Python framework for production ML systems.
modelkit is a minimalist yet powerful MLOps library for Python, built for people who want to deploy ML models to production.
It packs several features which make your go-to-production journey a breeze, and ensures that the same exact code will run in production, on your machine, or on data processing pipelines.
modelkit provides a straightforward and consistent way to wrap your prediction code in a Model class:
from modelkit import Model
class MyModel(Model):
def _predict(self, item):
# This is where your prediction logic goes
...
return resultBe sure to check out our tutorials in the documentation.
Wrapping your prediction code in modelkit instantly gives acces to all features:
- fast Model predictions can be batched for speed (you define the batching logic) with minimal overhead.
- composable Models can depend on other models, and evaluate them however you need to
- extensible Models can rely on arbitrary supporting configurations files called assets hosted on local or cloud object stores
- type-safe Models' inputs and outputs can be validated by pydantic, you get type annotations for your predictions and can catch errors with static type analysis tools during development.
- async Models support async and sync prediction functions.
modelkitsupports calling async code from sync code so you don't have to suffer from partially async code. - testable Models carry their own unit test cases, and unit testing fixtures are available for pytest
- fast to deploy Models can be served in a single CLI call using fastapi
In addition, you will find that modelkit is:
- simple Use pip to install
modelkit, it is just a Python library. - robust Follow software development best practices: version and test all your configurations and artifacts.
- customizable Go beyond off-the-shelf models: custom processing, heuristics, business logic, different frameworks, etc.
- framework agnostic Bring your own framework to the table, and use whatever code or library you want.
modelkitis not opinionated about how you build or train your models. - organized Version and share you ML library and artifacts with others, as a Python package or as a service. Let others use and evaluate your models!
- fast to code Just write the prediction logic and that's it. No cumbersome pre or postprocessing logic, branching options, etc... The boilerplate code is minimal and sensible.
Install with pip:
pip install modelkit
Optional dependencies are available for remote storage providers (see documentation)
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