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


usage: mlem import [options] uri target

URI     File to import  [required]
TARGET  Path to save MLEM object  [required]


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.


  • -p, --project TEXT: Path to MLEM project [default: (none)]
  • --rev TEXT: Repo revision to use [default: (none)]
  • --target-project, --tp TEXT: 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'] [default: (auto infer)]
  • --index / --no-index: Whether to index output in .mlem directory
  • -e, --external: Save result not in .mlem, but directly in project
  • -h, --help: Show this message and exit.


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

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