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Introduction to Jupyter#

What is Jupyter?#

A web app for interactive Python in a browser

  • "Live coding"
  • Instant visualization
  • Sharable
  • Reproducible
  • Customizable
  • Now supports other languages besides Python (R, Julia..)
    • these slides were created using Markdown in Jupyter!
import chart_studio.plotly as py
import plotly.figure_factory as ff
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
x = np.linspace(0, 5, 10)
y = x ** 2
n = np.array([0,1,2,3,4,5])
xx = np.linspace(-0.75, 1., 100)

fig, axes = plt.subplots(1, 4, figsize=(12,3))

axes[0].scatter(xx, xx + 1.25*np.random.randn(len(xx)))
#axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx)))

axes[1].step(n, n**2.0, lw=2)

axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5)

axes[3].fill_between(x, x**2.5, x**3, color="green", alpha=0.5);


Terminology - a Confusion of Words#


* Multi-user "backend" server
* Controls launching the single-user Jupyter server
* NREL's "Europa" runs Jupyterhub

(In general, don't worry about JupyterHub--unless you're a sysadmin)

Jupyter/Jupyter Server/Notebook server#

* The single-user server/web interface
* Create/save/load .ipynb notebook files
* What users generally interact with

Jupyter Notebook#

* An individual .pynb file
* Contains your Python code and visualizations
* Sharable/downloadable

Jupyter lab#

* A "nicer" web interface for Jupyter - "notebooks 2.0"
* Preferred by some
* Lacking some features of "classic" notebooks


* The Python environment used by a notebook
* More on kernels later

Using Europa#

We run a Jupyterhub server that is available.

Europa's Advantages:#

* Fast and easy access
* Use regular Eagle credentials
* Great for light to moderate processing/debugging/testing

Europa's Disadvantages:#

* Limited resource: 8 cores/128GB RAM per user beefore automatic throttling
* Compete with other users for CPU/RAM on a single machine
* No custom environments (for now)

Simple Instructions:#

- Visit Europa at (

- Log in using your HPC credentials

- Opens a standard "notebooks" interface

- Change url end /tree to /lab for Lab interface

Using a Compute Node#


* Custom environments
* 36 cores and up to ~750GB RAM
* No competing with other users for cores


* Compete with other users for nodes
* Costs AU

...Is more than one node possible?#

Yes... please see our advanced Jupyter documentation.

Using a Compute Node - Hard Way#

Start on a login node:#


[user@el1:]$ srun -A <account> -t 02:00:00 --pty /bin/bash

When the job starts on the compute node:#

[user@r2i7n35]$ module load conda

source activate myjupenv

jupyter-notebook --no-browser --ip=$(hostname -s)

note the node name (r2i7n35 in this example)

and the url, e.g.<alphabet soup>

In a terminal on your computer:#

[user@laptop]$ ssh -N -L 8888:<nodename>:8888

copy full url from jupyter startup into your web browser. e.g.:<alphabet soup>

Using a Compute Node - Easy Way#

Automation makes life better!

pyeagle - NREL Users#

The pyeagle package is available for internal users to handle launching and monitoring a jupyter server on a compute node. This package is maintained by an NREL HPC user group, and provides utilities for working on Eagle and Kestrel.

Auto-launching with an sbatch script - All Users#

Full directions included in the Jupyter repo.

Download and

Edit to change:



Run and follow directions

That's it!

Using a Login Node#

Yes, you can run jupyter directly on a login node.

Should you run jupyter directly on a login node?

Reasons to Not Run Jupyter Directly on a Login Node#

* Heavy lifting should be done via Europa or compute nodes
* Using a highly shared resource (login nodes)
    * Competition for cycles
    * arbiter2 will throttle moderate to heavy usage

Custom Conda Environments and Jupyter Kernels#

Creating a conda environment:#

conda create -n myjupyter -c conda-forge jupyter ipykernel

source activate myjupyter

conda install -c conda-forge scipy numpy matplotlib

Add custom ipykernel#

python -m ipykernel install --user --name=myjupyter

Restart your jupyter server

New kernel will appear in drop-down as an option

Remove custom ipykernel#

jupyter kernelspec list

jupyter kernelspec remove myoldjupyter

Magic commands#

Magic commands are "meta commands" that add extra functionality.

Magic commands begin with % or %%.

A Few Useful Examples#

* %lsmagic - list all magic commands
* %run _file.py_ - run an external python script
* %%time - placed at top of cell, prints execution time
* %who - list all defined variables in notebook
Available line magics:
%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode

Available cell magics:
%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile

Automagic is ON, % prefix IS NOT needed for line magics.

Shell Commands#

You can also run shell commands inside a cell. For example:

!conda list - see the packages installed in the environment you're using

/home/tthatche/jup    Jupyter Presentation.ipynb  slurm-6445885.out
geojsondemo.ipynb         old                         sshot1.png
Interesting Graphs.ipynb           sshot2.png
jup-logo.png              slurm

Interesting/Useful Notebooks#

Awesome Jupyter

Awesome Jupyterlab

Plotting with matplotlib

Python for Data Science

Numerical Computing in Python

The Sound of Hydrogen

Plotting Pitfalls

GeoJSON Extension

Happy Notebooking!#