Deploy a stack component
Individually deploying different stack components.
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Deploy a stack component
If you have used ZenML before, you must be familiar with the flow of registering new stack components. It goes something like this:
Commands like these assume that you already have the stack component deployed. In this case, it would mean that you must already have a bucket called my_bucket
on AWS S3 to be able to use this component.
We took inspiration from this design to build something that feels natural to use and is also sufficiently powerful to take care of the deployment of the respective stack components for you. This is where the <STACK_COMPONENT> deploy
CLI comes in!
The deploy
command allows you to deploy individual components of your MLOps stack with a single command 🚀. You can also customize your components easily by passing in flags (more on that later).
For example, to deploy an MLflow tracking server on a GCP account, you can run:
The command above takes in the following parameters:
Name: The name of the stack component. In this case, it is
my_tracker
. If you don't provide a name, a random one is generated for you.Flavor: The flavor of the stack component to deploy. Here, we are deploying an MLflow experiment tracker.
Cloud: The cloud to deploy this stack component on. Currently, only GCP, AWS, and k3d are supported as providers).
Additional Configuration: Some components can be customized by the user and these settings are passed as flags to the command. In the example above, we pass the GCP project ID to select what project to deploy the component to.
Successful execution of this command does the following:
Asks for your confirmation on the resources that will be deployed.
Once you agree, it starts the deployment process and gives you a list of outputs at the end pertaining to your deployed stack component (the text in green in the screenshot below).
It also automatically registers the deployed stack component with your ZenML server, so you don't have to worry about manually configuring components after the deployment! 🤩
The command currently uses your local credentials for GCP and AWS to provision resources. Integration with your ZenML connectors might be possible soon too!
🍨 Available flavors for stack components
Here's a table of all the flavors that can be deployed through the CLI for every stack component. This is a list that will keep on growing and you can also contribute any flavor or stack component that you feel is missing. Refer to the Contribution page for steps on how to do that 😄
Component Type | Flavor |
---|---|
Experiment Tracker | mlflow |
Model Deployer | seldon |
kserve | |
Artifact Store | s3 |
gcs | |
minio | |
Orchestrator | kubernetes |
kubeflow | |
tekton | |
sagemaker | |
vertex | |
Step Operator | sagemaker |
vertex | |
Container Registry | gcr |
ecr | |
k3d-registry |
✨ Customizing your stack components
With simplicity, we didn't want to compromise on the flexibility that this deployment method allows. As such, we have added the option to pass configuration specific to the stack components as key-value arguments to the deploy CLI. Here is an assortment of all possible configurations that can be set.
Experiment Trackers
You can assign an existing bucket to the MLflow experiment tracker by using the --mlflow_bucket
flag:
Artifact Stores
For an artifact store, you can pass bucket_name
as an argument to the command.
Container Registries
For container registries, you can pass the repository name using repo_name
:
This is only useful for the AWS case since AWS requires a repository to be created before pushing images to it and the deploy command ensures that a repository with the name you provide is created. In case of GCP and other providers, you can choose the repository name at the same time as you are pushing the image via code. This is achieved through setting the target_repo
attribute of the DockerSettings
object.
Other configuration
You can also pass a region to deploy your resources to in the case of AWS and GCP recipes. For example, to deploy an S3 artifact store in the
us-west-2
region, you can run:
The default region is eu-west-1
for AWS and europe-west1
for GCP.
Changing regions is not recommended as it can lead to unexpected results for components that share infrastructure like Kubernetes clusters. If you must do so, please destroy all the stack components from the older region by running the destroy
command and then redeploy using the deploy
command.
In the case of GCP components, it is required that you pass a project ID to the command for the first time you're creating any GCP resource. The command will remember the project ID for subsequent calls.
🧹 Destroying deployed stack components
You can destroy a stack component using the destroy
subcommand. For example, to destroy an S3 artifact store you had previously created, you could run:
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