Simple API serving for Python ML models

Serve model predictions from a RESTful API using your favorite Python ML library with ease

 from serveit.server import ModelServer
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
 
# fit logistic regression on Iris data
clf = LogisticRegression()
data = load_iris()
clf.fit(data.data, data.target)
 
# initialize server with a model and start serving predictions
ModelServer(clf, clf.predict).serve()

Easy serving

Painless model inference serving via RESTful API endpoint

Extensible

Customize preprocessing, validation, and postprocessing pipeline

Automatic

Automatic JSON serialization, logging, and model info parsing

Which ML libraries are supported?

Currently three of the most popular Python ML libraries are supported. Interested in seeing others incoporated? Open up a pull request!

Scikit-learn
Keras
PyTorch

	from serveit.server import ModelServer
	from sklearn.linear_model import LogisticRegression
	from sklearn.datasets import load_iris

	# fit logistic regression on Iris data
	clf = LogisticRegression()
	data = load_iris()
	clf.fit(data.data, data.target)

	# initialize server with a model and start serving predictions
	ModelServer(clf, clf.predict).serve()