Provide a generic mlflow pyfunc wrapper
Nobody has claimed this yet.
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
- Domain
- machine-learning
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
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
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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