automatic grading of jupyter notebooks
autograde is a tool that lets you run tests on Jupyter notebooks in an isolated environment and creates both, human and machine readable reports.
Now, in order to install autograde, run
pip install jupyter-autograde. Alternatively, you can install autograde from source by cloning this repository and runing
pip install -e . within it (if your’re developing autograde, run
pip install -e .[develop] instead).
Eventually build the respective container image:
python -m autograde build
When installing autograde via PyPI, docker support is not yet implemented. If you want to use docker, clone the directory and install the package from source.
autograde comes with some example files located in the
demo/ subdirectory that we will use for now to illustrate the workflow. Run:
python -m autograde test demo/test.py demo/notebook.ipynb --target /tmp --context demo/context
What happened? Let’s first have a look at the arguments of autograde:
demo/test.pycontains the a script with test cases we want apply
demo/notebook.ipynbis the a notebook to be tested (here you may also specify a directory to be recursively searched for notebooks)
- The optional flag
--targettells autograde where to store results,
/tmpin our case and the current working directory by default.
- The optional flag
--contextspecifies a directory that is mounted into the sandbox and may arbitrary files or subdirectories. This is useful when the notebook expects some external files to be present.
The output is a compressed archive that is named something like
results_[Lastname1,Lastname2,...]_XXXXXXXX.tar.xz and which has the following contents:
artifacts.tar.xz: all files that where created by or visible to the tested notebook
code.py: code extracted from the notebook including
notebook.ipynb: an identical copy of the tested notebook
test_results.csv: test results
test_restults.json: test results, enriched with participant credentials and a summary
report.rst: human readable report
In a typical scenario, test cases are not just applied to one notebook but many at a time. Therefore, autograde comes with a summary feature, that aggregates results, shows you a score distribution and has some very basic fraud detection. To create a summary, simply run:
python -m autograde summary path/to/results
Three new files will appear in the result directory:
summary.csv: aggregated results
score_distribution.pdf: a score distribution (without duplicates)
similarities.pdf: similarity heatmap of all notebooks
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