Python SDK

A Konduit Serving instance can be created by:

1. creating a Python object of the Server class using

   1. the Server() function; or

   2. the server_from_file() function from the konduit.load module; and

2. starting the server using the .start() method of the Server object created in step 1.

We will use the server_from_file() function to configure Konduit Serving in this example.

In Python, specify the path to your configuration in konduit_yaml_path:

konduit_yaml_path = "hello-world.yaml"

Initialize a Konduit Serving instance with the following code:

from konduit.load import server_from_file 
server = server_from_file(konduit_yaml_path)
server.start()

Note that the file also contains Client configuration. To create a Client object, use the client_from_file() function from the konduit.load module:

from konduit.load import client_from_file 
client = client_from_file(konduit_yaml_path)

The Client class provides a .predict() method that sends data to the Serving instance. First, create some sample data as a NumPy array:

import numpy as np 
data_input = np.ones(5)

Assuming your data is declared in the data_input object, data can be passed to client for prediction using:

client.predict(data_input)

The .predict() method takes a single argument data_input which is typically a dictionary. A NumPy array can be directly passed to the .predict() method if the input name is default.

Next steps

To build configurations using the YAML format, check out the YAML configurations page:

YAML configurations

YAML configurations are sufficient for most use cases. In particular, if your use case:

• does not involve DataVec transformations,

• for Python steps: has one transformation script at each pipeline step,

then you should use a YAML configuration.

For more complex configurations, you should use the Python SDK. To build configurations with Python steps, start with the Python pipeline steps page:

Python pipeline steps

To build configurations in Python with TensorFlow, DL4J and Keras models using DL4J and JavaCPP Presets, refer to the example for the respective framework:

TensorFlow (1.x)Keras (TensorFlow 2.0)

To build ETL processes into your serving pipeline, refer to the DataVec example:

DataVec