Command line interface (CLI)

A brief overview of konduit-serving command line interface.

konduit-serving comes with a handy CLI that you can use to manage your serving instances. Konduit CLI comes with the konduit pip package. After installing the pip package and initializing the CLI, you can use the konduit command line tool by typing the following on the terminal:

konduit --help

which should prompt all currently available commands:

Usage: konduit [COMMAND] [OPTIONS] [arg...]
config A helper command for creating JSON for inference configuration
inspect Inspect the details of a particular konduit server.
list Lists the running konduit servers.
logs View the logs of a particular konduit server
predict Run inference on konduit servers using given inputs
serve Start a konduit server application
stop Stop a running konduit server
version Displays konduit-serving version.
Run 'konduit COMMAND --help' for more information on a command.

For more help on individual commands you can run konduit [COMMAND] --help. For example:

konduit serve --help

to view the usage of serve command:

Usage: konduit serve [-b] [-cp <classpath>] -c <server-config> [-i <instances>]
[-jo <value>] [-s <type>] [-id <value>]
Start a konduit server application
Start a konduit server application. The application is identified with an id
that can be set using the `--serving-id` or `-id` option. The application can be
stopped with the `stop` command. This command takes the `run` command
parameters. To see the run command parameters, execute `run --help`
Example usages:
- Starts a server in the foreground with an id of 'inf_server' using
'config.json' as configuration file:
$ konduit serve -id inf_server -c config.json
- Starts a server in the background with an id of 'inf_server' using
'config.json' as configuration file:
$ konduit serve -id inf_server -c config.json -b
Options and Arguments:
-b,--background Runs the process in the background, if set.
-cp,--classpath <classpath> Provides an extra classpath to be used for the
verticle deployment.
-c,--config <server-config> Specifies configuration that should be provided
to the verticle. <config> should reference either
a text file containing a valid JSON object which
represents the configuration OR be a JSON string.
-i,--instances <instances> Specifies how many instances of the server will
be deployed. Defaults to 1.
-jo,--java-opts <value> Java Virtual Machine options to pass to the
spawned process such as "-Xmx1G -Xms256m
-XX:MaxPermSize=256m". If not set the `JAVA_OPTS`
environment variable is used.
-s,--service <type> Service type that needs to be deployed. Defaults
to "inference"
-id,--serving-id <value> Id of the serving process. This will be visible
in the 'list' command. This id can be used to
call 'predict' and 'stop' commands on the running
servers. If not given then an 8 character UUID is
created automatically.

The --help argument for the individual commands gives you a quick summary and a detailed description of what the command is about along with a few examples of common usage patterns.

Example Workflow

Following is an example workflow of how to use the konduit CLI for serving an ImageLoadingStep.

1. Create a configuration

Before running any server, you'll have to configure a json configuration for the serving pipeline. The config command is a very handy tool to create a baseline configuration that you can edit later based on your requirements. In this workflow, you'll see how to create a basic configuration for reading image file and return the loaded image in JSON format with the predict command. To create an image configuration you can run the config command as follows:

konduit config -t image

You'll see the following output from it (might differ based on your local environment):

"servingConfig" : {
"createLoggingEndpoints" : false,
"httpPort" : 0,
"listenHost" : "localhost",
"logTimings" : false,
"metricsConfigurations" : [ ],
"outputDataFormat" : "JSON",
"uploadsDirectory" : "C:\\Users\\konduit\\AppData\\Local\\Temp"
"steps" : [ {
"@type" : "ImageLoadingStep",
"dimensionsConfigs" : { },
"imageProcessingInitialLayout" : "NCHW",
"imageProcessingRequiredLayout" : "NCHW",
"imageTransformProcesses" : { },
"inputColumnNames" : { },
"inputNames" : [ "default" ],
"inputSchemas" : { },
"originalImageHeight" : 0,
"originalImageWidth" : 0,
"outputColumnNames" : { },
"outputNames" : [ "default" ],
"outputSchemas" : { },
"updateOrderingBeforeTransform" : false
} ]


A port equal to 0 means that a random port will be selected for the server when it's run.

To save the configuration in a file, you can run:

konduit config -t image -o image-config.json

You'll see the following output from it:

Config file created successfully at C:\Users\konduit\image-config.json

2. Start a server

For starting the server, you can use the serve command:

konduit serve -b -id image-server -c image-config.json

This will start a konduit server with the given configuration in the background.

3. List the running servers

To view the running servers, you can use the list command:

konduit list

You can an output like the following:

Listing konduit servers...
1 | image-server | inference | localhost:58663 | 23756 | started


You might see a different port based on your running environment.

4. View the logs

You can view the logs of the running server with the logs command:

konduit logs image-server

which will show you the following logs for the running server (truncated for brevity):

16:36:09.491 [main] INFO ai.konduit.serving.util.LogUtils - Logging file at: C:\Users\shams\.konduit-serving\command_logs\image-server.log
16:36:09.631 [main] INFO a.k.s.l.KonduitServingLauncher - Setup micro meter options.
16:36:10.154 [main] INFO a.k.s.l.command.KonduitRunCommand - Starting konduit server with an id of 'image-server'
16:36:10.397 [vert.x-eventloop-thread-0] INFO a.k.s.routers.PipelineRouteDefiner - Using metrics registry io.micrometer.prometheus.PrometheusMeterRegistry for inference
16:36:10.760 [vert.x-eventloop-thread-0] DEBUG o.h.common.AbstractCentralProcessor - Oracle MXBean detected.
16:36:10.811 [vert.x-eventloop-thread-0] DEBUG - Localized Processor to Processor
16:36:10.852 [vert.x-eventloop-thread-0] DEBUG o.h.p.w.WindowsCentralProcessor - Initialized Processor
16:36:11.896 [vert.x-eventloop-thread-0] DEBUG - Initialized OSVersionInfoEx .
16:36:15.164 [vert.x-eventloop-thread-0] INFO a.k.s.v.inference.InferenceVerticle - Inference server is listening on host: 'localhost'
16:36:15.164 [vert.x-eventloop-thread-0] INFO a.k.s.v.inference.InferenceVerticle - Inference server started on port 58663 with 1 pipeline steps
16:36:15.164 [vert.x-eventloop-thread-1] INFO i.v.c.i.l.c.VertxIsolatedDeployer - Succeeded in deploying verticle

5. Running predictions

After a server is successfully started you can use the predict command to run inferences on the server:

konduit predict -it IMAGE image-server C:\Users\konduit\mnist-5_10x10.png

The output json will look similar to (truncated for brevity):

"default" : {
"batchId" : "7d0db4c3-2cb4-4da4-b750-6e6435cadcab",
"ndArray" : {
"dataType" : "FLOAT",
"shape" : [ 1, 3, 10, 10 ],
"data" : [ 0.0, 28.0, 61.0, 25.0, , ..., 55.0, 0.0, 0.0, 0.0, 0.0, 11.0 ]

6. Stop a server

Finally for stopping a server you can use the stop command:

konduit stop image-server

which will output:

Stopping konduit server 'image-server'
Application 'image-server' terminated with status 0

What's next?

You can look at the description for each of the konduit CLI commands and try out different combination of configuration.