deepapi 0.4.3

Deep Learning as a Cloud API Service.

Deep API

Deep Learning as Cloud APIs.

This project provides an image classification cloud service for research on Black-box Adversarial Attacks.

Quick Start

Using Docker:

docker run -p 8080:8080 wuhanstudio/deepapi

Python 3:

$ pip install deepapi

# For development
# python setup.py develop

# By default, we enable all models on the server.
# Use deepapi -h to see more options.

$ python -m deepapi
Serving on port 8080...

The website and API service are available at https://localhost:8080.

DeepAPI Client

To initiate black-box adversarial attacks, we can get predictions from a cloud API using model.predict().

Behind the scene, this model makes predictions by sending a POST request to http://localhost:8080/vgg16_cifar10.

import numpy as np
from PIL import Image

from deepapi.api import DeepAPI_VGG16_Cifar10

# Load the image
x = Image.open("dog.jpg")
x = np.array(x)

# Initialize the model
model  = DeepAPI_VGG16_Cifar10('http://localhost:8080', concurrency=8)

# Predict
y = model.predict(np.array([x]))[0]

# Print the result
model.print(y)

Using Curl

export IMAGE_FILE=test/cat.jpg
(echo -n '{"file": "'; base64 $IMAGE_FILE; echo '"}') | \
curl -H "Content-Type: application/json" \
     -d @- http://127.0.0.1:8080/vgg16_cifar10

Using Python Request

You can also implement the API client from scratch using the request module.

def classification(url, file):
    # Load the input image and construct the payload for the request
    image = Image.open(file)
    buff = BytesIO()
    image.save(buff, format="JPEG")

    data = {'file': base64.b64encode(buff.getvalue()).decode("utf-8")}
    return requests.post(url, json=data).json()

res = classification('http://127.0.0.1:8080/vgg16_cifar10', 'cat.jpg')

This python script is available in the test folder. You should see prediction results by running python3 minimal.py:

cat            0.99804
deer           0.00156
truck          0.00012
airplane       0.00010
dog            0.00009
bird           0.00005
ship           0.00003
frog           0.00001
horse          0.00001
automobile     0.00001

Concurrent requests

Sending 5 concurrent requests to the API server:

$ python3 multi-client.py --num_workers 5 cat.jpg

You should see the result:

----- start -----
Sending requests
Sending requests
Sending requests
Sending requests
Sending requests
------ end ------
Concurrent Requests: 5
Total Runtime: 2.441638708114624