datalad.api.install(path=None, source=None, dataset=None, get_data=False, description=None, recursive=False, recursion_limit=None, save=True, reckless=None, jobs='auto')

Install a dataset from a (remote) source.

This command creates a local sibling of an existing dataset from a (remote) location identified via a URL or path. Optional recursion into potential subdatasets, and download of all referenced data is supported. The new dataset can be optionally registered in an existing superdataset by identifying it via the dataset argument (the new dataset’s path needs to be located within the superdataset for that).

It is recommended to provide a brief description to label the dataset’s nature and location, e.g. “Michael’s music on black laptop”. This helps humans to identify data locations in distributed scenarios. By default an identifier comprised of user and machine name, plus path will be generated.

When only partial dataset content shall be obtained, it is recommended to use this command without the get-data flag, followed by a ~datalad.api.get operation to obtain the desired data.


Power-user info: This command uses git clone, and git annex init to prepare the dataset. Registering to a superdataset is performed via a git submodule add operation in the discovered superdataset.


Install a dataset from Github into the current directory:

> install(source='')

Install a dataset as a subdataset into the current dataset:

> install(dataset='.',

Install a dataset, and get all content right away:

> install(source='',

Install a dataset with all its subdatasets:

> install(source='',
  • path – path/name of the installation target. If no path is provided a destination path will be derived from a source URL similar to git clone. [Default: None]
  • source (str or None, optional) – URL or local path of the installation source. [Default: None]
  • dataset (Dataset or None, optional) – specify the dataset to perform the install operation on. If no dataset is given, an attempt is made to identify the dataset in a parent directory of the current working directory and/or the path given. [Default: None]
  • get_data (bool, optional) – if given, obtain all data content too. [Default: False]
  • description (str or None, optional) – short description to use for a dataset location. Its primary purpose is to help humans to identify a dataset copy (e.g., “mike’s dataset on lab server”). Note that when a dataset is published, this information becomes available on the remote side. [Default: None]
  • recursive (bool, optional) – if set, recurse into potential subdataset. [Default: False]
  • recursion_limit (int or None, optional) – limit recursion into subdataset to the given number of levels. [Default: None]
  • save (bool, optional) – by default all modifications to a dataset are immediately saved. Giving this option will disable this behavior. [Default: True]
  • reckless ({None, True, False, 'auto', 'ephemeral'} or shared-..., optional) – set up the dataset in a potentially unsafe way for performance, or access reasons – use with care, any dataset is marked as ‘untrusted’. The reckless mode is stored in a dataset’s local configuration under ‘datalad.clone.reckless’, and will be inherited to any of its subdatasets. Supported modes are: [‘auto’]: hard-link files between local clones. In-place modification in any clone will alter original annex content. [‘ephemeral’]: symlink annex to origin’s annex and discard local availability info via git-annex- dead ‘here’. Shares an annex between origin and clone w/o git-annex being aware of it. In case of a change in origin you need to update the clone before you’re able to save new content on your end. Alternative to ‘auto’ when hardlinks are not an option, or number of consumed inodes needs to be minimized. [‘shared-<mode>’]: set up repository and annex permission to enable multi-user access. This disables the standard write protection of annex’ed files. <mode> can be any value support by ‘git init –shared=’, such as ‘group’, or ‘all’. [Default: None]
  • jobs (int or None or {'auto'}, optional) – how many parallel jobs (where possible) to use. “auto” corresponds to the number defined by ‘datalad.runtime.max-annex-jobs’ configuration item. [Default: ‘auto’]
  • on_failure ({'ignore', 'continue', 'stop'}, optional) – behavior to perform on failure: ‘ignore’ any failure is reported, but does not cause an exception; ‘continue’ if any failure occurs an exception will be raised at the end, but processing other actions will continue for as long as possible; ‘stop’: processing will stop on first failure and an exception is raised. A failure is any result with status ‘impossible’ or ‘error’. Raised exception is an IncompleteResultsError that carries the result dictionaries of the failures in its failed attribute. [Default: ‘continue’]
  • result_filter (callable or None, optional) – if given, each to-be-returned status dictionary is passed to this callable, and is only returned if the callable’s return value does not evaluate to False or a ValueError exception is raised. If the given callable supports **kwargs it will additionally be passed the keyword arguments of the original API call. [Default: <function is_result_matching_pathsource_argument at 0x7f37c763fae8>]
  • result_renderer ({'default', 'json', 'json_pp', 'tailored'} or None, optional) – format of return value rendering on stdout. [Default: None]
  • result_xfm ({'datasets', 'successdatasets-or-none', 'paths', 'relpaths', 'metadata'} or callable or None, optional) – if given, each to-be-returned result status dictionary is passed to this callable, and its return value becomes the result instead. This is different from result_filter, as it can perform arbitrary transformation of the result value. This is mostly useful for top- level command invocations that need to provide the results in a particular format. Instead of a callable, a label for a pre-crafted result transformation can be given. [Default: ‘successdatasets-or- none’]
  • return_type ({'generator', 'list', 'item-or-list'}, optional) – return value behavior switch. If ‘item-or-list’ a single value is returned instead of a one-item return value list, or a list in case of multiple return values. None is return in case of an empty list. [Default: ‘item-or-list’]