Depending on a users development workflow, Pipenv and the systems Python may not work. Here are a couple other common options for running notebooks locally in isolated environments.
For Conda users, create a new environment and install the dependencies:
conda create -n notebooks python=3.8
conda activate notebooks
conda install -n notebooks -c conda-forge jupyterlab ipykernel matplotlib shapely
pip install gbdxtools>=0.17.1Once all the dependencies are installed, navigate to the root of your repository. While the conda env is still active, create the kernel and start the server.
python -m ipykernel install --user --name=python3-notebooks
jupyter lab .Note: If you can't open Jupyter in your browser because it is asking for a password, try copying the url from the shell with the token, and open it in your browser. If that doesn't work, you can disable auth by running:
jupyter lab --NotebookApp.token='' --NotebookApp.password=''. Note that this removes authentication from the running server, and anyone with access to your computer can access the server.
For Docker users, there is a dockerfile and a compose file for running the jupyter server in a docker container. Make sure to navigate to the root of your repository so the correct directory is mounted to the container. You ~/.gbdx-config file is also mounted, so ensure that file exists.
To start the Jupyter server, run the following:
docker-compose up --buildThis will build the image and start a container running the server on port 8888. If you want to change the host port, you can edit the compose file. Also note that this is build on the jupyter/scipy-notebook image on Dockerhub. It has some common Python data science libraries pre-installed.
When you are done, you can close the browser and hold . You can then run the following to cleanup:
docker-compose down