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Stack deployment

Deploying your stack components directly from the ZenML CLI
The first step in running your pipelines on remote infrastructure is to deploy all the components that you would need, like an MLflow tracking server, a Seldon Core model deployer, and more to your cloud.
This can bring plenty of benefits like scalability, reliability, and collaboration. ZenML eases the path to production by providing a seamless way for all tools to interact with others through the use of abstractions. However, one of the most painful parts of this process, from what we see on our Slack and in general, is the deployment of these stack components.

Deploying MLOps tools is tricky 😭😵‍💫

It is not trivial to set up all the different tools that you might need for your pipeline.
  • 🌈 Each tool comes with a certain set of requirements. For example, a Kubeflow installation will require you to have a Kubernetes cluster, and so would a Seldon Core deployment.
  • 🤔 Figuring out the defaults for infra parameters is not easy. Even if you have identified the backing infra that you need for a stack component, setting up reasonable defaults for parameters like instance size, CPU, memory, etc., needs a lot of experimentation to figure out.
  • 🚧 Many times, standard tool installations don't work out of the box. For example, to run a custom pipeline in Vertex AI, it is not enough to just run an imported pipeline. You might also need a custom service account that is configured to perform tasks like reading secrets from your secret store or talking to other GCP services that your pipeline might need.
  • 🔐 Some tools need an additional layer of installations to enable a more secure, production-grade setup. For example, a standard MLflow tracking server deployment comes without an authentication frontend which might expose all of your tracking data to the world if deployed as-is.
  • 🗣️ All the components that you deploy must have the right permissions to be able to talk to each other. When you run your pipeline, it is inevitable that some components would need to communicate with the others. For example, your workloads running in a Kubernetes cluster might require access to the container registry or the secrets manager, and so on.
  • 🧹 Cleaning up your resources after you're done with your experiments is super important yet very challenging. Many of the components need a range of other resources to work which might slide past your radar if you're not careful. For example, if your Kubernetes cluster has made use of Load Balancers, you might still have one lying around in your account even after deleting the cluster, costing you money and frustration.
All of these points make taking your pipelines to production a more difficult task than it should be. We believe that the expertise in setting up these often-complex stacks shouldn't be a prerequisite to running your ML pipelines.
Thus, to make even this process easier for our users, we have created the deploy CLI which allows you to quickly get started with a full-fledged MLOps stack using only a few commands. You can choose to deploy individual stack components through the stack-component CLI or deploy a stack with multiple components together (a tad more manual steps).

What is mlstacks?

MLStacks is a Python package that allows you to quickly spin up MLOps infrastructure using Terraform. It is designed to be used with ZenML, but can be used with any MLOps tool or platform.
The ZenML CLI has special subcommands that allow you to deploy individual stack components as well as whole stacks using MLStacks. These stacks will be useful for you if:
  • You are at the start of your MLOps journey, and would like to explore different tools.
  • You are looking for guidelines for production-grade deployments.

Deploying a stack component

The ZenML CLI allows you to deploy individual stack components using the deploy subcommand which is implemented for all supported stack components. You can find the list of supported stack components here.

Deploying a stack

For deploying a full stack, use the zenml stack deploy command. See the stack deployment page for more details of which cloud providers and stack components are supported.

How does mlstacks work?

MLStacks is built around the concept of a stack specification. A stack specification is a YAML file that describes the stack and includes references to component specification files. A component specification is a YAML file that describes a component. (Currently all deployments of components (in various combinations) must be defined within the context of a stack.)
ZenML handles the creation of stack specifications for you when you run one of the deploy subcommands using the CLI. A valid specification is generated and used by mlstacks to deploy your stack using Terraform. The Terraform definitions and state are stored in your global configuration directory along with any state files generated while deploying your stack.
Your configuration directory could be in a number of different places depending on your operating system, but read more about it in the Click docs to see which location applies to your situation.

Migration / breaking changes

The new mlstacks package is a breaking change for the syntax you might have been used to in previous versions of ZenML. Previous versions of ZenML will still work with the old syntax and you will continue to be able to pull recipes from the old ZenML mlops-stacks repository, but we encourage you to try out the new mlstacks-driven way of deploying components and stacks.
Notably and most importantly, instead of using the zenml stack recipe ... command to use and interact with the stack recipes, you will now use the zenml stack deploy ... command. Some of the flags have also been updated to reflect and support the generation of the new stack specifications. Please refer to the documentation around deploying individual stack components and deploying a stack for more details on the specific changes.
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