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Cli

zenml.cli special

ZenML CLI

The ZenML CLI tool is usually downloaded and installed via PyPI and a pip install zenml command. Please see the Installation & Setup section above for more information about that process.

How to use the CLI

Our CLI behaves similarly to many other CLIs for basic features. In order to find out which version of ZenML you are running, type:

   zenml version

If you ever need more information on exactly what a certain command will do, use the --help flag attached to the end of your command string.

For example, to get a sense of all the commands available to you while using the zenml command, type:

   zenml --help

If you were instead looking to know more about a specific command, you can type something like this:

   zenml metadata-store register --help

This will give you information about how to register a metadata store. (See below for more on that).

Beginning a Project

In order to start working on your project, initialize a ZenML repository within your current directory with ZenML’s own config and resource management tools:

zenml init

This is all you need to begin using all the MLOps goodness that ZenML provides!

By default, zenml init will install its own hidden .zen folder inside the current directory from which you are running the command. You can also pass in a directory path manually using the --repo_path option:

zenml init --repo_path /path/to/dir

If you wish to specify that you do not want analytics to be transmitted back to ZenML about your usage of the tool, pass in False to the --analytics_opt_in option:

zenml init --analytics_opt_in false

If you wish to delete all data relating to your project from the directory, use the zenml clean command. This will:

  • delete all pipelines
  • delete all artifacts
  • delete all metadata

Note that the clean command is not implemented for the current version.

Loading and using pre-built examples

If you don’t have a project of your own that you’re currently working on, or if you just want to play around a bit and see some functional code, we’ve got your back! You can use the ZenML CLI tool to download some pre-built examples.

We know that working examples are a great way to get to know a tool, so we’ve made some examples for you to use to get started. (This is something that will grow as we add more).

To list all the examples available to you, type:

zenml example list

If you want more detailed information about a specific example, use the info subcommand in combination with the name of the example, like this:

zenml example info quickstart

If you want to pull all the examples into your current working directory (wherever you are executing the zenml command from in your terminal), the CLI will create a zenml_examples folder for you if it doesn’t already exist whenever you use the pull subcommand. The default is to copy all the examples, like this:

zenml example pull

If you’d only like to pull a single example, add the name of that example (for example, quickstart) as an argument to the same command, as follows:

zenml example pull quickstart

If you would like to force-redownload the examples, use the --force or -f flag as in this example:

zenml example pull --force

This will redownload all the examples afresh, using the same version of ZenML as you currently have installed. If for some reason you want to download examples corresponding to a previous release of ZenML, use the --version or -v flag to specify, as in the following example:

zenml example pull --force --version 0.3.8

If you wish to run the example, allowing the ZenML CLI to do the work of setting up whatever dependencies are required, use the run subcommand:

zenml example run quickstart

Using integrations

Integrations are the different pieces of a project stack that enable custom functionality. This ranges from bigger libraries like kubeflow for orchestration down to smaller visualization tools like facets. Our CLI is an easy way to get started with these integrations.

To list all the integrations available to you, type:

zenml integration list

To see the requirements for a specific integration, use the requirements command:

zenml integration requirements INTEGRATION_NAME

If you wish to install the integration, using the requirements listed in the previous command, install allows you to do this for your local environment:

zenml integration install INTEGRATION_NAME

Note that if you don't specify a specific integration to be installed, the ZenML CLI will install all available integrations.

Uninstalling a specific integration is as simple as typing:

zenml integration uninstall INTEGRATION_NAME

Customizing your Metadata Store

The configuration of each pipeline, step, backend, and produced artifacts are all tracked within the metadata store. By default, ZenML initializes your repository with a metadata store kept on your local machine. If you wish to register a new metadata store, do so with the register command:

zenml metadata-store register METADATA_STORE_NAME --type METADATA_STORE_TYPE [--OPTIONS]

If you wish to list the metadata stores that have already been registered within your ZenML project / repository, type:

zenml metadata-store list

If you wish to delete a particular metadata store, pass the name of the metadata store into the CLI with the following command:

zenml metadata-store delete METADATA_STORE_NAME

Customizing your Artifact Store

The artifact store is where all the inputs and outputs of your pipeline steps are stored. By default, ZenML initializes your repository with an artifact store with everything kept on your local machine. If you wish to register a new artifact store, do so with the register command:

zenml artifact-store register ARTIFACT_STORE_NAME --type ARTIFACT_STORE_TYPE [--OPTIONS]

If you wish to list the artifact stores that have already been registered within your ZenML project / repository, type:

zenml artifact-store list

If you wish to delete a particular artifact store, pass the name of the artifact store into the CLI with the following command:

zenml artifact-store delete ARTIFACT_STORE_NAME

Customizing your Orchestrator

An orchestrator is a special kind of backend that manages the running of each step of the pipeline. Orchestrators administer the actual pipeline runs. By default, ZenML initializes your repository with an orchestrator that runs everything on your local machine.

