Cluster Serving


API Guide

Python

class InputQueue

The class Input defines methods allowing you to input data into Cluster Serving Input Pipeline.

init

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__init__()

sets up a connection with configuration in your Cluster Serving configuration file config.yaml.

return: None

enqueue

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enqueue(uri, data*)

puts key-value pair into data pipeline, if your model has key regulation, pass corresponded key. Otherwise, give the key any name you want.

return: None

uri: a string, unique identification of your image

data: key-value pair of your data.

There are 4 types of inputs in total, string, image, tensor, sparse tensor. See following example to enqueue specific type of data. Example Import the dependency and create an instance of InputQueue

from zoo.serving.client import InputQueue
input_api = InputQueue()

To enqueue a list of string, pass a list of str objects, list of str type input is usually used in Tensorflow models.

input_api.enqueue('my-string-input', user_define_key=['hello', 'world'])

To enqueue an image, you could pass either image path or base64 encoded image bytes, the type of your parameter is identified by key of dict, see example below. If you pass image path, cv2 package is required. (Could be installed by pip install opencv-python)

To pass image path, use key path

image_path = "path/to/image"
input_api.enqueue('my-image1', user_define_key={"path": image_path})

To pass base64 encoded image bytes, use key b64

image_bytes = "base64_encoded_bytes"
input_api.enqueue('my-image1', user_define_key={"b64": image_bytes})

To enqueue a tensor or sparse tensor, numpy package is required. (Would be installed while you installed Analytics Zoo, if not, could be installed by pip install numpy)

To enqueue a tensor, pass a ndarray object.

import numpy as np
input_api.enqueue('my-tensor1', a=np.array([1,2]))

To enqueue a sparse tensor pass a list of ndarray object, normally sparse tensor is only used if your model specifies the input as sparse tensor. The list should have size of 3, where the 1st element is a 2-D ndarray, representing the indices of values, the 2nd element is a 1-D ndarray, representing the values corresponded with the indices, the 3rd element is a 1-D ndarray representing the shape of the sparse tensor.

A sparse tensor of shape (5,10), with 3 elements at position (0, 0), (1, 2), (4, 5), having value 101, 102, 103, visualized as following,

101. 0.   0.   0.   0.   0.   0.   0.   0.   0
0.   0.   102. 0.   0.   0.   0.   0.   0.   0
0.   0.   0.   0.   0.   0.   0.   0.   0.   0
0.   0.   0.   0.   0.   0.   0.   0.   0.   0
0.   0.   0.   0.   0.   103. 0.   0.   0.   0

could be represented as following list.

indices = np.array([[0, 1, 4], [0, 2, 5]])
values = np.array([101, 102, 103])
shape = np.array([5, 10])
tensor = [indices, values, shape]

and enqueue it by

input_api.enqueue("my-sparse-tensor", input=tensor)

To enqueue an instance containing several images, tensors and sparse tensors.

import numpy as np
input_api.enqueue('my-instance', 
    img1={"path: 'path/to/image1'},
    img2={"path: 'path/to/image2'},
    tensor1=np.array([1,2]), 
    tensor2=np.array([[1,3],[2,3]])
    sparse_tensor=[np.array([[0, 1, 4], [0, 2, 5]]),
                   np.array([101, 102, 103])
                   np.array([5, 10])]
)

class OutputQueue

The class Output defines methods allowing you to get result from Cluster Serving Output Pipeline.

init

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__init__()

sets up a connection with configuration in your Cluster Serving configuration file config.yaml.

query

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query(uri)

query result in output Pipeline by key uri

return: string type, the output of your prediction, which can be parsed to a dict by json.

Format:

{
    "class_1": "probability_1",
    "class_2": "probability_2",
    ...,
    "class_n": "probability_n"
}

where n is top_n in your serving config, the result is sorted by output probability.

Example

from zoo.serving.client import OutputQueue
import json
output_api = OutputQueue()
d = output_api.query('my-image') 

tmp_dict = json.loads(d)
for class_idx in tmp_dict.keys():
    output += "class: " + class_idx + "'s prob: " + tmp_dict[class_idx]
print(output)

dequeue

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dequeue()

gets all result of your model prediction and dequeue them from OutputQueue

return: dict(), with keys the uri of your enqueue, string type, and values the output of your prediction, string type, which can be parsed by json.

Format:

{
  "image1": {
      "class_1": "probability_1",
      "class_2": "probability_2",
      ...,
      "class_n": "probability_n"
  }, 
  "image2": {
      "class_1": "probability_1",
      "class_2": "probability_2",
      ...,
      "class_n": "probability_n"
  }
  ...
}

where n is top_n in your serving config, the result is sorted by output probability.

Example

from zoo.serving.client import OutputQueue
import json
output_api = OutputQueue()
d = output_api.dequeue()

for k in d.keys():
    output = "image: " + k + ", classification-result:"
    tmp_dict = json.loads(result[k])
    for class_idx in tmp_dict.keys():
        output += "class: " + class_idx + "'s prob: " + tmp_dict[class_idx]
    print(output)