This command-line tool is a software development toolkit to help instructional teams author asynchronous graders for Coursera (typically programming assignments). Coursera's asynchronous grading environment is based upon docker. While use of this tool is by no means required to develop the docker container images, we believe it is helpful in the endeavour. See below for brief descriptions of this tool's capabilities.

You can install coursera_autograder using pip! Simply run:

python3 -m pip install coursera_autograder

If you've previously installed coursera_autograder from source, you can uninstall it by navigating to the directory where it is installed and running:

python setup.py develop --uninstall

Alternatively, you may use the following commands to install from source:

git clone https://github.com/coursera/coursera_autograder.git
cd coursera_autograder
virtualenv -p python3 venv (Optional)
source venv/bin/activate (Optional)
python setup.py develop
pip install -r test_requirements.txt
pip install -e .

If you would like to separate your build environments, we recommend installing coursera_autograder within a virtual environment.

pip is a python package manager. If you do not have pip installed on your machine, please follow the installation instructions for your platform found at: https://pip.pypa.io/en/latest/installing.html#install-or-upgrade-pip

The tool includes its own usage information and documentation. Simply run:

coursera_autograder -h

or:

coursera_autograder --help

for a complete list of features, flags, and documentation.

Note: the tool requires docker to already be installed on your machine. Please see the docker installation instructions for further information.

This grade local subcommand loosely replicates the production grading environment on your local machine. Note that because the GrID system has adopted a defense-in-depth or layered defensive posture, not all layers of the production environment can be faithfully replicated locally.

grade local runs a local grader container image on a sample submission on the local file system, provided as part of the command. This command is intended to help instructional teams verify new versions of their graders correctly handle problematic submissions.

Examples:

• coursera_autograder grade local $MY_CONTAINER_IMAGE_TAG /path/to/sample/submission/ $ENV_VAR_JSON --dst-dir ~/Desktop invokes the grader passing in the sample submission into the grader.
• When the grade local is successful, you can verify that feedback.json is produced in dst-dir (in this case it is ~/Desktop).
• $ENV_VAR_JSON is a json string like'{"partId": "Zb6wb"}',
• coursera_autograder grade local --help displays the full list of flags and options available.
• coursera_autograder grade local python_grader ./submission '{"partId": "5ShhY"}' --dst-dir ~/Desktop
• Please make sure there is only the correct solution file in the submission directory (./submission).

In contrast to this local tester, Coursera's production system will also set these environment variables for internal purposes. In local testing, it is possible to specify these as well with the environment variable JSON, although it's completely up to the grading Docker you create to use them or not. In typical usage, you would not set or read these variables.

• filename - The original filename of the file the student has chosen to submit, prior to being renamed on the server automatically. To avoid confusion, note that the grade local command takes a directory path, not a file path; the directory should contain a submission file with the same filename as the "suggested filename" you've configured for the assignment as published on Coursera's UI, and your autograder should also look for the file with the "suggested filename." The filename environment variable does not specify the "suggested filename". Also, autograders live in production will find the submitted file has already been renamed, so the filename env var does not have much usefulness inside the grader. One use case might be to display a warning to learners if the file they chose to submit does not have the correct file extension prior to being automatically renamed.
• userId - a unique string Coursera uses to disambiguate learners.

Allows an instructional team to upload their containers to Coursera without using a web browser. It is designed to even work in an unattended fashion (i.e. from a jenkins job). In order to make the upload command work from a Jenkins automated build machine, simply copy the ~/.coursera directory from a working machine, and install it in the jenkins home folder. Beware that the oauth2_cache file within that directory contains a refresh token for the user who authorized themselves. This refresh token should be treated as if it were a password and not shared or otherwise disclosed!

To find the course id, item id, and part id, first go to the web authoring interface for your programming assignment. There, the URL will be of the form: /:courseSlug/author/outline/programming/:itemId/. The part id will be displayed in the authoring user interface for each part. To convert the courseSlug into a courseId, you can take advantage of our Course API putting in the appropriate courseSlug. For example, given a course slug of developer-iot, you can query the course id by making the request: https://api.coursera.org/api/onDemandCourses.v1?q=slug&slug=developer-iot. The response will be a JSON object containing an id field with the value: iRl53_BWEeW4_wr--Yv6Aw.

The uploaded grader can be linked to multiple (itemId, partId) pairs without making duplicate uploads by using the --additional_item_and_part flag.

This command can also be used to customize the resources that will be allocated to your grader when it grades learner submissions. The CPU, memory limit and timeout are all customizable.

