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Advanced computing with IPython

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Re: Advanced computing with IPython

#3

For the last couple of year I had wrongly thought that IPython was short for Iron Python. Now that I know it is not, I understand the hype around it more

Early in my career I thought CPython and Cython were the same. And then Pypy and Pypi confused me. And Python the language vs. Python the implementations and how when someone says "Python" in some contexts they assume "CPython". Landmines everywhere!

Re: Advanced computing with IPython

#4

For the last couple of year I had wrongly thought that IPython was short for Iron Python. Now that I know it is not, I understand the hype around it more

Early in my career I thought CPython and Cython were the same. And then Pypy and Pypi confused me. And Python the language vs. Python the implementations and how when someone says "Python" in some contexts they assume "CPython". Landmines everywhere!

[deleted]

Re: Advanced computing with IPython

#5

For the last couple of year I had wrongly thought that IPython was short for Iron Python. Now that I know it is not, I understand the hype around it more

Early in my career I thought CPython and Cython were the same. And then Pypy and Pypi confused me. And Python the language vs. Python the implementations and how when someone says "Python" in some contexts they assume "CPython". Landmines everywhere!

Yup, whenever I write one of these I need to spend some time on terminology. It's even more confusing now with Jupyter, as there is still much overlap between that and IPython.

Re: Advanced computing with IPython

#6
If interested, I spent some time on a comment thread a few days ago describing how my experience leads me to believe the Notebook environment (not all of Jupyter / IPython, just the Notebook part) is actually only appropriate for a tiny subset of pedagogical or throw-away situations, and should be avoided most of the time and avoided in most of the cases it’s marketed for (especially anything having to do with ‘reproducibility’ or ‘exploratory analysis’).

https://news.ycombinator.com/item?id=17202704 >

Re: Advanced computing with IPython

#7

Earlier quoted context omitted.

Early in my career I thought CPython and Cython were the same. And then Pypy and Pypi confused me. And Python the language vs. Python the implementations and how when someone says "Python" in some contexts they assume "CPython". Landmines everywhere!

Yup, whenever I write one of these I need to spend some time on terminology. It's even more confusing now with Jupyter, as there is still much overlap between that and IPython.

Yep. I honestly thought Jupyter is just a rename of IPython until you said that. Now I have to go learn the difference.

Re: Advanced computing with IPython

#8
Don't overlook all of the % commands, such as %edit. If you are familiar with emacs keybindings, it has a very good built in editor as well. You can also load snippets from saved files, and save your history to a file. Or individual lines to files using range type expressions. In short it is very easy to get code in and out of IPython.

Another great trick. Anywhere you want to debug or play in your scripts run `import IPython` and the run `IPython.embed()` and your program at that point with all its locals drops into an IPython session, which is nice.

Re: Advanced computing with IPython

#9

Don't overlook all of the % commands, such as %edit. If you are familiar with emacs keybindings, it has a very good built in editor as well. You can also load snippets from saved files, and save your history to a file. Or individual lines to files using range type expressions. In short it is very easy to get code in and out of IPython. Another great trick. Anywhere you want to debug or play in your scripts run `impor…

As of 3.7 I think, you can use breakpoint() in your code and select the debugger you want with an environment variable, or enable/disable it without changing the code.

https://www.python.org/dev/peps/pep-0553/

Re: Advanced computing with IPython

#10
At Harvard we've built out an infrastructure to allow us to deploy JupyterHub to courses with authentication managed by Canvas. It has allowed us to easily deploy complex set-ups to students so they can do really cool stuff without having to spend hours walking them through setup.

Instructors are writing their lectures as IPython notebooks, and distributing them to students, who then work through them in their JupyterHub environment.

Our most ambitious so far has been setting up each student in the course with a p2.xlarge machine with cuda and TensorFlow so they could do deep learning work for their final projects.

We supported 15 courses last year, and got deployment time for an implementation down to only 2-3 hours.

In conclusion, IPython good, JupyterHub good.

Edit: surfacing the link to the open source repo on GitHub https://github.com/harvard/cloudJHub

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