Scipy 2019 JupyterLab tutorial

This repository contains material and instructions for the JupyterLab tutorial during Scipy 2019.

During the tutorial, feel free to get on the Gitter jupyterlab/jupyterlab channel for help and updates.

Installation

IMPORTANT: We will be using JupyterLab 1.0, which was released in late June.

If you were using an older version of JupyterLab, please upgrade to the latest. Typically, this is done using pip install --upgrade jupyterlab.

Please read the following section and install the required software ahead of time if possible. We may ask you to update versions of the software more closely to the tutorial date.

Please do not rely on cloud hosting to follow this tutorial, as the network connection may be unreliable. If possible, come to the tutorial with a computer where you have administrative privileges.

For this tutorial, we are standardizing on a conda-based python distribution (miniconda or Anaconda). We may not be able to help with installation issues if you are using a different python distribution.

Software installation

1. Install either the full anaconda distribution (very large, includes lots of conda packages by default) or miniconda (much smaller, with only essential packages by default, but any conda package can be installed).

2. To get the tutorial materials, clone this repository. Please plan to update the materials shortly before the tutorial.

   git clone https://github.com/jupyterlab/scipy2019-jupyterlab-tutorial.git
   

   To update the materials:

   cd scipy2019-jupyterlab-tutorial
   git pull
   

   Feel free to open an issue or send a pull request to update these materials if things are unclear.

3. Set up a new environment. Please do this after July 1, 2019

   Create a new conda environment for this tutorial:

   conda create -n jlabtutorial --override-channels --strict-channel-priority -c conda-forge -c anaconda --yes jupyterlab=1 ipywidgets nodejs pip cookiecutter pandas=0.24 matplotlib
   

   (You could instead create the environment from the supplied environment file with conda env create -f scipy2019-jupyterlab-tutorial/environment.yml)

   Activate the conda environment:

   conda activate jlabtutorial
   

   Install extra JupyterLab extensions:

   jupyter labextension install @jupyter-widgets/jupyterlab-manager @jupyterlab/geojson-extension @jupyterlab/toc
   

If you open multiple terminal windows make sure to activate the environment in each of them. Your terminal prompt should be preceded by the name of the current environment, for example:

(jlabtutorial) ~/scipy2019-jupyterlab-tutorial $

Starting JupyterLab

Enter the following command in a new terminal window to start JupyterLab.

$ jupyter lab

Removing environment

You can delete the environment by using the following in a terminal prompt.

conda env remove --name jlabtutorial --yes

This will not delete any data, but only the conda environement named jlabtutorial .

Optional packages

We are demonstrating a few packages not installed in the above lists. These are optional, and not required for the exercises in this tutorial.

To install these, first activate the tutorial environment:

conda activate jlabtutorial

Then install the python packages:

conda install -c conda-forge --override-channels bqplot ipyleaflet ipympl ipyvolume pythreejs
pip install sidecar

and install the JupyterLab extensions:

jupyter labextension install bqplot jupyter-leaflet ipyvolume jupyter-threejs @jupyter-widgets/jupyterlab-sidecar jupyterlab-drawio

Troubleshooting

If you experience an out-of-memory error, you can increase the memory available:

NODE_OPTIONS=--max_old_space_size=4096 jupyter lab build

or

NODE_OPTIONS=--max_old_space_size=4096 jupyter labextension install ...

This increases the available memory for the build process to 4Gb.