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I just did this in my University lab as well. Most people aren't savvy with Linux, so having normal accounts with Jupyter port forwarding is out of the question

I just did this in my University lab as well. Most people aren't savvy with Linux, so having normal accounts with Jupyter port forwarding is out of the question. JupyterHub is just about the lowest friction I can possibly make it for introducing the Python data science stack to non data scientists.

rhizome31 7y ago
And just to be explicit for readers, Jupyter and JupyterHub also allow to work with other data science stacks, R in particular.
amrrs 7y ago
IMO, Jupyter Notebook is the closest equivalent to Python as what Rstudio is for R. While Pycharm and VSCode are also preferred by some Py-based Data scientists, Jupyterhub offers almost everything that a typical IDE would do along with the traditional Notebook environment which a lot of beginners these days start with. Thus much less friction while getting started.
- prakhar987 7y ago
  I would be really hesitant to comapre Jupyter Notebook to an IDE.....an example is a debugger...the only visual debugger that i have come across for jupyter is pixie debugger, which is miles behind the debugger of an IDE like Pycharm.... there is a huge list of features that jupyter needs before you can compare it to an IDE
  - d0mine 7y ago
    It is an interactive environment (not much use for a debugger).
    - erikgaas 7y ago
      FWIW I use the %debug magic command in Jupyter and it has been a great experience. I'm pretty ignorant of the enterprise debugging tools so take that with a grain of salt.
      - y4mi 7y ago
        Debuggers are only really useful if you're trying to figure out why some object in your server doesn't do what you want it to. I'd wager that almost no data scientists write object oriented code.. it's probably mostly done one calculation at a time. executed in the notebooks repl. So the value you get from ide debuggers is tiny, as you're already doing everything one step at a time.
        bonoboTP 7y ago
        You still write functions and may want to inspect variable state in the middle of function execution.
        applecrazy 7y ago
        Correct. RStudio has this feature, where variable values can be inspected in a sidebar. This would be a really useful feature for Jupyter, especially when running a Python kernel.
        timdumol 7y ago
        There is a JupyterLab extension for that: https://github.com/lckr/jupyterlab-variableInspector https://github.com/lckr/jupyterlab-variableInspector
        bonoboTP 7y ago
        Does it work with variables that are local to a function? I don't mean inspecting global variables after having executed a cell, but local variables in the middle of a function execution.
- d0mine 7y ago
  Org-mode + jupyter-python https://github.com/dzop/emacs-jupyter#org-mode-source-blocks https://github.com/dzop/emacs-jupyter#org-mode-source-blocks

rhizome31 7y ago

And just to be explicit for readers, Jupyter and JupyterHub also allow to work with other data science stacks, R in particular.

amrrs 7y ago

IMO, Jupyter Notebook is the closest equivalent to Python as what Rstudio is for R. While Pycharm and VSCode are also preferred by some Py-based Data scientists, Jupyterhub offers almost everything that a typical IDE would do along with the traditional Notebook environment which a lot of beginners these days start with. Thus much less friction while getting started.

prakhar987 7y ago
I would be really hesitant to comapre Jupyter Notebook to an IDE.....an example is a debugger...the only visual debugger that i have come across for jupyter is pixie debugger, which is miles behind the debugger of an IDE like Pycharm.... there is a huge list of features that jupyter needs before you can compare it to an IDE
- d0mine 7y ago
  It is an interactive environment (not much use for a debugger).
  - erikgaas 7y ago
    FWIW I use the %debug magic command in Jupyter and it has been a great experience. I'm pretty ignorant of the enterprise debugging tools so take that with a grain of salt.
    - y4mi 7y ago
      Debuggers are only really useful if you're trying to figure out why some object in your server doesn't do what you want it to. I'd wager that almost no data scientists write object oriented code.. it's probably mostly done one calculation at a time. executed in the notebooks repl. So the value you get from ide debuggers is tiny, as you're already doing everything one step at a time.
      - bonoboTP 7y ago
        You still write functions and may want to inspect variable state in the middle of function execution.
        applecrazy 7y ago
        Correct. RStudio has this feature, where variable values can be inspected in a sidebar. This would be a really useful feature for Jupyter, especially when running a Python kernel.
        timdumol 7y ago
        There is a JupyterLab extension for that: https://github.com/lckr/jupyterlab-variableInspector https://github.com/lckr/jupyterlab-variableInspector
        bonoboTP 7y ago
        Does it work with variables that are local to a function? I don't mean inspecting global variables after having executed a cell, but local variables in the middle of a function execution.
d0mine 7y ago
Org-mode + jupyter-python https://github.com/dzop/emacs-jupyter#org-mode-source-blocks https://github.com/dzop/emacs-jupyter#org-mode-source-blocks