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tensorchord/openmodelz

Autoscale LLM (vLLM, SGLang, LMDeploy) inferences on Kubernetes (and others)

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Go Apache-2.0Created Jul 13, 2023

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Overview

Autoscale LLM (vLLM, SGLang, LMDeploy) inferences on Kubernetes (and others)

Capability facts

CLI
CLI entrypoint

Source: pyproject.toml:[project.scripts] · Jul 11, 2026

Languages
go, python

Source: github.language+pyproject.toml · Jul 11, 2026

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Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Jul 11, 2026)

1 root 0:00 /usr/bin/dumb-init /bin/sh -c python3 -m http.server 80
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README

Install mdz

You can install OpenModelZ using the following command:

pip install openmodelz

You could verify the installation by running the following command:

mdz

Once you've installed the mdz you can start deploying models and experimenting with them.


Create your first UI-based deployment

Once you've bootstrapped the mdz server, you can start deploying your first applications. We will use jupyter notebook as an example in this tutorial. You could use any docker image as your deployment.

$ mdz deploy --image jupyter/minimal-notebook:lab-4.0.3 --name jupyter --port 8888 --command "jupyter notebook --ip='*' --NotebookApp.token='' --NotebookApp.password=''"
Inference jupyter is created
$ mdz list
 NAME     ENDPOINT                                                   STATUS  INVOCATIONS  REPLICAS
 jupyter  http://jupyter-9pnxdkeb6jsfqkmq.192.168.71.93.modelz.live  Ready           488  1/1
          http://192.168.71.93/inference/jupyter.default                                                                         

You could access the deployment by visiting the endpoint URL. The endpoint will be automatically generated for each deployment with the following format: <name>-<random-string>.<ip>.modelz.live.

It is http://jupyter-9pnxdkeb6jsfqkmq.192.168.71.93.modelz.live in this case. The endpoint could be accessed from the outside world as well if you've provided the public IP address of your server to the mdz server start command.


Scale your deployment

You could scale your deployment by using the mdz scale command.

$ mdz scale simple-server --replicas 3

The requests will be load balanced between the replicas of your deployment.

You could also tell the mdz to autoscale your deployment based on the inflight requests. Please check out the Autoscaling documentation for more details.


Debug your deployment

Sometimes you may want to debug your deployment. You could use the mdz logs command to get the logs of your deployment.

$ mdz logs simple-server
simple-server-6756dd67ff-4bf4g: 10.42.0.1 - - [27/Jul/2023 02:32:16] "GET / HTTP/1.1" 200 -
simple-server-6756dd67ff-4bf4g: 10.42.0.1 - - [27/Jul/2023 02:32:16] "GET / HTTP/1.1" 200 -
simple-server-6756dd67ff-4bf4g: 10.42.0.1 - - [27/Jul/2023 02:32:17] "GET / HTTP/1.1" 200 -

You could also use the mdz exec command to execute a command in the container of your deployment. You do not need to ssh into the server to do that.

$ mdz exec simple-server ps
PID   USER     TIME   COMMAND
    1 root       0:00 /usr/bin/dumb-init /bin/sh -c python3 -m http.server 80
    7 root       0:00 /bin/sh -c python3 -m http.server 80
    8 root       0:00 python3 -m http.server 80
    9 root       0:00 ps
$ mdz exec simple-server -ti bash
bash-4.4# 

Or you could port-forward the deployment to your local machine and debug it locally.

$ mdz port-forward simple-server 7860
Forwarding inference simple-server to local port 7860