Jupyter Hub on SciNet

Disclaimer: the following describes is an experimental setup at SciNet running on good, but out-of-warrantee hardware.*

• Four jupyterhub servers, each with 128 GB of memory and 16 or 20 cores.
• Access using an ssh tunnel via login.scinet.utoronto.ca:

$ ssh USER@login.scinet.utoronto.ca -L8888:jupyterhub:8000 -N -f

• This will select (round-robin) one of the four jupyterhub servers.
• Point your browser to 'localhost:8888' and log in with your SciNet account.
• The browser should now show the files in your $HOME on SciNet. (If not, try reloading the page, it may have timed out).
• You can open or create Python 2, Python 3, and R notebooks.
• Large number of python packages preinstalled.

Tips to get started

• Jupyter can also browse your (SciNet) files and edit them.
• Use the 'new' button to create a new python notebook.
• Give your notebooks reasonable names.
• To execute a python input line, press `Shift-Enter`.
• Save your work periodically (even though there is autosave).
• To work similarly to `ipython --pylab`, do:

In [1]: from pylab import *
        %matplotlib notebook

Advantages and Disadvantages of a Notebook Environment

Drawbacks:

• Notebook files (.ipynb) are not scripts.
• Does not (always) work well with version control.
• Designed to run in browser.
• Back-end runs on shared resources.
• Graphics is inline, which is great for quick exploration but make tweaking a plot harder (IPython+X works better for this).
• You can jump around in the notebook, and execute different parts: hard to keep track of what you did.

Advantages:

• You can jump around in the notebook, and execute different parts: Easier exploration, experimentation and debugging.
• Auto-save
• You can rerun parts of your code (while, e.g., keeping large data in memory)
• You can add text portions, making your notebook more like an article.
• Which in turn can be useful for sharing, demos, teaching, ...
• You can still export as a script.
• Also has a terminal.