MarcoLandtHayen/predict_sahel_rainfall

Predict rainfall in the African Sahel region with various machine learning and deep learning methods. predict_sahel_rainfall ============================== ## Outline In this project, we aim to predict rainfall in the African Sahel region with various machine learning and deep learning methods. As data, we use a CICMoD - a climate index collection based on model data from two state-of-the-art earth system models. For details, see: github.com ## Development For now, we're developing in a Docker container with JupyterLab environment, Tensorflow and several extensions, based on martinclaus/py-da-stack. To start a JupyterLab within this container, run ```shell $ docker pull mlandthayen/py-da-tf:shap $ docker run -p 8888:8888 --rm -it -v $PWD:/work -w /work mlandthayen/py-da-tf:shap jupyter lab --ip=0.0.0.0 ``` and open the URL starting on `127.0.0.1...`. Then, open a Terminal within JupyterLab and run ```shell $ python -m pip install -e . ``` to have a local editable installation of the package. ## Container Image Additionally, there's a container image having **predict_sahel_rainfall** as pre-installed Python package: hub.docker.com. ### Use with Docker You can use it wherever Docker is installed by running: ```shell $ docker pull mlandthayen/predict_sahel_rainfall: $ docker run -p 8888:8888 --rm -it -v $PWD:/work -w /work mlandthayen/predict_sahel_rainfall: jupyter lab --ip=0.0.0.0 ``` and open the URL starting on `127.0.0.1...`. Here, ` ` can either be `latest` or a more specific tag. ### Use with Singularity You can use it wherever Singularity is installed by essentially running: ```shell $ singularity pull --disable-cache docker://mlandthayen/predict_sahel_rainfall: $ singularity run --bind $WORK jupyter lab --no-browser --ip $(hostname) $WORK ``` Here, ` ` can either be `latest` or a more specific tag. And ` `specifies the target file to store the container image. -------- Project based on the cookiecutter science project template .