# modelkit
**Repository Path**: mirrors_pquentin/modelkit
## Basic Information
- **Project Name**: modelkit
- **Description**: Toolkit for developing and maintaining ML models
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-07
- **Last Updated**: 2026-05-24
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
modelkit
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.
## Quickstart
`modelkit` provides a straightforward and consistent way to wrap your prediction code in a `Model` class:
```python
from modelkit import Model
class MyModel(Model):
def _predict(self, item):
# This is where your prediction logic goes, use any asset loaded by
# _load here
...
return result
```
Be sure to check out our tutorials in the [documentation](https://clustree.github.io/modelkit/).
## Features
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](https://pydantic-docs.helpmanual.io/), 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. `modelkit` supports 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](https://docs.pytest.org/en/6.2.x/)
- **fast to deploy** Models can be served in a single CLI call using [fastapi](https://fastapi.tiangolo.com/)
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. `modelkit` is 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.
## Installation
Install with `pip`:
```
pip install modelkit
```