Using CLI

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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
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konduit --version
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You'll see an output similar to the one below
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$ konduit --version
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------------------------------------------------
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Version: 0.1.0-SNAPSHOT
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Commit hash: 3dd38832
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Commit time: 01.03.2021 @ 03:37:08 MYT
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Build time: 07.03.2021 @ 16:57:51 MYT
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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
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git clone https://github.com/KonduitAI/konduit-serving-examples.git
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and navigate to the quickstart folder
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cd konduit-serving-demo/quickstart
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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
DL4J
Navigate to 3-keras-mnist 
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cd 3-keras-mnist
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Here, you'll find the following files:
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.
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├── keras-mnist.ipynb   |    A supplementary jupyter/beakerx notebook
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├── keras.h5            |    Model file we want to serve
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├── keras.json          |    Konduit-Serving configuration
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├── test-image.jpg      |    Test input for predictions
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└── train.py            |    Script for creating 'keras.h5' model file
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The keras.json contains the configuration file for running an MNIST dataset trained model in Keras. To serve the model, execute the following command
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konduit serve --config keras.json -id keras-server 
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You'll be able to see a similar output like the following
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15:00:08.575 [vert.x-worker-thread-0] INFO  a.k.s.m.d.step.DL4JRunner - 
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15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - 
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​
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####################################################################
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#                                                                  #
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#    |  /   _ \   \ |  _ \  |  | _ _| __ __|    |  /     |  /      #
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#    . <   (   | .  |  |  | |  |   |     |      . <      . <       #
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#   _|\_\ \___/ _|\_| ___/ \__/  ___|   _|     _|\_\ _) _|\_\ _)   #
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#                                                                  #
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####################################################################
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​
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15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
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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'
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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
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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.
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konduit serve --config keras.json -id keras-server --background
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You'll see something similar to
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Starting konduit server...
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Expected classpath: /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar
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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
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For server status, execute: 'konduit list'
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For logs, execute: 'konduit logs keras-server'
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To list the server, simply run
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konduit list
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You'll see the running servers as a list
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Listing konduit servers...
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​
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 #   | ID                             | TYPE       | URL                  | PID     | STATUS     
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 1   | keras-server                   | inference  | localhost:1000       | 1200    | Started
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To view the logs, you can run the following command
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konduit logs keras-server --lines 2
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The --lines or -l flag shows the specified number of last lines. By executing the above command you'll see the following
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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'
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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
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Now finally, let's look at running predictions with Konduit-Serving by sending an image file to the server. 
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konduit predict keras-server -it multipart '[email protected]'
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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
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{
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  "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 ] ],
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  "prob" : 0.5524258017539978,
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  "index" : 5,
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  "label" : "5"
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}
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Navigate to 5-dl4j-mnist
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cd 5-dl4j-mnist
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Here, you'll find the following files:
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.
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├── dl4j-mnist.ipynb    |    A supplementary jupyter/beakerx notebook
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├── dl4j-mnist.zip      |    Model file we want to serve
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├── dl4j.json           |    Konduit-Serving configuration 
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└── test-image.jpg      |    Test input for predictions
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The dl4j.json contains the configuration file for running an MNIST dataset trained model in DL4J. To serve the model, execute the following command
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konduit serve --config dl4j.json -id dl4j-server 
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You'll be able to see a similar output like the following
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15:00:08.575 [vert.x-worker-thread-0] INFO  a.k.s.m.d.step.DL4JRunner - 
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15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - 
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​
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####################################################################
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#                                                                  #
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#    |  /   _ \   \ |  _ \  |  | _ _| __ __|    |  /     |  /      #
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#    . <   (   | .  |  |  | |  |   |     |      . <      . <       #
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#   _|\_\ \___/ _|\_| ___/ \__/  ___|   _|     _|\_\ _) _|\_\ _)   #
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#                                                                  #
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####################################################################
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​
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15:00:08.576 [vert.x-worker-thread-0] INFO  a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
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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'
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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
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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.
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konduit serve --config dl4j.json -id dl4j-server --background
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You'll see something similar to
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Starting konduit server...
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Expected classpath: /Users/konduit/Projects/Konduit/konduit-serving/konduit-serving-tar/target/konduit-serving-tar-0.1.0-SNAPSHOT-dist/bin/../konduit.jar
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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
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For server status, execute: 'konduit list'
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For logs, execute: 'konduit logs dl4j-server'
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To list the server, simply run
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konduit list
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You'll see the running servers as a list
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Listing konduit servers...
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 #   | ID                             | TYPE       | URL                  | PID     | STATUS     
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 1   | dl4j-server                    | inference  | localhost:1000       | 1200    | Started
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To view the logs, you can run the following command
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konduit logs dl4j-server --lines 2
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The --lines or -l flag shows the specified number of last lines. By executing the above command you'll see the following
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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'
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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
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Now finally, let's look at running predictions with Konduit-Serving by sending an image file to the server. 
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konduit predict dl4j-server -it multipart '[email protected]'
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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
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{
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  "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 ] ],
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  "prob" : 0.439374715089798,
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  "index" : 3,
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  "label" : "3"
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}
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Congratulations! You've learned the basic workflow for Konduit-Serving using the Command Line Interface.
Using Python SDK
Building from source
Last modified 7mo ago
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Contents
Prerequisite
Installation from Sources
Deploying Models
Cloning Examples Repo