Provide a generic mlflow pyfunc wrapper

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python, pytorch

Research direction

Start by reviewing the package's model and artifact interfaces alongside MLflow's pyfunc wrapper expectations. Define the stored artifact assumptions and the inference dependencies before deciding on the generic wrapper. Done means package users can use the wrapper for registered text classifiers, with the standalone torch-and-numpy inference option evaluated separately.

Written by the indexing model from the issue text.

Description

enhancement

When using mlflow to train and register a text classifier model using the package, we often have to write our own pyfunc wrapper, which could be tedious.

The idea would be to provide a generic wrapper (with some hypothesis on the stored artifact) usable by all package users.

As a bonus, it could be really great if this wrapper was self sufficient (no need to install the package for inference, only torch and numpy)

Dominant language
Python
Stars
23
Forks
7
PR merge metrics
No merged PRs in 30d

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