Michał Szczepanik
523fa30263
This fixes the usage of contraction it's (it is / it has) and possessive its, as far as I could grep.
64 lines
2.8 KiB
ReStructuredText
64 lines
2.8 KiB
ReStructuredText
.. _summary_containers:
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Summary
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-------
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The last two sections have first of all extended your knowledge on dataset nesting:
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- When subdatasets are created or installed, they are registered to the superdataset
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in their current version state (as identified by their most recent commit's hash).
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For a freshly created subdatasets, the most recent commit is at the same time its
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first commit.
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- Once the subdataset evolves, the superdataset recognizes this as a ``modification``
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of the subdatasets version state. If you want to record this, you need to
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:dlcmd:`save` it in the superdataset:
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.. code-block:: console
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$ datalad save -m "a short summary of changes in subds" <path to subds>
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But more than nesting concepts, they have also extended your knowledge on
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reproducible analyses with :dlcmd:`run` and you have experienced
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for yourself why and how software containers can go hand-in-hand with DataLad:
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- A software container encapsulates a complete software environment, independent
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from the environment of the computer it runs on. This allows you to create or
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use secluded software and also share it together with your analysis to ensure
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computational reproducibility. The DataLad extension
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`datalad containers <https://docs.datalad.org/projects/container>`_
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can make this possible.
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- The command :dlcmd:`containers-add` registers an :term:`container image` from a path or
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URL to your dataset.
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- If you use :dlcmd:`containers-run` instead of :dlcmd:`run`,
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you can reproducibly execute a command of your choice *within* the software
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environment.
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- A :dlcmd:`rerun` of a commit produced with :dlcmd:`containers-run`
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will re-execute the command in the same software environment.
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.. index::
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pair: hub; Docker
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Now what can I do with it?
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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For one, you will not be surprised if you ever see a subdataset being shown as
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``modified`` by :dlcmd:`status`: You now know that if a subdataset
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evolves, its most recent state needs to be explicitly saved to the superdataset's
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history.
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On a different matter, you are now able to capture and share analysis provenance that
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includes the relevant software environment. This does not only make your analyses
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projects automatically reproducible, but automatically *computationally* reproducible -
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you can make sure that your analyses runs on any computer with Singularity,
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regardless of the software environment on this computer. Even if you are unsure how you can wrap up an
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environment into a software :term:`container image` at this point, you could make use of
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hundreds of publicly available images on `Singularity-Hub <https://singularity-hub.org>`_ and
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`Docker-Hub <https://hub.docker.com>`_.
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With this, you have also gotten a first glimpse into an extension of DataLad: A
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Python module you can install with Python package managers such as ``pip`` that
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extends DataLad's functionality.
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