falcon-challenge 0.1.3

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FALCON Benchmark and Challenge

This package contains core code for submitting decoders to the FALCON challenge. Full github contains additional examples and documentation.

Installation

Install falcon_challenge with:

pip install falcon-challenge

To create Docker containers for submission, you must have Docker installed. See, e.g. https://docs.docker.com/desktop/install/linux-install/. Try building and locally testing the provided sklearn_sample.Dockerfile, to confirm your setup works. Do this with the following commands (once Docker is installed)

# Build
sudo docker build -t sk_smoke -f ./decoder_demos/sklearn_sample.Dockerfile .
sudo docker run -v ~/projects/stability-benchmark/data:/evaluation_data -it sk_smoke

Note that additional steps will be needed to allow the docker container to see GPU resources. See NVIDIA's documentation for more information. (The final docker run needs a --gpus all flag.)

Submission

To submit to the FALCON benchmark, prepare a decoder and Dockerfile. The decoder will likely reference source code that must be made importable to the Dockerfile.

To run local evaluation, first setup a data directory at ./data.

You can then run:

python <my_decoder>.py --evaluation local --phase <dataset>

TODO EvalAI submission instructions