[IPython-dev] Distributing evaluated notebooks that require big-ish data
tritemio at gmail.com
Wed May 21 17:54:33 EDT 2014
thanks for the reply. Your approach is very compelling indeed. I'll try it
whenever I switch to an own server to build the docs.
For now I'll stick to ReadTheDoc and I'll manually run the notebooks for
testing/updating. I'll try to do not update the images too frequently so
that the repository will not explode.
On Mon, May 19, 2014 at 12:16 AM, Nathan Goldbaum <nathan12343 at gmail.com>wrote:
> For the yt documentation  we do this using a jenkins buildbot and a
> sphinx extension that I wrote called RunNotebook . To follow this route
> you'll need to host your own docs builds on your project's website and also
> have a server that can dynamically generate the docs builds. If you don't
> need to build the docs with each commit to your codebase, you could also
> generate the docs manually as part of your release process.
> A nice bonus to incorporating notebooks into your docs in this fashion is
> that the notebooks serve as a form of testing: broken code in the docs
> leads to broken results embedded in the docs build.
>  http://yt-project.org/doc, see e.g. the yt bootcamp at
>  https://github.com/ngoldbaum/runnotebook
> On Fri, May 16, 2014 at 12:24 PM, Antonino Ingargiola <tritemio at gmail.com>wrote:
>> I'm working on a software for single-molecule FRET analysis
>> (FRETBursts) that heavily relies on ipython notebook to run the analysis.
>> I provide some evaluated notebooks serving as tutorials in a separate
>> repository (FRETBursts_notebooks). The reference documentation (mostly
>> installation instructions and API) is hosted on ReadTheDocs .
>> I have concerns on the viability of this approach since the notebooks
>> repository can easily grow to hundreds of MB given the high number of
>> images. Maintaining a second repository it is also and additional burden.
>> Ideally I would like to generate the notebooks dynamically from
>> unevaluated notebooks in the original source repository. However the data
>> necessary to reproduce the analysis is ~150MB (hosted on figshare ),
>> and I may add more datasets in the future.
>> So I'm asking for suggestions.
>> I like the simple concept of a notebook repository with links to
>> nbviewer, but seems that the solution is not scalable.
>> I don't think RTD can handle downloading and data processing that
>> requires several minutes to execute on modern desktops.
>> So, what's left? Anybody has a similar issue?
>>  FRETBursts: https://github.com/tritemio/FRETBursts
>>  FRETBursts_notebooks:
>>  FRETBursts documentation:
>>  FRETBursts datatsets: http://dx.doi.org/10.6084/m9.figshare.1019906
>> IPython-dev mailing list
>> IPython-dev at scipy.org
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