A step by step guide on how to use tensorflow serving to serve a tensorflow model. These steps are illustrated with google's slim models, e.g., inception-v4, inception-resnet-v2, via tensorflow serving. And the result served models with gRPC and REST services are wrapped into a docker image for further development. Read the step by step guide at this gh-pages.
$ docker run -p 80:80 -d gyang274/yg-tfs-slim:rest
- check usage:
GET /
# endpoint: GET /
# - returns: usage
$ curl -X GET 127.0.0.1:80
- main endpoint:
POST /
# endpoint: POST /
# - payload:
# - host: optional, host for tensorflow serving model server, default "127.0.0.1",
# - port: optional, port for tensorflow serving model server, default "9000",
# - model_name: optional, tensorflow serving model name, default "slim_inception_resnet_v2",
# all available models: slim_inception_resnet_v2 at port 9000, and slim_inception_v4 at port 9090',
# - image_urls: required, image urls in list
# - returns:
# - classes: top 5 classes of each input image_urls, in shape `n x 5`
# - scores: top 5 classes scores (probabilities) of each input image_urls, in shape `n x 5`,
# - prelogits: a numeric vector of 1536 of each input image_urls, in shape `n x 1536`,
# this vector can be viewed as features of each input image_urls for transfer learning or etc.
$ curl -X POST 127.0.0.1:80 -d '{
"image_urls": [
"https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg",
"https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Labrador_Retriever_portrait.jpg/1200px-Labrador_Retriever_portrait.jpg",
"https://upload.wikimedia.org/wikipedia/commons/f/fd/Qantas_a380_vh-oqa_takeoff_heathrow_arp.jpg"
]
}'
- note
# note: the rest api expected a valid json as payload, so it might need to remove line breaks and make the post data in
# one line, since the terminal might interpret line breaks as `\n` and add it into payload which causes invalid json.
$ curl -X POST 127.0.0.1:80 -d '{"image_urls": ["https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg","https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Labrador_Retriever_portrait.jpg/1200px-Labrador_Retriever_portrait.jpg","https://upload.wikimedia.org/wikipedia/commons/f/fd/Qantas_a380_vh-oqa_takeoff_heathrow_arp.jpg"]}'
import requests
response = requests.post(
url="http://127.0.0.1:80",
json={
"image_urls": [
"https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg",
"https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Labrador_Retriever_portrait.jpg/1200px-Labrador_Retriever_portrait.jpg",
"https://upload.wikimedia.org/wikipedia/commons/f/fd/Qantas_a380_vh-oqa_takeoff_heathrow_arp.jpg"
]
}
)
print(response.json())
This gh-pages includes a step by step on how to make these docker images from the begining.
This github repository includes all source codes.