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SETI_Breakthrough_Listen

An entry in a Kaggle AI/ML competition to find extraterrestrial technosignatures in spectrogram data from deep space observations using radio telescopes.

Running the models and notebooks within Docker

An explanation of some non-standard arguments and shared volumes:

  • Ports for Jupyter Notebook
  • GPU Passthrough for CUDA-accelerated code
  • Mount shm to increase shared memory, prevent out of memory efforts on large batch sizes
  • Mount the poject repository
  • Mount the data store

For the interactive situation, the run command is augmented with an interactive flag and an entrypoint to preempt the automated execution.

Automated - Train and Test Model

Running the docker container with the following command will cause the container to automatically train, save, and test the model detailed in seti_bl_pytorch_cnn.py.

docker run -p 8888:8888 --gpus all --rm -v /dev/shm:/dev/shm -v /home/jeffrey/repos/SETI_Breakthrough_Listen:/SETI -v /home/jeffrey/data/seti_bl:/data  setibl:0.1.0

Interative & Jupyter Notebook

To run the container and either work within the environment interactively or run the jupyter notebook, use the following command:

docker run -p 8888:8888 --gpus all --rm -v /dev/shm:/dev/shm -v /home/jeffrey/repos/SETI_Breakthrough_Listen:/SETI -v /home/jeffrey/data/seti_bl:/data -it --entrypoint bash setibl:0.1.0

And then at the container's bash prompt, use the following command to run the notebooks:

jupyter notebook --port=8888 --no-browser --ip=0.0.0.0 --allow-root

Any other python or tasks requireing the environment may be performed here as well.

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