HomeCommunity

YAML configurations

Konduit Serving supports specifying server configurations as YAML files. This allows you to serve simple server configurations using the Konduit Python CLI and the konduit.load module.

YAML components

A Konduit Serving YAML configuration file has three top-level entities:

  1. serving

  2. steps

  3. client

The following is a sample YAML file for serving a Python script located at simple.py which takes a NumPy array first as input and returns a NumPy array second as output:

serving:
  http_port: 1337
  output_data_format: NUMPY
  log_timings: True
  extra_start_args: -Xmx8g
steps:
  python_step:
    type: PYTHON
    python_path: .
    python_code_path: ./simple.py
    python_inputs:
      first: NDARRAY
    python_outputs:
      second: NDARRAY
client:
    port: 1337

Serving

The server configuration, serving takes the following arguments:

  • http_port: specify the port number

  • listen_host: the host of the Konduit Serving instance. Defaults to http://localhost.

Additional arguments include:

  • uploads_directory: Directory to store file uploads. Defaults to 'file-uploads/'.

  • jar_path: Path to the Konduit Serving uberjar. Defaults to the KONDUIT_JAR_PATH environment variable, or if unavailable, ~/.konduit/konduit-serving/konduit.jar.

  • log_timings: Whether to log timings for this config. Defaults to False.

  • extra_start_args: Java Virtual Machine (JVM) arguments. In this case, -Xmx8g specifies that the maximum memory allocation for the JVM is 8GB.

Refer to the Server documentation for other arguments.

Client

The client configuration takes the following arguments:

  • port: specify the same HTTP port as serving.

  • host: defaults to http://localhost. Ignore this argument for local instances.

Typically it is sufficient to specify the port and hostas the remaining attributes are obtained from the Server. Refer to the Client documentation for details.

  • input_names, output_names: names of the first and final nodes of the Konduit Serving pipeline configuration defined in the Server.

  • output_data_format: the format of the server's input and output. Specify one of the following: JSON, NUMPY, ARROW, IMAGE, ND4J.

Steps

Python steps

Python steps run code specified in the python_code (a string) or python_code_path (a .py script) argument. Python steps defined in the YAML configuration default to input name and output name "default".

Importantly, the python_inputs argument maps each input and output variable to the respective data type. Accepted data types are INT, STR, FLOAT, BOOL, NDARRAY.

steps: 
  python_step: 
    type: PYTHON
    python_code: |
      first += 2
      second = first
    python_inputs:
      first: NDARRAY
    python_outputs:
      second: NDARRAY

If no Python path is specified, NumPy will still be available in the environment where the Python step is run.

To further customize Python steps, refer to the YAML configuration section of the Python pipeline steps page.

Python pipeline steps

A more comprehensive example is available on the following page:

Open Neural Network Exchange (ONNX)

Model steps

Use model steps when you want to use pre-packaged modules such as TensorFlow, DL4J and PMML for inference.

steps:
  tensorflow_step:
    type: TENSORFLOW
    model_loading_path: ../data/mnist/mnist_2.0.0.pb
    input_names:
      - input_layer
    output_names:
      - output_layer/Softmax
    input_data_types:
      input_layer: FLOAT

The following parameters should be specified:

  • type: one of TENSORFLOW, KERAS, COMPUTATION_GRAPH, MULTI_LAYER_NETWORK, PMML, SAMEDIFF;

  • model_loading_path: location of your model file;

  • input_names: list of the names of input nodes of your model file;

  • output_names: list of the names of output nodes of your model file;

  • input_data_types: map each of the input nodes to one of the following data types using the input names as keys: INT, STR, FLOAT, BOOL, NDARRAY.

Refer to the model-specific example for details on configuring model steps.

TensorFlow (1.x)Keras (TensorFlow 2.0)

Usage

On the server, start a Konduit Serving instance by:

  1. creating a Server object using server_from_file,

  2. starting the server using the .start() method. 

from konduit.load import server_from_file

konduit_yaml_path = "../yaml/simple.yaml"

server = server_from_file(konduit_yaml_path)
server.start()
Starting server..

Server has started successfully.

After the server has started, on the client:

  1. create a Client object using client_from_file; and

  2. use the .predict() method to perform inference on a NumPy array (note that the input name of this Server configuration is default, therefore we can pass a NumPy array directly to the .predict() method.).

import numpy as np 
import os
from konduit.load import client_from_file

konduit_yaml_path = "../yaml/simple.yaml"
input_arr = np.array(33)

client = client_from_file(konduit_yaml_path)
print(client.predict(input_arr))
[35]

Finally, stop the server with the .stop() method:

server.stop()

This can also be run in the command line. In the root folder of konduit-serving-examples, initialize the Konduit Serving instance with

konduit serve --config yaml/simple.yaml

Send the NPY file to the server for inference with

konduit predict-numpy --config yaml/simple.yaml --numpy_data data/simple/input_arr.npy

and finally, stop the server with

konduit stop-server --config yaml/simple.yaml

Resources

Some resources on the YAML format:

PreviousBuilding from sourceNextPython

Last updated