Aleksandra, I have never encountered issues using conda and Jupyter notebooks of the kind you describe. But this long thread confirms that you are not alone.
https://github.com/jupyter/notebook/issues/2359 I will try to add a call out box with a best practice for installing jupyter notebook and juypterlab etc. Thanks for clarifying! Sent from my iPhone > On Jun 12, 2019, at 18:20, Giuseppe Profiti <profgiuseppe+...@gmail.com> > wrote: > > First, good job David. > Aleksandra: there are few things to consider when using conda and jupyter. > Just recently we managed to deploy a jupyterhub on a computing cluster, along > with several different conda environments. > Long story short: you should register the environment kernel in the jupyter > instance. I hope my boss let me write a blog post about it soon. > > Best, > Giuseppe > >> Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov >> <atara...@ucdavis.edu> ha scritto: >> David, to answer your question, the reason I stopped using conda and >> switched to pip installs was that I'd conda install jupyter and conda >> install packages, but then when I tried to run them, jupyter notebooks >> couldn't find the package. I'm probably making some very basic error here, >> but I'm also likely not the only one confused about this. >> >> Thanks again for making this great resource. >> >>> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan <msara...@gmail.com> wrote: >>> That's a good point, but rather than say "don't use conda at all" - that's >>> more reason to have custom channels where conda is set up to comply with >>> those needs. Conda need not be mutually exclusive with these things, but >>> it does take some setup to get them working together. >>> >>> Saying "don't use conda at all" is ignoring the work that has to happen >>> either way. Either you have to reproduce what conda is providing somehow, >>> or you have to make conda use the part on the system side. That's >>> definitely a case-by-case scenario for everyone, and we need to document >>> both paths. >>> >>> For your example of MPI, conda packages are setup to explicitly require >>> some MPI implementation where necessary. That package can come from an >>> actual conda MPICH package, or it can come from a known binary compatible >>> system installation that has a conda package setup to reference it. Conda >>> is not dogmatic about being hermetic (unlike, say, bazel). Binary >>> compatibility with external libraries can be pretty tricky, though. >>> >>>> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault >>>> <maxime.boissonnea...@calculquebec.ca> wrote: >>>> Hi, >>>> How about including a part about when *not* to use Conda ? >>>> >>>> In particular, if they are going to be computing on a supercomputer, they >>>> should consult with your cluster specialists first. >>>> Conda works well on somebody's desktop, but it creates a lot of problems >>>> on supercomputers, because it does crazy stuff like installing MPI by >>>> itself instead of relying on staff-installed modules and software packages. >>>> >>>> Cheers, >>>> >>>> Maxime >>>> >>>> >>>>> On 2019-06-12 9:49 AM, David Pugh wrote: >>>>> All, >>>>> >>>>> I have developed a Software Carpentry style lesson for Conda and would be >>>>> keen to get feedback from the community! >>>>> >>>>> Website: >>>>> >>>>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/ >>>>> >>>>> Repo: >>>>> >>>>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists >>>>> >>>>> Thanks and look forward to hearing from you! >>>>> >>>>> David > > The Carpentries / discuss / see discussions + participants + delivery options > Permalink ------------------------------------------ The Carpentries: discuss Permalink: https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M83665a3db57a07f7da5acb8d Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription
