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MLFlow Toolchain Training Integration

Support official
Language Python 3.6+

Legion provides officially supported :term:`Toolchain Train Integration` with MLflow - Legion MLflow.

Installation instructions are available on official GitHub page.

This integration allows to train models, written in Python with usage of MLflow API, and to convert them into :term:`General Python Prediction Interface` (that is :term:`Trained Model Binary Format`).

Warning

To use train models, written using these integration, please ensure, that appropriate :term:`Toolchain Train Integration` has been installed on a platform (for cloud usage).

Installation instructions are available on official GitHub page.

Limitations

  • Legion supports all model flavours, that have Python flavor (e.g. keras, sklearn and etc.). MLeap flavour is not supported;
  • Only Python programming language version 3 is supported;
  • MLproject has to use conda environment management, all required packages (python and system) has to be declared in conda environment file;
  • Training code may save only one model in one MLproject entry point, otherwise exception will be raised;
  • To allow using names of input / output columns, artifacts named head_input.pkl and head_output.pkl have to be saved in artifact's folder;
  • Legion executes exact one entry point during :term:`Model Training process`;
  • Direct usage of MLflow client inside model training code is undesirable;