Python code evaluation system and submissions server capable of unit tests, tracing, and AST inspection. Server can run on Python 2.7 but evaluation requires 3.7+.
Code for automatically evaluating Python programming tasks, including a
flask WSGI server for handling submissions.
Specifications API design by Peter Mawhorter.
Server design by Peter Mawhorter, Scott Anderson, and Franklyn Turbak.
codder program by Ben Wood w/ contributions by Franklyn Turbak
and Peter Mawhorter.
The core evaluation code depends on the
Optional dependencies (get them using e.g.,
python -m pip install potluck-eval[test]):
[test]: Tests depend on
pytest, and you can run them using
toxif you want.
[expectations]: Integration with
optimismis available to require and grade student unit tests.
[turtle_capture]: Full support for capturing
turtledrawings requires the
Pillowpackage (version 6.0.0 or later), as well as a Ghostscript installation (which is not simply a PyPI package and needs to be installed manually). Support for other image-producing code is possible, but would also require
[synth]: Integration with
wavesynthis available for capturing audio produced by that package. Support for other audio libraries is not built in but is possible.
[server]: If you want to run the
potluck_serverWSGI app, you'll need
flask_cas. If you're running the WSGI app on a server without a windowing system but still want to be able to evaluate submissions that use graphics (notably submissions which use the
turtlemodule), there is support for using
xvfb-run(which would have to be installed separately as it's not a PyPI package).
[security]For full server security, you should also install
flask_seasurf, but these are not required for running the server and won't be used if they're not present (although this introduces some extra security vulnerabilities).
[https_debug]If you want to use a self-signed certificate for HTTPS while hosting the WSGI server locally for debugging purposes, you'll need
pyopenssl. This is inconvenient, so it's not recommended unless you want to develop the server side of things.
[formatting]For better formatting of markdown instructions,
pymarkdown-extensionscan be installed; it will be used if present, and the most important feature it provides is indented fenced code blocks so that they can be placed into list items.
To install from PyPI, run the following command on the command-line:
python3 -m pip install potluck-eval
Confirm installation from within Python by running:
>>> import potluck
Once that's done, you can perform run the built-in tests on the command-line:
python -m potluck.tests
Note that if you get a command not found error, the
might not have been installed somewhere that's on your command line's
path, which you'll need to fix to get the tests to run.
If you want to see what evaluation looks like yourself instead of just
running automated tests that clean up after themselves, in your installed
potluck directory inside of
site-packages there's a
testarea/test_course/fall2021 you should be able to
run the following commands:
potluck_eval -t functionsTest --rubric potluck_eval -t functionsTest --instructions potluck_eval -t functionsTest -u perfect potluck_eval -t functionsTest -u imperfect potluck_eval -t functionsTest --check
The first command creates a rubric for the "functionsTest" task in the
rubrics directory, and the second creates instructions in the
instructions directory. The third and fourth commands will evaluate the
provided test submissions for the same task, creating reports as
reports/(im)perfect/functionsTest_TIMESTAMP.html where TIMESTAMP is a
time-stamp based on when you run the command. The fifth command runs the
specification's built-in tests and prints out a report.
If the tests pass and these commands work, then
potluck is properly
installed and you can start figuring out how to set up your own
evaluation area and define your own tasks. The documentation for the
potluck.specifications module describes the task-definition process and
provides a worked example that shows off many of the possibilities; you
can find that example specification at:
potluck is installed and working , you'll need to set up your own
folder for evaluating submissions. The
potluck/testarea folder contains
an example of this, including task specifications and example
submissions (note that it's missing a
submissions folder because all of
its submissions are examples, as the
potluck_config.py there notes).
You can test things out there, but eventually you'll want to create your
own evaluation directory, which should have at minimum:
tasks.json: This file specifies which tasks exist and how to load their specifications, as well as which submitted files to look for and evaluate. You can work from the example in
specsfolder with one or more task sub-folders, named by their task IDs. Each task sub-folder should have a
spec.pyfile that defines the task, as well as
soln/folders which hold starter and solution code. These files and folders need to match what's specified in
submissionsfolder, with per-user submissions folders containing per-task folders that have actual submitted files in them. Note that if you're going to use the
potluck_serverWSGI app, this can be created automatically.
If you're going to use the
potluck_server WSGI app, your evaluation
directory will also need:
potluck-admin.json: Defines which users have admin privileges and allows things like masquerading and time travel. Work from the provided example
Finally, to run automated tests on your specifications (always a good idea) you will need:
examplesfolder with the same structure as the
To set up
potluck_server, in addition to an evaluation directory set up
as described above, you'll need to create a
ps_config.py file in a
directory of your choosing (could be the same as the base evaluation
directory if you want); there's a
rundir directory inside the installed
potluck_server directory which has an example of this; in addition to
syncauth files will be created in the
server run-directory if not present.
