Merge branch 'main' into commas-examples

docs/basics/101-133-containersrun.rst

@@ -38,7 +38,7 @@ that can link computational environments to datasets, the
 extension.
 
 This section will give a quick overview on what containers are and
-demonstrate how ``datalad-containers`` helps to capture full provenance of an
+demonstrate how ``datalad-container`` helps to capture full provenance of an
 analysis by linking containers to datasets and analyses.
 
 .. index::
@@ -97,7 +97,7 @@ Both of these tools share core terminology:
   `Singularity-Hub <https://singularity-hub.org>`_,
   `Docker-Hub <https://hub.docker.com>`_, and `Amazon ECR <https://aws.amazon.com/ecr>`_ which hosts Docker Images.
 
-Note that as of now, the ``datalad-containers`` extension supports
+Note that as of now, the ``datalad-container`` extension supports
 Singularity and Docker images.
 Singularity furthermore is compatible with Docker -- you can use
 Docker Images as a basis for Singularity Images, or run Docker Images with
@@ -391,7 +391,7 @@ the most recent state of the subdataset to the superdataset ``DataLad-101``.
    $ datalad save -d . -m "add container and execute analysis within container" midterm_project
 
 
-Software containers, the ``datalad-containers`` extension, and DataLad thus work well together
+Software containers, the ``datalad-container`` extension, and DataLad thus work well together
 to make your analysis completely reproducible -- by not only linking code, data,
 and outputs, but also the software environment of an analysis. And this does not
 only benefit your future self, but also whomever you share your dataset with, as

docs/code_from_chapters/MPI_code.rst

@@ -305,7 +305,7 @@ In order to be computationally reproducible I need to attach the software that i
    cd ../myanalysis
 
 And the way I can do this is with a datalad extension called datalad containers.
-You can install this extension with pip by running ``pip install datalad-containers``.
+You can install this extension with pip by running ``pip install datalad-container``.
 This extension allow to attach software containers such as singularity images to my dataset and execute my commands inside of these containers.
 Thus, I can share share data, code, code execution, and software.
 

docs/intro/windows.rst

@@ -25,7 +25,8 @@ Beyond this, Windows uses a different file system than Unix based systems.
 Given that DataLad is a data management software, it is heavily affected by this, and the Basics part of the handbook is filled with "Windows-Wits", dedicated sections that highlight different behavior on native Windows installations of DataLad, or provide adjusted commands -- nevertheless, standard DataLad operations on Windows can be much slower than on other operating systems.
 
 A major annoyance and problem is that some tools that DataLad or :term:`datalad extension`\s use are not available on Windows.
-If you are interested in adding :term:`software container`\s to your DataLad dataset (with the ``datalad-container`` extension) for example, you will likely not be able to do so on a native Windows computer -- :term:`Singularity`, a widely used containerization software, doesn't exit for Windows, and while there *is* some support for :term:`Docker` on Windows, it does not apply to most private computers [#f1]_.
+If you are interested in adding :term:`software container`\s to your DataLad dataset (with the ``datalad-container`` extension), for example, you won't be able to use :term:`Singularity`, a widely used containerization software.
+There is, however, support for :term:`Docker` on Windows.
 
 Windows also has insufficient support for :term:`symlink`\ing and locking files (i.e., revoking write :term:`permissions`), which alters how :term:`git-annex` works, and may make interoperability of datasets between Windows and non-Windows operating systems not as smooth as between various flavors of Unix-like operating systems.
 
