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 sourceTo install the respective built binaries you can navigate to the section below
Installing BinariesAfter you've installed Konduit-Serving in your local machine you can switch to a terminal and verify the installation by running
You'll see an output similar to the one below
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
and navigate to the quickstart
folder
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.
Navigate to 3-keras-mnist
Here, you'll find the following files:
The keras.json
contains the configuration file for running an MNIST dataset trained model in Keras. To serve the model, execute the following command
You'll be able to see a similar output like the following
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.
You'll see something similar to
To list the server, simply run
You'll see the running servers as a list
To view the logs, you can run the following command
The --lines
or -l
flag shows the specified number of last lines. By executing the above command you'll see the following
Now finally, let's look at running predictions with Konduit-Serving by sending an image file to the server.
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
Congratulations! You've learned the basic workflow for Konduit-Serving using the Command Line Interface.
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