OpenModelZ is the ideal solution for practitioners who want to quickly deploy their machine learning models to a (public or private) endpoint without the hassle of spending excessive time, money, and effort to figure out the entire end-to-end process.
We created OpenModelZ in response to the difficulties of finding a simple, cost-effective way to get models into production fast. Traditional deployment methods can be complex and time-consuming, requiring significant effort and resources to get models up and running.
- Kubernetes: Setting up and maintaining Kubernetes and Kubeflow can be challenging due to their technical complexity. Data scientists spend significant time configuring and debugging infrastructure instead of focusing on model development.
- Managed services: Alternatively, using a managed service like AWS SageMaker can be expensive and inflexible, limiting the ability to customize deployment options.
- Virtual machines: As an alternative, setting up a cloud VM-based solution requires learning complex infrastructure concepts like load balancers, ingress controllers, and other components. This takes a lot of specialized knowledge and resources.
With OpenModelZ, we take care of the underlying technical details for you, and provide a simple and easy-to-use CLI to deploy your models to any cloud (GCP, AWS, or others), your home lab, or even a single machine.
You could start from a single machine and scale it up to a cluster of machines without any hassle. OpenModelZ lies at the heart of our ModelZ, which is a serverless inference platform. It's used in production to deploy models for our customers.
You can find the documentation at docs.open.modelz.ai.
Once you've installed the mdz you can start deploying models and experimenting with them.
There are only two concepts in mdz:
- Deployment: A deployment is a running inference service. You could configure the number of replicas, the port, and the image, and some other parameters.
- Server: A server is a machine that could run the deployments. It could be a cloud VM, a PC, or even a Raspberry Pi. You could start from a single server and scale it up to a cluster of machines without any hassle.
It's super easy to bootstrap the mdz server. You just need to find a server (could be a cloud VM, a home lab, or even a single machine) and run the mdz server start command. The mdz server will be bootstrapped on the server and you could start deploying your models.
$ mdz server start
🚧 Creating the server...
🚧 Initializing the load balancer...
🚧 Initializing the GPU resource...
🚧 Initializing the server...
🚧 Waiting for the server to be ready...
🐋 Checking if the server is running...
Agent:
Version: v0.0.13
Build Date: 2023-07-19T09:12:55Z
Git Commit: 84d0171640453e9272f78a63e621392e93ef6bbb
Git State: clean
Go Version: go1.19.10
Compiler: gc
Platform: linux/amd64
🐳 The server is running at http://192.168.71.93.modelz.live
🎉 You could set the environment variable to get started!
export MDZ_URL=http://192.168.71.93.modelz.live
The internal IP address will be used as the default endpoint of your deployments. You could provide the public IP address of your server to the mdz server start command to make it accessible from the outside world.
# Provide the public IP as an argument
$ mdz server start 1.2.3.4Once you've bootstrapped the mdz server, you can start deploying your first applications.
$ mdz deploy --image aikain/simplehttpserver:0.1 --name simple-server --port 80
Inference simple-server is created
$ mdz list
NAME ENDPOINT STATUS INVOCATIONS REPLICAS
simple-server http://simple-server-4k2epq5lynxbaayn.192.168.71.93.modelz.live Ready 2 1/1
http://192.168.71.93.modelz.live/inference/simple-server.default
You could access the deployment by visiting the endpoint URL. It will be http://simple-server-4k2epq5lynxbaayn.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.
You could scale your deployment by using the mdz scale command.
$ mdz scale simple-server --replicas 3The requests will be load balanced between the replicas of 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# uname -r
5.19.0-46-generic
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
You could add more servers to your cluster by using the mdz server join command. The mdz server will be bootstrapped on the server and join the cluster automatically.
$ mdz server list
NAME PHASE ALLOCATABLE CAPACITY
node1 Ready cpu: 16 cpu: 16
mem: 32784748Ki mem: 32784748Ki
node2 Ready cpu: 16 cpu: 16
mem: 32784748Ki mem: 32784748Ki
You could label your servers to deploy your models to specific servers. For example, you could label your servers with gpu=true and deploy your models to servers with GPUs.
$ mdz server label node3 gpu=true type=nvidia-a100
$ mdz deploy --image aikain/simplehttpserver:0.1 --name simple-server --port 80 --node-labels gpu=true,type=nvidia-a100
Please checkout ROADMAP.
We welcome all kinds of contributions from the open-source community, individuals, and partners.
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