In this section, we explain how to prepare, package and test your submission.

At this point, we assume that your attack works with (see Framework).

Submission directory

Put your submission in a directory that satisfies the following (the demo_submission directory is a good starting point).

  1. It must contain the filesubmission.toml. See demo_submission/submission.toml for example and instructions.
  2. If your attacks depend on python packages, put your dependencies in setup/requirements.txt (you can generate it with pip freeze).
  3. It must contain the file setup/ with setup container instructions. If you only depend on python packages, keep the one of demo_submission/, otherwise, add your custom build/install steps here (see Beyond Python for details).
  4. Ensure that the ressources required by your submission are generated (e.g., profile file, etc.). The demo submission is using a library (python wheel) built by verime. It should thus be generated (in demo_submission/setup/ and must be listed in the file demo_submission/setup/requirement.txt for the evaluation to work. To this end, the command
    # Run in venv-demo-eval environment.
    make -C demo_submission/values-simulations 
    generates the library wheel, copies it into the directory demo_submission/setup and updates the file demo_submission/setup/requirements.txt accordingly.
  5. If your submission include non-source files (e.g., binary libraries or profiled models), it must contain a succint README explaining how to re-generate those from source. It may also explain how your attack works.

First test (in-place, native)

Test your submission with the script.

# To run in the SMAesH-challenge directory, assuming the submission directory is still demo_submission.
# To run after activating the venv-scripts virtual environment (see "Getting Started").
python3 scripts/ --package ./demo_submission --package-inplace --workdir workdir-eval-inplace --dataset-dir $SMAESH_DATASET

If this does not work, it is time to debug your submission. To accelerate the debugging process, see the various command-line options of In particular, --only allows you to run only some steps (e.g. --only attack).

Building and validating the submission package

The scripts/ scripts generates a valid submission .zip file based on the submission directory. You can use the following command to generate the package archive for the demo submission.

# (To run in the venv-scripts environment.)
python3 scripts/ --submission-dir ./demo_submission --package-file --large-files "setup/*.whl"

If you use "Outside the framework" profiling, you will likely have to add multiple parameters to --large-files, e.g., --large-files "setup/*.whl" "profiled_model.pkl". We try to keep submissions small (as it makes it easier to download them afterwards) by not including non-required large files.

Then, you can validate basic elements of its content with

python3 scripts/

Final tests

Let us now test the content of

python3 scripts/ --package --workdir workdir-eval-inplace --dataset-dir $SMAESH_DATASET

If this succeeds, we can move to the final test. To ensure a reproducible environment, submissions will be evaluated within a (docker-like) container runtime. The following test ensures that everything is functioning correctly inside the container (and in particular that your submission has no un-listed native dependencies -- the container is (before runs) a fresh Ubuntu 23.04). It will also validate resource constraints (you may want to relax timeouts if you use a slower machine than the evaluation server).

  • Install the Apptainer container runtime.
  • Use in --apptainer mode:
python3 scripts/ --package --workdir workdir-eval-inplace --dataset-dir $SMAESH_DATASET --apptainer

If this works, congrats! Your submission is fully functional and your results will be easily reproduced! It only remains to test it on the test dataset.

If it does not work, for debugging, note that the apptainer mode prints the commands it runs, so you can see what happens.

You may want to:

  • Use the --package-inplace mode of to avoid rebuilding the zip at every attempt.
  • Run only some steps of the submission with the --only option.
  • Clean up buggy state by deleting the workdir.
  • Run commands inside the container using apptainer shell.

Test and submit


python3 scripts/ --package --workdir workdir-eval-inplace --dataset-dir $SMAESH_DATASET --apptainer --attack-dataset-name fk1

to run the evaluation against the test dataset.

Then, send the evaluation result, along with a test dataset, to the organizers.

Remark: The resource limit rule is lifted for the post-CHES part of the challenge. However, please let us know if your submission requires significantly more computational resources that this limit.