Deploy a stack using Stack Recipes

Deploying an entire stack with the ZenML Stack Recipes.

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Deploy a stack using Stack Recipes

A Stack Recipe is a collection of carefully crafted Terraform modules and resources which, when executed, creates a range of stack components that can be used to run your pipelines. Each recipe is designed to offer a great deal of flexibility in configuring the resources while preserving the ease of application through the use of sensible defaults.

Check out the full list of available recipes at the mlops-stacks repository.

When should I use the stack recipes?

To answer this question, here are some pros and cons in comparison to the stack-component deploy method which can help you choose what works best for you!

😍 Pros

• Offers a lot of flexibility in what you deploy. Stack recipes need to be pulled before you can apply them, and you can choose to edit any of the terraform files as you see fit to alter the end result.

• This also allows you add custom components that you don't want the public to use, to the existing recipes, for a complete deployment experience.

• Running a stack recipe gives you a full MLOps stack as the output. You get a stack YAML file that you can directly import to ZenML. This saves you the effort of manually running different stack-component deploy commands.

😥 Cons

• It involves more manual steps as compared to the stack-component deploy.

• You have to directly edit the terraform files (either the locals.tf or the variables.tf if you want to customize the deployment. The stack-component CLI, on the other hand, gives you a Python interface for you to avoid dipping your toes in HCL code at all.

We recommend the use of modular recipes going forward if you're deploying on GCP, AWS or k3d. These recipes allow you to mix and match from a variety of different components instead of relying on an opinionated stack component selection with the legacy recipes.

Deploying a recipe 🚀

Detailed steps are available in the README of the respective stack recipes but here's what a simple flow could look like:

1. 📃 List all available recipes in the repository.

   zenml stack recipe list
    

2. Pull the recipe that you wish to deploy to your local system.

   zenml stack recipe pull <STACK_RECIPE_NAME>

3. 🎨 Customize your deployment by editing the default values in the locals.tf file. This file holds all the configurable parameters for each of the stack components.

4. 🔐 Enable services and add your secret information like keys and passwords into the values.tfvars.json file which is not committed and only exists locally. You can learn what values can be configured here by taking a look at the variables.tf file.

5. 🚀 Deploy the recipe with the following command. Let's take the example of deploying a Kubeflow, MLflow and Seldon stack on GCP.

```
zenml stack recipe deploy gcp-modular \
--enable_orchestrator_kubeflow=true --enable_model_deployer_seldon=true --enable_experiment_tracker_mlflow=true --no-server
```

If you want to allow ZenML to automatically import the created resources as a ZenML stack, pass the --import flag to the command above. By default, the imported stack will have the same name as the stack recipe and you can provide your own custom name with the --stack-name option.

This command will detect and deploy a ZenML server if you are not connected to one already. To prevent a server deployment, use the --no-server flag.

Once the recipe is deployed, you'll notice that a ZenML stack configuration file gets created. This YAML file can be imported as a ZenML stack manually by running the following command 🤯!

zenml stack import <STACK_NAME> -f <PATH_TO_THE_CREATED_STACK_CONFIG_YAML>

Want more details on how this works internally?

The stack recipe CLI interacts with the mlops-stacks repository to fetch the recipes and stores them locally in the Global Config directory. From here, they are pulled to your local directory or whatever directory you specify in the --path flag for the CLI.

This is what you see and where you can make any changes you want to the recipe files. You can also use native terraform commands like terraform apply to deploy components but this would require you to pass the variables manually using the -var-file flag to the terraform CLI.

Workflow behind using the stack recipe CLI

Deleting resources

1. 🗑️ Once you're done running your pipelines, there's only a single command you need to execute that will take care of cleaning up all the resources that you had created on your cloud.

   zenml stack recipe destroy <STACK_RECIPE_NAME>

2. (Optional) 🧹 You can also remove all the downloaded recipe files from the pull execution by using the clean command.

   zenml stack recipe clean

Check out the API docs to learn more about each of these commands and the options that are available.