instance running, the Forecasting Toolbox will still return the results, but they will not be stored anywhere.
+
+### Run built-in Flask server
+
+```
+         127.0.0.1:5000
+Client <----------------> Flask
+```
+
+To start the Forecasting Toolbox using the built-in **Flask** server, use the command promt inside the active Conda or Container environment and execute the following command: 
+
+```bash
+python forecaster_service.py 0.0.0.0 5000 builtin --debug
+```
+
+This command will start the built-in Flask server locally (0.0.0.0) on port 5000.
+
+**MongoDB Integration**
+
+In case there is a MongoDB instance running, use the command promt inside the active conda or Container environment and execute the following command: 
+
+```bash
+python forecaster_service.py 0.0.0.0 5000 builtin -dh localhost -dp 27017 -dn forecaster_service --debug
+```
+
+This command will start the built-in Flask server locally on port 5000 and store the results on a MongoDB database named "forecaster_service" running locally on port 27017.
+
+**Warning**: The built-in Flask mode is useful for development since it has debugging enabled (e.g. in case of error the client gets a full stack trace). However, it is single-threaded. Do NOT use this mode in production!
+
+### Run Waitress server
+
+```
+         127.0.0.1:5000
+Client <----------------> Waitress <---> Flask
+```
+
+To start the Forecasting Toolbox using the **Waitress** server, use the command promt inside the active Conda or Container environment and execute the following command:
+
+```bash
+python forecaster_service.py 0.0.0.0 5000 waitress
+```
+
+This command will start the Waitress server locally (0.0.0.0) on port 5000.
+
+**MongoDB Integration**
+
+In case there is a MongoDB instance running, use the command promt inside the active conda or Container environment and execute the following command: 
+
+```bash
+python forecaster_service.py 0.0.0.0 5000 waitress -dh localhost -dp 27017 -dn forecaster_service
+```
+
+This command will start the Waitress server locally on port 5000 and store the results on a MongoDB database named "forecaster_service" running locally on port 27017.
+
+**Warning**: The Waitress mode is higly recommended in real production environments, since it supports scaling and multiple-request handling features.