Sign inSign up

wuhanstudio/deepapi

By wuhanstudio

•Updated over 3 years ago

Deep Learning as a Cloud API Service.

Image
0

9.7K

wuhanstudio/deepapi repository overview

⁠Deep API

Deep Learning as Cloud APIs.

This project provides pre-trained deep learning models as a cloud API service. A web interface is available as well.

⁠Quick Start
⁠Python 3:
$ pip3 install -r requirements.txt
$ python main.py
⁠Anaconda:
$ conda env create -f environment.yml
$ conda activate cloudapi
$ python main.py
⁠Using Docker:
docker run -p 8080:8080 wuhanstudio/deep-api

Navigate to https://localhost:8080⁠

⁠API Client

It's possible to get predictions by sending a POST request to http://127.0.0.1:8080/vgg16_cifar10⁠.

⁠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:
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/vgg', '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 clients

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
⁠Full APIs
⁠Post URLs:
ModelDatasetPost URL
VGG-16Cifar10http://127.0.0.1:8080/vgg16_cifar10⁠
VGG-16ImageNethttp://127.0.0.1:8080/vgg16⁠
Resnet-50ImageNethttp://127.0.0.1:8080/resnet50⁠
Inception v3ImageNethttp://127.0.0.1:8080/inceptionv3⁠
⁠Post Data (JSON):
{
  "file": ""
}
⁠Query Parameters:
NameTypeDefaultValue
topinteger10One of [1, 3, 5, 10], top=5 returns top 5 predictions.
no-probinteger0no-prob=1 returns labels without probabilities. no-prob=0 returns labels and probabilities.

Example post urls (returns top 10 predictions with probabilities):

http://127.0.0.1:8080/vgg16?top=10&no-prob=0
⁠Returns (JSON):
KeyValue
successTrue / False
PredictionsArray of prediction results, each element contains {"labels": "cat", "probability": 0.99}
errorThe error message if any

Example returned json:

{
  "success": true,
  "predictions": [
    {
      "label": "cat",
      "probability": 0.9996376037597656
    },
    {
      "label": "dog",
      "probability": 0.0002855948405340314
    },
    {
      "label": "deer",
      "probability": 0.000021985460989526473
    },
    {
      "label": "bird",
      "probability": 0.000021391952031990513
    },
    {
      "label": "horse",
      "probability": 0.000013297495570441242
    },
    {
      "label": "airplane",
      "probability": 0.000006046993803465739
    },
    {
      "label": "ship",
      "probability": 0.0000044226785576029215
    },
    {
      "label": "frog",
      "probability": 0.0000036349929359857924
    },
    {
      "label": "truck",
      "probability": 0.0000035354278224986047
    },
    {
      "label": "automobile",
      "probability": 0.000002384880417594104
    }
  ],
}
⁠References

Tag summary

Content type

Image

Digest

sha256:f7adb3873…

Size

1.3 GB

Last updated

over 3 years ago

docker pull wuhanstudio/deepapi