import

Create a .mlem MLEM Object for a model or data in any file or directory.

Synopsis

usage: mlem import [-p <path>] [--rev <commitish>]
                   [--tp <path>] [--copy] [--type <text>] [-h]
                   uri target

arguments:
  uri              File to import
  target           Path to save MLEM object

Description

Use import on an existing data or model files (or directories) to generate the necessary .mlem metafiles for them. This is useful to quickly make existing data and model files compatible with MLEM, which then can be used in future operations such as mlem apply.

This command provides a quick and easy alternative to writing Python code to load those models/datasets into object for subsequent usage in MLEM context.

Options

• -p <path>, --project <path> - Path to MLEM project [default: (none)]
• --rev <commitish> - Repo revision to use [default: (none)]
• --tp <path>, --target-project <path> - Project to save target to [default: (none)]
• --copy / --no-copy - Whether to create a copy of file in target location or just link existing file [default: copy]
• --type <text> - Specify how to read file Available types: ['pandas', 'pickle', 'torch'] [default: (auto infer)]
• -h, --help - Show this message and exit.

Examples

Create a MLEM dataset from a local .csv file

$ mlem import data/data.csv data/imported_data --type pandas[csv]
...

Create a MLEM model from local .pkl (pickle) file

$ mlem import data/model.pkl data/imported_model
...

Create a MLEM model from remote .pkl (pickle) file

$ mlem import models/rf \
    --project https://github.com/iterative/example-mlem-get-started \
    --rev main \
    data/imported_model \
    --type pickle
💾 Saving model to data/imported_model.mlem