The Broadway API is a service that receives, distributes, and keeps track of grading jobs and runs.
The aim of this project is to provide a generic interface to a distributed autograding system that can be used by multiple courses. Broadway aims to provide the following benefits:
- More stable and reliable grading runs. No one student can break the entire AG run.
- Faster grading runs. Multiple machines can grade the same assignment.
- Easier tracking and debugging of student failures during grading.
- A more consistent environment to grade student code.
- Easier to scale out the infrastructure.
Please read the Wiki for documentation. It explains how Broadway works and how to interact with it. Please be sure to read all the pages if you are planning on using Broadway.
See our contribution guidelines if you want to contribute.
MongoDB must be installed and the
mongod daemon must be running locally before starting the API. Default options are usually sufficient (but for security purposes, be sure to disallow external access to the store).
Python 3.5 is the minimum supported interpreter version. Versions 3.5 and 3.6 are officially supported, but 3.7 should work just as well.
Most configuration options are available and documented in-line in
config.py at the root of the project directory. This is the file that will be imported by the API and used for configuration.
Some behavioral configuration options are available through environment variables for ease-of-use. These are documented below.
||The token to use for cluster authentication||A randomly generated token (logged at startup)|
Running the API
Python dependencies can be installed by executing (from the project root):
pip3 install -r requirements.txt
Then the API can be started by running executing:
Starting a Grading Run
We provide a sample script to start a grading run. Make sure
PORT are set correctly. Usage:
python start_run_script.py <path to grading config json> <path to run time env json> <token>
It is recommended to build a CLI which can generate the required config files and start the grading run (so that AG run scheduling can be automated).