Skip to content
Standalone RESTful autograding service
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Create PULL_REQUEST_TEMPLATE Mar 17, 2016
autodriver Change find command to work on all systems, not just ubuntu Feb 17, 2016
clients Rename Tango CLI Client tango-rest -> tango-cli Feb 13, 2017
deployment/config
restful-tango Fix regression during file upload. Jan 22, 2019
tests Autopep8 the repo Apr 1, 2015
vmms Add fix missing to autograding image Dockerfile Feb 12, 2017
.dockerignore Add initial Docker config Dec 10, 2015
.gitignore Add courselabs to .gitignore (#133) Feb 8, 2017
Dockerfile
LICENSE
Makefile
README.md SVG Logo Jul 26, 2017
config.template.py USE_REDIS=True default Feb 8, 2017
hosts
jobManager.py Allow Autolab front-end to specify the AWS credentials that are used … May 11, 2016
jobQueue.py
preallocator.py Streamline imports to make dependency tree simpler Dec 3, 2015
requirements.txt
tango.py
tangoObjects.py
worker.py
wrapdocker

README.md

Tango Circle CI

Tango is a standalone RESTful Web service that runs and manages jobs. A job is a set of files that must satisfy the following constraints:

  1. There must be exactly one Makefile that runs the job.
  2. The output for the job should be printed to stdout.

Example jobs are provided for the user to peruse in clients/. Tango has a REST API which is used for job submission.

Upon receiving a job, Tango will copy all of the job's input files into a VM, run make, and copy the resulting output back to the host machine. Tango jobs are run in pre-configured VMs. Support for various Virtual Machine Management Systems (VMMSs) like KVM, Docker, or Amazon EC2 can be added by implementing a high level VMMS API that Tango provides.

A brief overview of the Tango respository:

  • tango.py - Main tango server
  • jobQueue.py - Manages the job queue
  • jobManager.py - Assigns jobs to free VMs
  • worker.py - Shepherds a job through its execution
  • preallocator.py - Manages pools of VMs
  • vmms/ - VMMS library implementations
  • restful-tango/ - HTTP server layer on the main Tango

Tango was developed as a distributed grading system for Autolab at Carnegie Mellon University and has been extensively used for autograding programming assignments in CMU courses.

Using Tango

Please feel free to use Tango at your school/organization. If you run into any problems with the steps below, you can reach the core developers at autolab-dev@andrew.cmu.edu and we would be happy to help.

  1. Follow the steps to set up Tango.
  2. Read the documentation for the REST API.
  3. Read the documentation for the VMMS API.
  4. Test whether Tango is set up properly and can process jobs.

Contributing to Tango

  1. Fork the Tango repository.
  2. Create a local clone of the forked repo.
  3. Make a branch for your feature and start committing changes.
  4. Create a pull request (PR).
  5. Address any comments by updating the PR and wait for it to be accepted.
  6. Once your PR is accepted, a reviewer will ask you to squash the commits on your branch into one well-worded commit.
  7. Squash your commits into one and push to your branch on your forked repo.
  8. A reviewer will fetch from your repo, rebase your commit, and push to Tango.

Please see the git linear development guide for a more in-depth explanation of the version control model that we use.

License

Tango is released under the Apache License 2.0.

You can’t perform that action at this time.