This repository contains the code for Building a simple Keras + deep learning REST API, published on the Keras.io blog.
The method covered here is intended to be instructional. It is not meant to be production-level and capable of scaling under heavy load. If you're interested in a more advanced Keras REST API that leverages message queues and batching, please refer to this tutorial.
For an even more advanced version that includes deploying a model to production, refer to this blog post.
I assume you already have Keras (and a supported backend) installed on your system. From there you need to install Flask and requests:
$ pip install flask gevent requestsNext, clone the repo:
$ git clone https://github.com/jrosebr1/simple-keras-rest-api.gitBelow you can see the image we wish to classify, a dog, but more specifically a beagle:
The Flask + Keras server can be started by running:
$ python run_keras_server.py
Using TensorFlow backend.
* Loading Keras model and Flask starting server...please wait until server has fully started
...
* Running on http://127.0.0.1:5000You can now access the REST API via http://127.0.0.1:5000.
Requests can be submitted via cURL:
$ curl -X POST -F [email protected] 'http://localhost:5000/predict'
{
"predictions": [
{
"label": "beagle",
"probability": 0.9901360869407654
},
{
"label": "Walker_hound",
"probability": 0.002396771451458335
},
{
"label": "pot",
"probability": 0.0013951235450804234
},
{
"label": "Brittany_spaniel",
"probability": 0.001283277408219874
},
{
"label": "bluetick",
"probability": 0.0010894243605434895
}
],
"success": true
}Or programmatically:
$ python simple_request.py
1. beagle: 0.9901
2. Walker_hound: 0.0024
3. pot: 0.0014
4. Brittany_spaniel: 0.0013
5. bluetick: 0.0011
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