• --grader-cpu takes a value of 1, 2 or 4, representing the number of cores the grader will have access to when grading. The default is 1.

• --grader-memory-limit takes a value between 4096 to 16384, increnment of 1024. representing the amount of memory in MB the grader will have access to when grading. The default is 4096 (4GB).

  Not all combinations of cpu and memory are supported. The supported combinations is listed here:

  • For 1024 (1 vCPU), Memory needs to be between 2048 (2GB) and 8192 (8GB) in increments of 1024 (1GB).
  • For 2048 (2 vCPU), Memory needs to be between 4096 (4GB) and 16384 (16GB) in increments of 1024 (1GB).
  • For 4096 (4 vCPU), Memory needs to be between 8192 (8GB) and 16384 (16GB) in increments of 1024 (1GB)
• --grading-timeout takes a value between 300 and 1800, representing the amount of time the grader is allowed to run before it times out. Note this value is counted from the moment the grader starts execution and does not include the time it takes Coursera to schedule the grader. The default value is 1200.

Examples:

• coursera_autograder upload $PATH_TO_IMAGE_ZIP_FILE $COURSE_OR_BRANCH_ID $ITEM_ID $PART_ID uploads the specified grader container image to Coursera, begins the post-upload processing, and associates the new grader with the specified item part in a new draft. Navigate to the course authoring UI or use the publish command to publish the draft to make it live.
• coursera_autograder upload $PATH_TO_IMAGE_ZIP_FILE $COURSE_OR_BRANCH_ID $ITEM_ID $PART_ID --additional_item_and_part $ITEM_ID2 $PART_ID2 $ITEM_ID3 $PART_ID3 uploads the specified graded container image to Coursera, begins the post-upload procesing, and associates the new grader with all the three item_id part_id pairs. Navigate to the course authoring UI for each item to publish the draft to make it live.
• coursera_autograder upload --help displays all available options for the upload subcommand.
• zip -r PythonGrader.zip . (Make sure you are in the directory containing the Dockerfile. This must be the top level directory)
• coursera_autograder upload ./PythonGrader.zip iRl53_BWEeW4_wr--Yv6Aw rLa7F Zb6wb

Allows an instructional team to view the resource limits (vCPU's, MiB, timeout) allocated to the grader for a given programming assignment. It requires the instructor to provide the course id, item id, and part id to identify the specific programming assignment. Instructions on how to find these values can be found in the previous section for the upload command.

Usage:

• coursera_autograder get_resource_limits $COURSE_OR_BRANCH_ID $ITEM_ID $PART_ID

Allows an instructional team to update the resource limits (vCPU's, MiB, timeout) allocated to the grader for a given programming assignment. It requires the instructor to provide the course id, item id, and part id to identify the specific programming assignment. Instructions on how to find these values can be found in the previous section for the upload command. In addition, the instructor must provide the values they wish to update by using the parameter flags

• --grader-cpu to update the allocated vCPU's
• --grader-memory-limit to update the memory limit
• --grader-timeout to update the timeout threshold

If a certain parameter is not provided, then we will simply use the previously existing value. Note that there are restrictions on which combinations of CPU's and memory values are valid. These restrictions can be found in the upload section above.

Usage:

• coursera_autograder update_resource_limits $COURSE_OR_BRANCH_ID $ITEM_ID $PART_ID --grader-cpu $CPU --grader-memory-limit $MEMORY --grader-timeout $TIMEOUT

Makes sure that the instructor is able to communicate with the coursera.org API servers with the correct authentication.

Usage:

• coursera_autograder configure check-auth
• coursera_autograder configure display-auth-cache

Lists all graders uploaded to the given course.

Usage:

• coursera_autograder list_graders $COURSE_ID

Gets the upload status of a grader given its executor id. The executor id can be found by using the list_graders command.

Usage:

• coursera_autograder get_status $EXECUTOR_ID

Please reach out to your partner support teams to file an enhancement request or report a bug. While we check in on the issues logged on this repository from time to time, reaching out to your partner support teams will likely provide you with a faster response. We appreciate your support and dedication to improving this tool for all Coursera partners!

Note: We do not have the bandwidth to officially support this tool on windows. That said, patches to add / maintain windows support are welcome!

We recommend developing coursera_autograder within a python virtualenv. To get your environment set up properly, do the following:

virtualenv venv
source venv/bin/activate
python setup.py develop
pip install -r test_requirements.txt

To run tests, simply run: nosetests, or tox.

Code should conform to pep8 style requirements. To check, simply run:

pep8 coursera_autograder tests