Using CLI

Guide to start using Konduit-Serving with CLI

This document will demonstrate using Konduit-Serving using mainly CLI tools. You can deploy ML/DL models to production using minimal effort using Konduit-Serving. Let's look at the process of building and installing Konduit-Serving from source and how to deploy a model using a simple configuration.

Prerequisite

You will need following prerequisites to follow along

• Maven 3.x

• JDK 8

• Git

Installation from Sources

The following two sections explains how to clone, build and install Konduit-Serving from sources.

To build from source, follow the guide below

Building from source

To install the respective built binaries you can navigate to the section below

Installing Binaries

After you've installed Konduit-Serving in your local machine you can switch to a terminal and verify the installation by running

konduit --version

You'll see an output similar to the one below

$ konduit --version
------------------------------------------------
Version: 0.1.0-SNAPSHOT
Commit hash: 3dd38832
Commit time: 01.03.2021 @ 03:37:08 MYT
Build time: 07.03.2021 @ 16:57:51 MYT

Deploying Models

Let's look at how to deploy a dl4j/keras model using Konduit-Serving

Cloning Examples Repo

Let's clone the konduit-serving-examples repo

git clone https://github.com/KonduitAI/konduit-serving-examples.git

and navigate to the quickstart folder

cd konduit-serving-demo/quickstart

The examples we want to run are under the folders 3-keras-mnist and 5-dl4j-mnist. Let's follow a basic workflow for both models using the Konduit-Serving CLI.

Keras

Navigate to 3-keras-mnist

cd 3-keras-mnist

Here, you'll find the following files:

.
├── keras-mnist.ipynb   |    A supplementary jupyter/beakerx notebook
├── keras.h5            |    Model file we want to serve
├── keras.json          |    Konduit-Serving configuration
├── test-image.jpg      |    Test input for predictions
└── train.py            |    Script for creating 'keras.h5' model file

The keras.json contains the configuration file for running an MNIST dataset trained model in Keras. To serve the model, execute the following command

konduit serve --config keras.json -id keras-server 

You'll be able to see a similar output like the following

.
.
.
15:00:08.575 [vert.x-worker-thread-0] INFO  a.k.s.m.d.step.DL4JRunner - 
15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - 

####################################################################
#                                                                  #
#    |  /   _ \   \ |  _ \  |  | _ _| __ __|    |  /     |  /      #
#    . <   (   | .  |  |  | |  |   |     |      . <      . <       #
#   _|\_\ \___/ _|\_| ___/ \__/  ___|   _|     _|\_\ _) _|\_\ _)   #
#                                                                  #
####################################################################

15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
.
.
.
.
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 40987 with 4 pipeline steps

The last line will show you the details about which URL the server is serving the models at.

Press Ctrl + C, or execute konduit stop keras-server to kill the server.

To run the server in the background, you can run the same command with the --background or -b flag.

konduit serve --config keras.json -id keras-server --background

You'll see something similar to

Starting konduit server...
Expected classpath: /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar
INFO: Running command /Users/konduit/opt/miniconda3/jre/bin/java -Dkonduit.logs.file.path=/Users/konduit/.konduit-serving/command_logs/keras-server.log -Dlogback.configurationFile=/Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../conf/logback-run_command.xml -cp /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar ai.konduit.serving.cli.launcher.KonduitServingLauncher run --instances 1 -s inference -c keras.json -Dserving.id=keras-server
For server status, execute: 'konduit list'
For logs, execute: 'konduit logs keras-server'

To list the server, simply run

konduit list

You'll see the running servers as a list

Listing konduit servers...

