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