For testing purposes, you will not need to change the
from the defaults supplied in
ps_config.py.example, but you'll want to
edit it extensively before running the server for real. When running in a
real WSGI context, you'll also need the
potluck.wsgi file that's
present in the
ps_config.py has been created, from the
directory (or whatever directory you set up) you should be able to run:
python -m potluck_server.app
to run the WSGI app on a local port in debugging mode. It will print several messages including one or more prompts about running without authentication, and you'll have to press enter at these prompts to actually start the server, after which it should provide you with a link you can use in a browser to access it.
NOTE THAT THE POTLUCK WEB APP ALLOWS AUTHENTICATED USERS TO RUN ARBITRARY PYTHON CODE ON THE SERVER!
In addition to this, in debugging mode the server has no authentication,
and is only protected by the fact that it's only accessible to localhost.
Accordingly, you will need to set up CAS (Central Authentication Server)
via the values in
ps_config.py to run the server for real. If you don't
have access to a CAS instance via your company or institution, you can
either set one up yourself, or you'll have to modify the server to use
some other form of authentication. It is also strongly recommended that
you install the
flask_seasurf modules, which will
be used to provide additional security only if they're available. If
pyopenssl is installed alongside
flask_talisman, a self-signed
certificate will be used to provide HTTPS even in debugging mode, mostly
just to maximize similarity between debugging & production environments.
In debugging mode, you will automatically be logged in as the "test"
user, and with the default
potluck-admin.json file, this will be an
admin account, allowing you to do things like view full feedback before
the submission deadline is past. With the default setup, you should be
able to submit files for the testing tasks, and view the feedback
generated for those files (eventually, you may have to modify the due
dates in the example
tasks.json for this to work). You can find files
to submit in the
directory, and you can always try submitting some of the solution files.
See the documentation at the top of
python_server/app.py for a run-down
of how the server works and what's available.
To actually install the server as a WSGI app, you'll need to follow the
standard procedure for whatever HTTP server you're using. For example,
with Apache, this involves installing mod_wsgi and creating various
configuration files. An example Apache mod_wsgi configuration might look
like this (to be placed in
# ================================================================ # Potluck App for code submission & grading (runs potluck_eval) # the following is now necessary in Apache 2.4; the default seems to be to deny. <Directory "/home/potluck/private/potluck/potluck_server"> Require all granted </Directory> WSGIDaemonProcess potluck user=potluck processes=5 display-name=httpd-potluck home=/home/potluck/rundir python-home=/home/potluck/potluck-python python-path=/home/potluck/rundir WSGIScriptAlias /potluck /home/potluck/rundir/potluck.wsgi process-group=potluck
Running the potluck_server WSGI app on a public-facing port represents a significant security vulnerability, since any authenticated user can submit tasks, and the evaluation mechanisms currently do not use any sandboxing, meaning that they RUN UNTRUSTED PYTHON CODE DIRECTLY ON YOUR SERVER (even if they used sandboxing, which is a target feature for the future, they would be vulnerable to any means of circumventing the sandboxing used).
You therefore need to trust that your CAS setup is secure, and trust that your users will be responsible about submitting files and about keeping their accounts secure. If you can't depend on these things, DO NOT run the web app.
Even if you do not run the web app, and instead collect submissions via some other mechanism, the evaluation machinery still runs submitted code directly. You will need to trust the users submitting tasks for evaluation, and watch out for accidental mis-use of resources (e.g., creating files in an infinite loop). It's not a bad idea to run the entire evaluation process in a virtual machine, although the details of such a setup are beyond this document.
Extracted documentation can be viewed online at: https://cs.wellesley.edu/~pmwh/potluck/docs/potluck/
You can also read the same documentation in the docstrings of the source
code, or compile it yourself if you've got
pdoc installed by
make docs script on the command-line (note that shenanigans
are necessary to prevent pdoc from trying to import the test
- Version 1.0/1.1 brings potluck up-to-date with optimism 2.0, and adds a validation mode for checking test cases against solution code. Some improvements to resubmission and admin-based submission on the server are also included.
potluckversion 1.1.10 includes generator expressions and dictionary comprehensions when matching loops generally and comprehensions specifically. The wording of rubrics for these is also improved. Also sets the default behavior of
DontWasteBoxesto ignore loop variables.
potluckversion 1.1.11 includes better support for testing optimism tests cases defined within specific functions, via a testing harness in the
potluckversion 1.1.12 includes
specifications.py(although these have severe limitations) and fixes
validation.pyto be up-to-date with
optimismversion 2.6.4. It also sets the default subslip to be equal to the number of sub-rules, meaning that by default, any match is considered partial if the syntax we're looking for was found. It also adds some tests for try/with matching to the
masttests, including one that fails for now because pattern vars in the 'as -name-' position of an except block aren't supported. Try/except matching in general is extremely fragile...
potluckversion 1.1.13 upgrades the
returns_a_new_valueharness to match the
report_argument_modificationsharness in reporting positions of arguments rather than their names.
potluckversion 1.1.14 makes single-loop dictionary and set comprehensions matchable with a default Loop object, and adds set comprehensions to the relevant pattern variables.
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