 #   | ID                             | TYPE       | URL                  | PID     | STATUS     
 1   | keras-server                   | inference  | localhost:1000       | 1200    | Started

To view the logs, you can run the following command

konduit logs keras-server --lines 2

The --lines or -l flag shows the specified number of last lines. By executing the above command you'll see the following

15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 1000 with 4 pipeline steps

Now finally, let's look at running predictions with Konduit-Serving by sending an image file to the server.

konduit predict keras-server -it multipart 'image=@test-image.jpg'

It will convert the image into an n-dimensional array and then send the input to the keras model and you'll see the following output

{
  "output_layer" : [ [ 9.0376153E-7, 1.0595608E-8, 1.3115231E-5, 0.44657645, 6.748624E-12, 0.5524258, 1.848306E-7, 2.7652052E-9, 9.76023E-4, 7.5933513E-6 ] ],
  "prob" : 0.5524258017539978,
  "index" : 5,
  "label" : "5"
}

DL4J

Navigate to 5-dl4j-mnist

cd 5-dl4j-mnist

Here, you'll find the following files:

.
├── dl4j-mnist.ipynb    |    A supplementary jupyter/beakerx notebook
├── dl4j-mnist.zip      |    Model file we want to serve
├── dl4j.json           |    Konduit-Serving configuration 
└── test-image.jpg      |    Test input for predictions

The dl4j.json contains the configuration file for running an MNIST dataset trained model in DL4J. To serve the model, execute the following command

konduit serve --config dl4j.json -id dl4j-server 

You'll be able to see a similar output like the following

.
.
.
15:00:08.575 [vert.x-worker-thread-0] INFO  a.k.s.m.d.step.DL4JRunner - 
15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - 

####################################################################
#                                                                  #
#    |  /   _ \   \ |  _ \  |  | _ _| __ __|    |  /     |  /      #
#    . <   (   | .  |  |  | |  |   |     |      . <      . <       #
#   _|\_\ \___/ _|\_| ___/ \__/  ___|   _|     _|\_\ _) _|\_\ _)   #
#                                                                  #
####################################################################

15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
.
.
.
.
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 40987 with 4 pipeline steps

The last line will show you the details about which URL the server is serving the models at.

Press Ctrl + C, or execute konduit stop keras-server to kill the server.

To run the server in the background, you can run the same command with the --background or -b flag.

konduit serve --config dl4j.json -id dl4j-server --background

You'll see something similar to

Starting konduit server...
Expected classpath: /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar
INFO: Running command /Users/konduit/opt/miniconda3/jre/bin/java -Dkonduit.logs.file.path=/Users/konduit/.konduit-serving/command_logs/dl4j-server.log -Dlogback.configurationFile=/Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../conf/logback-run_command.xml -cp /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar ai.konduit.serving.cli.launcher.KonduitServingLauncher run --instances 1 -s inference -c dl4j.json -Dserving.id=dl4j-server
For server status, execute: 'konduit list'
For logs, execute: 'konduit logs dl4j-server'

To list the server, simply run

konduit list

You'll see the running servers as a list

Listing konduit servers...

 #   | ID                             | TYPE       | URL                  | PID     | STATUS     
 1   | dl4j-server                    | inference  | localhost:1000       | 1200    | Started

To view the logs, you can run the following command

konduit logs dl4j-server --lines 2

The --lines or -l flag shows the specified number of last lines. By executing the above command you'll see the following

15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
15:00:08.752 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 1000 with 4 pipeline steps

Now finally, let's look at running predictions with Konduit-Serving by sending an image file to the server.

konduit predict dl4j-server -it multipart 'image=@test-image.jpg'

It will convert the image into an n-dimensional array and then send the input to the DL4J model and you'll see the following output

{
  "layer5" : [ [ 1.845163E-5, 1.8346094E-6, 0.31436875, 0.43937472, 2.6101702E-8, 0.24587035, 5.9430695E-6, 3.3270408E-4, 6.3698195E-8, 2.708706E-5 ] ],
  "prob" : 0.439374715089798,
  "index" : 3,
  "label" : "3"
}

Congratulations! You've learned the basic workflow for Konduit-Serving using the Command Line Interface.