allenstin / image_recognition_serving_api

Easy way to deploy multiple models with deep learning frameworks for your products.

Api for image recognition serving

Easy way to deploy multiple models with deep learning frameworks for your products.

深度学习模型多模型线上部署API. 可根据需要快速部署tensorflow, Keras模型(flask + gevent + gunicorn). 有更高级的需求可以使用tensorflow serving

My test environment:

• Ubuntu 16.04
• python3

Installation

python setup.py build
python setup.py install

Usage

from img_recog_api.model_creator import ImageModelSingle

config = 'path/to/config'

# image recognition
# create and load models
Model_s = ImageModelSingle(config)
# load image to api support format
image = your_load_image_func(image)
# predict image
results_s = Model_s.predict(image, top=4)

configs

Contains all config files.

• config.yaml

model_type: SingleTf
model_file: /path/to/model.pb
label_file: /path/to/labels.txt
# gpu_num could be set as any int. for cpu using gpu_num: ""
gpu_num: 0
input_tensor: input tensor name of tensorflow model(eg: Placeholder:0), if None, find it with pattern
output_tensor: output tensor name of tensorflow model(eg: Prediction:0), if None, find it with pattern

Outputs

ImageModelSingle()

model_type=SingleTf

**result = model.predict(image, top)**

{
  "recognitionList": [
    {
      "className": "label1",
      "confidence": 0.2440231442451477
    }
  ]
}

Add your own model

Just over write the sub_models.py and add new class for your model like the example SingleTf.

Deploy your models

In this demo, I deployed a serving model with python using flask + gevent + gunicorn.

Is easy to deploy multiple models in this way. But tensorflow serving maybe a good choose for long-term use.

How to use it:

• cd to example folder.
• add your prediction scripts in sub_model.py and install the api.
• change config.yaml according to your models.
• add config information in gunicorn.config. For detail information pleae view gunicorn setting document
• change image_recognition_all.py.

Run the server:

sh ./example_run.sh

Test it:

python example/service_test.py

TODO

• remove the warm-up part (If anyone comes up a good idea to speed up the first test, feel free to contact me :) )