If you wish to register a new orchestrator, do so with the register command:

zenml orchestrator register ORCHESTRATOR_NAME --type ORCHESTRATOR_TYPE [--ORCHESTRATOR_OPTIONS]

If you wish to list the orchestrators that have already been registered within your ZenML project / repository, type:

zenml orchestrator list

If you wish to delete a particular orchestrator, pass the name of the orchestrator into the CLI with the following command:

zenml orchestrator delete ORCHESTRATOR_NAME

Customizing your Container Registry

The container registry is where all the images that are used by a container-based orchestrator are stored. By default, a default ZenML local stack will not register a container registry. If you wish to register a new container registry, do so with the register command:

zenml container-registry register REGISTRY_NAME --type REGISTRY_TYPE [--REGISTRY_OPTIONS]

If you want the name of the current container registry, use the get command:

zenml container-registry get

To list all container registries available and registered for use, use the list command:

zenml container-registry list

For details about a particular container registry, use the describe command. By default, (without a specific registry name passed in) it will describe the active or currently used container registry:

zenml container-registry describe [REGISTRY_NAME]

To delete a container registry (and all of its contents), use the delete command:

zenml container-registry delete

Setting up a Secrets Manager

ZenML offers a way to securely store secrets associated with your project. To set up a local file-based secrets manager, use the following CLI command:

zenml secrets-manager register SECRETS_MANAGER_NAME -t local

This can then be used as part of your Stack (see below).

Using Secrets

Secrets are administered by the Secrets Manager. You must first register that and then register a stack that includes the secrets manager before you can start to use it. To get a full list of all the possible commands, type zenml secret --help. A ZenML Secret is a collection or grouping of key-value pairs. These Secret groupings come in different types, and certain types have predefined keys that should be used. For example, an AWS secret has predefined keys of aws_access_key_id and aws_secret_access_key (and an optional aws_session_token). If you do not have a specific secret type you wish to use, ZenML will use the arbitrary type to store your key-value pairs.

To register a secret, use the register command:

zenml secret register SECRET_NAME

If you wish to register a single secret non-interactively, you can pass in a key-value pair at the command line, as in the following example:

zenml secret register SECRET_NAME -k KEY -v VALUE

Note that the key and value will be preserved in your bash_history file, so you may prefer to use the interactive register command instead.

To list all the secrets available, use the list command:

zenml secret list

To get the key-value pairs for a particular secret, use the get command:

zenml secret get SECRET_NAME

To update a secret, use the update command:

zenml secret update SECRET_NAME

If you wish to update a single secret non-interactively, you can pass in a key-value pair at the command line, as in the following example:

zenml secret update SECRET_NAME -k KEY -v NEW_VALUE

Note that the key and value will be preserved in your bash_history file, so you may prefer to use the interactive update command instead.

Finally, to delete a secret, use the delete command:

zenml secret delete SECRET_NAME

Add a Feature Store to your Stack

ZenML supports connecting to a Redis-backed Feast feature store as a stack component integration. To set up a feature store, use the following CLI command:

```shell zenml feature-store register FEATURE_STORE_NAME -t feast --feast_repo=REPO_PATH --online_host HOST_NAME --online_port ONLINE_PORT_NUMBER


Once you have registered your feature store as a stack component, you can use it
in your ZenML Stack.

Interacting with Deployed Models
-------------

Deployed models are


If you want to simply see what models have been deployed within your stack, run
the following command:

```bash
zenml served-models list

This should give you a list of served models containing their uuid, the name of the pipeline that produced them including the run id and the step name as well as the status. This information should help you identify the different models.

If you want further information about a specific model, simply copy the UUID and the following command.

zenml served-models describe <UUID>

If you are only interested in the prediction-url of the specific model you can also run:

zenml served-models get-url <UUID>

Finally, you will also be able to start/stop the services using the following two commands:

zenml served-models start <UUID>
zenml served-models stop <UUID>

If you want to completely remove a served model you can also irreversibly delete it using:

zenml served-models delete <UUID>
Administering the Stack
-----------------------

The stack is a grouping of your artifact store, your metadata store and
your orchestrator. With the ZenML tool, switching from a local stack to
a distributed cloud environment can be accomplished with just a few CLI
commands.

To register a new stack, you must already have registered the individual
components of the stack using the commands listed above.

Use the ``zenml stack register`` command to register your stack. It
takes four arguments as in the following example:

```bash
zenml stack register STACK_NAME        -m METADATA_STORE_NAME        -a ARTIFACT_STORE_NAME        -o ORCHESTRATOR_NAME

Each corresponding argument should be the name you passed in as an identifier for the artifact store, metadata store or orchestrator when you originally registered it. (If you want to use your secrets manager, you should pass its name in with the -x option flag.)

To list the stacks that you have registered within your current ZenML project, type:

zenml stack list

To delete a stack that you have previously registered, type:

zenml stack delete STACK_NAME

By default, ZenML uses a local stack whereby all pipelines run on your local computer. If you wish to set a different stack as the current active stack to be used when running your pipeline, type:

zenml stack set STACK_NAME

This changes a configuration property within your local environment.

To see which stack is currently set as the default active stack, type:

zenml stack get