prog_server Guide#
The Prognostics As-A-Service (PaaS) Sandbox (a.k.a., prog_server) is a simplified implementation of a Service-Oriented Architecture (SOA) for performing prognostics (estimation of time until events and future system states) of engineering systems. The PaaS Sandbox is a wrapper around the ProgPy package, allowing one or more users to access the features of these packages through a REST API. The package is intended to be used as a research tool to prototype and benchmark Prognostics As-A-Service (PaaS) architectures and work on the challenges facing such architectures, including Generality, Communication, Security, Environmental Complexity, Utility, and Trust.
The PaaS Sandbox is actually two packages, prog_server and prog_client. The prog_server package is a prognostics server that provides the REST API. The prog_client package is a python client that provides functions to interact with the server via the REST API.
prog_server uses ProgPy. See the State Estimation and Prediction Guide and Modeling and Simulation Guide.
The PaaS Sandbox is a simplified version of the Prognostics As-A-Service Architecture implented as the PaaS/SWS Safety Service software by the NASA System Wide Safety (SWS) project, building upon the original work of the Convergent Aeronautics Solutions (CAS) project. This implementation is a research tool, and is therefore missing important features that should be present in a full implementation of the PaaS architecture such as authentication and persistent state management.
Installing prog_server#
The latest stable release of prog_server is hosted on PyPi. For most users, this version will be adequate. To install from the command line, use the following command:
$ pip install prog_server
Users who would like to contribute to prog_server or would like to use pre-release features can do so using the prog_server GitHub repo. This isn’t recommended for most users as this version may be unstable. To use this version, use the following commands:
$ git clone https://github.com/nasa/prog_server
$ cd prog_server
$ git checkout dev
$ pip install -e .
About#
prog_server uses progpy. The best way to learn how to use prog_server is to first learn how to use that package. See State Estimation and Prediction Guide and Modeling and Simulation Guide for more details.
The PaaS Sandbox is actually two packages, prog_server and prog_client. The prog_server package is the server that provides the REST API. The prog_client package is a python client that uses the REST API (see prog_client.Session). The prog_server package is the PaaS Sandbox Server. Once started the server can accept requests from one or more applications requesting prognostics, using its REST API (described in prog_server API Reference).
Starting the prog_server#
There are two methods for starting the prog_server, described below:
Generally, you can start the prog_server by running the module, like this:
$ python -m prog_server
Note
You can force the server to start in debug mode using the debug flag. For example, python -m prog_server --debug
There are two methods to start the prog_server in python. The first, below, is non-blocking allowing users to perform other functions while the server is running.
>>> import prog_server
>>> prog_server.start() # Starts the server in a new process (is non-blocking)
>>> ...
>>> prog_server.stop() # Stops the server
The second method, illustrated below, is blocking, meaning that the python shell will be blocked until the server is exited (e.g., by keyboard interrupt)
>>> import prog_server
>>> prog_server.run() # Starts the server- blocking.
Both run and start accept the following optional keyword arguments:
host (str): Server host address. Defaults to ‘127.0.0.1’
port (int): Server port address. Defaults to 5000
debug (bool): If the server is to be started in debug mode
Examples#
The best way to learn how to use prog_server is to look at examples. There are a number of examples included with prog_server, listed below:
examples.online_prog- This example shows how to use the PaaS Client and Server for online prognostics. Prior to running the example start the server in a terminal window with the command:
python -m prog_server
This example creates a session with the server to run prognostics for a Thrown Object, a simplified model of an object thrown into the air. Data is then sent to the server and a prediction is requested. The prediction is then displayed.
examples.option_scoring- This example demonstrates how to score multiple considered options using the PaaS Sandbox. Prior to running the example start the server in a terminal window with the command:
python -m prog_server
This example creates a session with the server to run prognostics for a BatteryCircuit. Three options with different loading profiles are compared by creating a session for each option and comparing the resulting prediction metrics