@@ -54,7 +55,6 @@ We can adapt to limitations, but in many cases it is not possible to overcome th
 That sucks, and we're really sorry for this.
 It's not that we pick dependencies that only work on Unix-based systems -- we try to use tools that are as cross-platform-compatible as possible, but certain tools, functions, or concepts simply don't (yet) work on Windows:
 
-- As there is no way to install :term:`Singularity` or :term:`Docker` on regular Windows machines, none of the functionality that the ``datalad-container`` extension provides can be used.
 - As there is insufficient support for symlinking and locking, datasets will have a higher disk usage on Windows machines. Section :ref:`symlink` has the details on this.
 - The Windows terminals are much less user friendly, and errors that are thrown on Windows systems are typically much more complex.
 - DataLad and its underlying tools are slower on Windows.
@@ -163,8 +163,6 @@ Take a look at user forums such as `forums.linuxmint.com <https://forums.linuxmi
 
 .. rubric:: Footnotes
 
-.. [#f1] If you are thinking, "Well, why would you use :term:`Singularity`, :term:`Docker` is available on Windows!": True, and ``datalad-container`` can indeed use Docker. But Docker can only be installed on Windows Pro or Enterprise, but not on Windows Home. Eh. :(
-
 .. [#f2] The path length limitation on Windows is the reason that DataLad datasets always use hashes based on `MD5 <https://en.wikipedia.org/wiki/MD5>`_, a hash function that produces a 32 character hash digest value. This wouldn't be necessary on Unix-based operating systems, but is required to ensure portability of datasets to Windows computers.
 
 .. [#f3] The path length limitation certainly isn't only a problem for DataLad and its underlying tools. Many users run into a Path length related problems at least once, by accident. Downloading or copying files with long names into a folder that itself has a long name, for example, can become an unexpected issue (especially if you are not aware of the limit). Imagine transferring pictures from your friends camera into ``C:\Users\"Bob McBobface"\Desktop\Pictures\"Vacation Pictures"\2020\Saint-Remy-en-Bouzemont-Saint-Genest-et-Isson\"From Alice and Sasha"\Camera\`` -- those file names shouldn't be too long to fit in the limit. Likewise, when ``git clone``\ing a :term:`Git` repository that was created on a Unix computer and contains very long file names could fail.

docs/usecases/ml-analysis.rst

@@ -56,8 +56,8 @@ Step-by-Step
 .. admonition:: Required software
 
    The analysis requires the Python packages `scikit-learn <https://scikit-learn.org>`_, `scikit-image <https://scikit-image.org>`_, `pandas <https://pandas.pydata.org>`_, and `numpy <https://numpy.org>`_.
-   We have build a :term:`Singularity` :term:`software container` with all relevant software, and the code below will use the ``datalad-containers`` extension [#f1]_ to download the container from :term:`Singularity-Hub` and execute all analysis in this software environment.
-   If you do not want to install the ``datalad-containers`` extension or Singularity, you can also create a :term:`virtual environment` with all necessary software if you prefer [#f2]_, and exchange the ``datalad containers-run`` commands below with ``datalad run`` commands.
+   We have build a :term:`Singularity` :term:`software container` with all relevant software, and the code below will use the ``datalad-container`` extension [#f1]_ to download the container from :term:`Singularity-Hub` and execute all analysis in this software environment.
+   If you do not want to install the ``datalad-container`` extension or Singularity, you can also create a :term:`virtual environment` with all necessary software if you prefer [#f2]_, and exchange the ``datalad containers-run`` commands below with ``datalad run`` commands.
 
 Let's start with an overview of the analysis plans:
 We're aiming for an image classification analysis.
@@ -570,7 +570,7 @@ The analysis is adapted from the chapter :ref:`dvc`, which in turn is based on `
 
 .. rubric:: Footnotes
 
-.. [#f1] You can install the ``datalad-containers`` extension from :term:`pip` via ``pip install datalad-container``. You can find out more about extensions in general in the section :ref:`extensions_intro`, and you can more computationally reproducible analysis using ``datalad container`` in the chapter :ref:`containersrun` and the use case :ref:`usecase_reproduce_neuroimg`.
+.. [#f1] You can install the ``datalad-container`` extension from :term:`pip` via ``pip install datalad-container``. You can find out more about extensions in general in the section :ref:`extensions_intro`, and you can more computationally reproducible analysis using ``datalad container`` in the chapter :ref:`containersrun` and the use case :ref:`usecase_reproduce_neuroimg`.
 
 .. [#f2] Unsure how to create a :term:`virtual environment`? You can find a tutorial using :term:`pip` and the ``virtualenv`` module `in the Python docs <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments>`_.