ServeIt is an open source library that lets you easily serve model predictions and supplementary information from a RESTful API on any domain using your favorite Python ML library. This post illustrates the process of deploying a pre-trained ImageNet classifier with ServeIt to a new API. The classifier accepts an image URL as a parameter in a POST request and responds with the top predicted classes for that image.
You can interact with a live DenseNet121 demo server at
https://imagenet-keras.ryanlee.site/predictions (source code and request examples can be found here) or follow along below to start serving your own classifier.
Pre-trained DenseNet121 serving using ServeIt
Let’s start by installing the dependencies listed here (
pip install -r requirements.txt, in a virtual environment). After our dependencies are installed, we’ll load a DenseNet121 model that has been pre-trained for us on the ImageNet dataset:
1 2 3 4 from keras.applications import densenet # load DenseNet121 model pre-trained on ImageNet model = densenet.DenseNet121(weights='imagenet')
Next we define methods for loading and preprocessing an image from a URL…
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 from keras.preprocessing import image from flask import request import requests from serveit.utils import make_serializable, get_bytes_to_image_callback # define a loader callback for the API to fetch the relevant data and # preprocessor callbacks to map to a format expected by the model def loader(): """Load image from URL, and preprocess for DenseNet.""" url = request.args.get('url') # read image URL as a request URL param return requests.get(url).content # return image as bytes # get a bytes-to-image callback to resize image to 224x224 bytes_to_image = get_bytes_to_image_callback(image_dims=(224, 224)) # create a list of different preprocessors to chain multiple steps preprocessor = [bytes_to_image, densenet.preprocess_input]
…and now we’re ready to start serving our image classifier:
1 2 3 4 5 6 7 8 9 from serveit.server import ModelServer # initialize a ModelServer server = ModelServer( model=model, predict=model.predict, data_loader=loader, preprocessor=preprocessor, postprocessor=densenet.decode_predictions) # start serving server.serve()
1 2 curl -XPOST 'localhost:5000/predictions?url=https://images.pexels.com/photos/96938/pexels-photo-96938.jpeg' # [[["n02123045", "tabby", 0.69397], ["n02123159", "tiger_cat", 0.14688], ["n02124075", "Egyptian_cat", 0.05430], ...]]
1 2 curl -XPOST 'localhost:5000/predictions?url=https://images.pexels.com/photos/67807/plane-aircraft-take-off-sky-67807.jpeg' # [[["n02690373", "airliner", 0.60765], ["n04592741", "wing", 0.22114], ["n04552348", "warplane", 0.14071], ...]]
Additional examples can be found here. Happy serving!