ServeIt 0.0.2a6

Machine learning prediction serving

ServeIt deploys your trained models to a RESTful API for prediction serving. Current features include:

1. Model prediction serving

2. Supplementary information endpoint creation

3. Input validation and exception handling

4. Configurable request and response logging (work in progress)

Installation: Python 2.7 and Python 3.6

• PyPi: pip install serveit

• source: python setup.py

Supported libraries

• Scikit-Learn

Fit Scikit-Learn model:

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

# fit a model on the Iris dataset
data = load_iris()
reg = LogisticRegression()
reg.fit(data.data, data.target)

Serve your trained model:

from serveit.sklearn_server import SklearnServer

# initialize server
sklearn_server = SklearnServer(reg, reg.predict)

# add (optional) informational endpoints
sklearn_server.create_model_info_endpoint()
sklearn_server.create_info_endpoint('features', data.feature_names)
sklearn_server.create_info_endpoint('target_labels', data.target_names.tolist())

# start serving predictions from API
sklearn_server.serve()

Then try out your new API:

curl -XPOST 'localhost:5000/predictions'\
    -H "Content-Type: application/json"\
    -d "[[5.6, 2.9, 3.6, 1.3], [4.4, 2.9, 1.4, 0.2], [5.5, 2.4, 3.8, 1.1], [5.0, 3.4, 1.5, 0.2], [5.7, 2.5, 5.0, 2.0]]"
# [1, 0, 1, 0, 2]

curl -XGET 'localhost:5000/info/model'
# {"penalty": "l2", "tol": 0.0001, "C": 1.0, "classes_": [0, 1, 2], "coef_": [[0.4150, 1.4613, -2.2621, -1.0291], ...], ...}

curl -XGET 'localhost:5000/info/features'
# ["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]

curl -XGET 'localhost:5000/info/target_labels'
#  ["setosa", "versicolor", "virginica"]

Coming soon:

• TensorFlow

• Keras

• PyTorch