vidakris/flatwrm2 repository overview

FLATW'RM2 is a deep learning code that was developed to detect flares in light curves obtained by space observatories (like Kepler or TESS). For more details, see the paper on arXiv⁠.

The container is based on the tensorflow/tensorflow:2.3.4-gpu-jupyter⁠ image.

⁠Running the container

First, obtain the docker image:

$ docker pull vidakris/flatwrm2

Then change to the directory with your data files and run:

$ docker run -p 8888:8888 -v "$(pwd):/data" vidakris/flatwrm2

You can of course change the $(pwd) part with the full path to the light curves. Your data will be accessible in the /data directory of the container, and this way the output of the calculations can be directly saved in this directory making them accessible easily outside the container.

To access the notebook, search for the token line in the output, something like this: http://127.0.0.1:8888/?token=3995baa083a0780920375f94b3acbaa92a9115a457d1d4e0⁠, and copy this line to your browser. You can either run FLATW'RM2 from a notebook by opening the examples/exampe.ipynb notebook or open a terminal from jupyter and run flatwrm2 from a command line.

⁠GPU support

The container supports calculation on GPU based on Nvidia CUDA⁠. For details on getting CUDA working with docker consult

https://hub.docker.com/r/tensorflow/tensorflow⁠

https://github.com/NVIDIA/nvidia-docker⁠

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker⁠

Once the drivers are installed, and the GPUs are visible in docker by

$ docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

run flatwrm2 with the following option for GPU support:

$ docker run -p 8888:8888 -v "$(pwd):/data" --gpus all vidakris/flatwrm2