From cecb03995692f6797d02b83cfd88785b69456963 Mon Sep 17 00:00:00 2001 From: Marie-Gabrielle Dondon <85bc36e0a8096c618fbd5993d1cca191@app-learninglab.inria.fr> Date: Tue, 20 Nov 2018 20:41:26 +0000 Subject: [PATCH] Ajout liste des kernels Jupyter --- module4/ressources/resources_refs.org | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/module4/ressources/resources_refs.org b/module4/ressources/resources_refs.org index 718e0ba..d5aecee 100644 --- a/module4/ressources/resources_refs.org +++ b/module4/ressources/resources_refs.org @@ -41,12 +41,11 @@ careful about stability. We (the authors of this MOOC) believe that open source (which allows to inspect how computation is done and to identify both mistakes and sources of non-reproducibility) is more important than the rock solid stability of SAS, which is proprietary -software. Yet, if you really need to stay with SAS (similar solutions -probably exist for other languages as well), you should know that SAS -can be used within Jupyter using either the [[https://sassoftware.github.io/sas_kernel/][Python SASKernel]] or the -[[https://sassoftware.github.io/saspy/][Python SASPy]] package (step by step explanations about this are given -[[https://app-learninglab.inria.fr/gitlab/85bc36e0a8096c618fbd5993d1cca191/mooc-rr/blob/master/documents/tuto_jupyter_windows/tuto_jupyter_windows.md][here]]). Using such literate programming approach allied with systematic -version and environment control will always help. +software. Yet, if you really need to stay with SAS, you should know that SAS +can be used within Jupyter using the [[https://sassoftware.github.io/saspy/][Python SASPy]] and the +[[https://sassoftware.github.io/sas_kernel/][Python SASKernel]] packages (step by step explanations about this are given +[[https://app-learninglab.inria.fr/gitlab/85bc36e0a8096c618fbd5993d1cca191/mooc-rr/blob/master/documents/tuto_jupyter_windows/tuto_jupyter_windows.md#53-le-package-python-saspy-permet-dex%C3%A9cuter-du-code-sas-dans-un-notebook-python][here]]). Using such literate programming approach allied with systematic +version and environment control will always help. Similar solutions exist for many languages ([[https://github.com/jupyter/jupyter/wiki/Jupyter-kernels][list of Jupyter kernels]]). * Controlling your software environment As we mentioned in the video sequences, there are several solutions to control your environment: @@ -80,11 +79,11 @@ whenever the data is not sensitive. * Workflows In the video sequences, we mentioned workflow managers (original application domain in parenthesis): - [[https://galaxyproject.org/][Galaxy]] (genomics), [[https://kepler-project.org/][Kepler]] (ecology), [[https://taverna.apache.org/][Taverna]] (bio-informatics), [[https://pegasus.isi.edu/][Pegasus]] - (astronomy), [[http://cknowledge.org/][Collective Knowledge]] (compiling optimization) , + (astronomy), [[http://cknowledge.org/][Collective Knowledge]] (compiling optimization), [[https://www.vistrails.org][VisTrails]] (image processing) - Light-weight: [[http://dask.pydata.org/][dask]] (python), [[https://ropensci.github.io/drake/][drake]] (R), [[http://swift-lang.org/][swift]] (molecular biology), - [[https://snakemake.readthedocs.io/][snakemake]] (like =make= but more expressive and in =python=) ... -- Hybrids: [[https://vatlab.github.io/sos-docs/][SOS-notebook]], ... + [[https://snakemake.readthedocs.io/][snakemake]] (like =make= but more expressive and in =python=)... +- Hybrids: [[https://vatlab.github.io/sos-docs/][SOS-notebook]]... You may want to have a look at this webinar: [[https://github.com/alegrand/RR_webinars/blob/master/6_reproducibility_bioinformatics/index.org][Reproducible Science in Bio-informatics: Current Status, Solutions and Research Opportunities -- 2.18.1