Differential Privacy Temporal Map Challenge: Sprint 1 (Prescreened Arena)

CALLING PRESCREENED PARTICIPANTS! Help public safety agencies share data while protecting privacy. If you haven't been prescreened yet, head on over to the Open Arena to learn more and get started. #privacy

$29,000 in prizes
jan 2021
26 joined

Code submission format

In a typical competition, you would craft your algorithms and generate outputs for the evaluation dataset on your local machine. Then you would submit the output to the DrivenData competition for scoring. For this competition, you'll submit your algorithm itself and the code to run it, and we will generate outputs for the evaluation dataset in a containerized runtime in our cloud environment.

What to submit

Your final submission should be a zip archive named with the extension .zip (for example, submission.zip). The root level of the submission.zip file must contain a main.py, main.R, or main executable which processes a test dataset and writes the output to a file named submission.csv in the same directory as the main script/executable.

For more detail on how to create and test your submission, visit the runtime repository. The repository also contains an example submission to help you get started.


Your code is executed within a container that is defined in our runtime repository. The limits are as follows:

  • Your submission must be written in Python (3.8.5), R (4.0.2), or be an executable binary file. If your submission is in Python or R, it must use the packages defined in the runtime repository (Python and R requirements). If submitting an executable, you must include the source code in your submission.
  • The submission must complete execution in 30 minutes or less. This limit is especially helpful to address non-terminating code, and we expect most submissions to complete more quickly. If you find yourself requiring more time than this limit allows, open a Github issue in the repository to let us know.
  • The container runtime has access to a single GPU. All of your code should run within the GPU environments in the container, even if actual computation happens on the CPU. (CPU environments are provided within the container for local debugging only.)
  • The container has access to 6 vCPUs powered by an Intel Xeon E5-2690 chip and 56GB RAM.
  • The container has 1 Tesla K80 GPU with 12GB of memory.
  • The container will not have network access. All necessary files (code and model assets) must be included in your submission.
  • The container execution will not have root access to the filesystem.

The GPUs for executing your code are a shared resource across competitors. We request you are conscientious in your use of them. Please add progress information to your logs and cancel jobs that will run longer than the time limit. Canceled jobs won't count against your submission limit, and this means more available resources to score submissions that will complete on time.

Requesting package installations

Since the Docker container will not have network access, all packages must be pre-installed. We are happy to add packages as long as they do not conflict and can build successfully. Packages must be available through conda for Python 3.8.5 or R 4.0.2. To request an additional package be added to the docker image, follow the instructions in the runtime repository.

Happy building! Once again, if you have any questions or issues you can always head on over the user forum!