Introduction
MLup is a library for easy and fast running of ML models in production.
All you need is to deliver the model file to the server (a config is optional) — pymlup turns it into a FastAPI web application with one CLI command. No web app code to write or maintain.
- Pure Python, no required framework-specific glue code;
- Uses FastAPI for the web layer;
- Works with any Python object that exposes a
predict-like method, plus native (de)serialization support for scikit-learn, lightgbm, tensorflow, torch and onnx models.
Requirements
Python 3.8+ (3.12, 3.13, 3.14 supported; the tensorflow extra requires Python <3.14 until TensorFlow publishes 3.14 wheels).
Installation
pip install pymlup
With an ML backend extra:
pip install "pymlup[scikit-learn]" # For scikit-learn
pip install "pymlup[lightgbm]" # For microsoft lightgbm
pip install "pymlup[tensorflow]" # For tensorflow
pip install "pymlup[torch]" # For torch
pip install "pymlup[onnx]" # For onnx models: torch, tensorflow, sklearn, etc...
Quick start
The easiest way to try it, from a model file on disk:
mlup run -m /path/to/my/model.onnx
Or from Python, with any object that has a predict-like method:
import mlup
class MyAnyModelForExample:
def predict(self, X):
return X
up = mlup.UP(ml_model=MyAnyModelForExample())
up.ml.load()
# You can open your browser at http://localhost:8009/docs for interactive API docs (Swagger UI)
up.run_web_app(daemon=True)
import requests
response = requests.post('http://0.0.0.0:8009/predict', json={'X': [[1, 2, 3], [4, 5, 6]]})
print(response.json())
up.stop_web_app()
Supported ML frameworks
Work tested with machine learning model frameworks (links to tests):
- scikit-learn>=1.2.0,<2.0.0
- tensorflow>=2.0.0,<3.0.0, Python<3.14
- lightgbm>=4.0.0,<5.0.0
- torch>=2.0.0,<3.0.0
- onnx>=1.0.0,<2.0.0
- onnxruntime>=1.14.0,<1.26.0
Support and tested with machine learning libraries:
Documentation
The full documentation — Python API, CLI reference, config file format, storages, binarizers, data transformers, web app architectures/API, application life cycle — lives at mlup.org (source: docs/).
Useful links
Metrics
MLup PyPi download statistics: https://pepy.tech/project/pymlup
1 maintainer
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