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Docker Image for Environmental Data Science

Author: Dr. Zhonghua Zheng ([email protected])

Introduction

This Docker image is used for Environmental Data Science. It consists of:

  • Jupyter
  • data processing
    • numpy
    • pandas
    • xarray (with netCDF4 support)
    • scipy
  • machine learning
    • scikit-learn
    • PyTorch (for Deep Learning)
    • LightGBM
    • XGBoost
  • mapping & plotting:
    • Cartopy
    • matplotlib

Usage

Step 0: Install Docker Desktop. It's free.

Step 1: run a docker container (using Terminal)

  • If you have your scripts (notebooks) and data in the same folder:

    $ cd your_scripts_folder
$ docker run -it --rm -p 8888:8888 -v $PWD:/home envdes/env

  • If you have your scripts (notebooks) and data in different folders (then you would have a folder "/data" after you running the container):

    $ cd your_scripts_folder
$ docker run -it --rm -p 8888:8888 -v $PWD:/home -v /path_to_data_folder:/data envdes/env

Step 2: open your browser, type "localhost:8888", copy the token from terminal (after "token=") and paste into the webpage

  • When you import python packge(s), if you got the error like:

    libgomp-d22c30c5.so.1.0.0: cannot allocate memory in static TLS block'

    please import the package(s) based on the order below

    import sklearn
import torch
import xgboost
import xarray as xr