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pprint

Display all details about a specific MLEM object from an existing MLEM project.

Synopsis

usage: mlem pprint [options] path

arguments: PATH Path to object [required]

Description

All MLEM objects can be printed to view their metadata. This includes generic information such as requirements, type of object, hash, size, as well as object specific information such as methods for a model or reader for data.

Since only one specific object is printed, a PATH to the specific MLEM object is always required.

You can use mlem list to list MLEM objects.

Options

  • -p, --project TEXT: Path to MLEM project [default: (none)]
  • --rev TEXT: Repo revision to use [default: (none)]
  • -f, --follow-links: If specified, follow the link to the actual object.
  • --json: Output as json
  • --help: Show this message and exit.

Example: Showing local model

$ mlem pprint rf
⏳️ Loading meta from .mlem/model/rf.mlem
{'artifacts': {'data': {'hash': 'a61a1fa54893dcebe6fa448df81a1418',
                        'size': 163651,
                        'type': 'dvc',
                        'uri': 'rf'}},
 'model_type': {'methods': {'predict': {'args': [{'name': 'data',
                                                  'type_': {'columns': ['sepal '
                                                                        'length '
                                                                        '(cm)',
...

Example: Showing remote data

$ mlem pprint https://github.com/iterative/example-mlem-get-started/iris.csv --rev 4-pack
⏳️ Loading meta from https://github.com/iterative/example-mlem-get-started/tree/4-pack/.mlem/data/iris.csv.mlem
{'artifacts': {'data': {'hash': '45109f850511f9474665f2c26f4c79f3',
                        'size': 2470,
                        'uri': 'iris.csv'}},
 'data_type': {'columns': ['sepal length (cm)',
                           'sepal width (cm)',
                           'petal length (cm)',
                           'petal width (cm)'],
               'dtypes': ['float64', 'float64', 'float64', 'float64'],
               'index_cols': [],
               'type': 'dataframe'},
 'object_type': 'data',
 'reader': {'data_type': {'columns': ['sepal length (cm)',
                                      'sepal width (cm)',
                                      'petal length (cm)',
                                      'petal width (cm)'],
                          'dtypes': ['float64',
                                     'float64',
                                     'float64',
                                     'float64'],
                          'index_cols': [],
                          'type': 'dataframe'},
            'format': 'csv',
            'type': 'pandas'},
 'requirements': [{'module': 'pandas', 'version': '1.4.2'}]}
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