Ask or search…

Interact with secrets

Managing your secrets with ZenML.

How to create a secret

Python SDK
Head to the Secrets section in the sidebar and click Register Secret in the bottom left and you should be guided through the rest of the process.
Registering a Secret in the Dashboard
To create a secret with a name <SECRET_NAME> and a key-value pair, you can run the following CLI command:
zenml secret create <SECRET_NAME> \
--<KEY_1>=<VALUE_1> \
# Another option is to use the '--values' option and provide key-value pairs in either JSON or YAML format.
zenml secret create <SECRET_NAME> \
Alternatively, you can create the secret in an interactive session (in which ZenML will query you for the secret keys and values) by passing the --interactive/-i parameter:
zenml secret create <SECRET_NAME> -i
For secret values that are too big to pass as a command line argument, or have special characters, you can also use the special @ syntax to indicate to ZenML that the value needs to be read from a file:
zenml secret create <SECRET_NAME> \
--key=@path/to/file.txt \
# Alternatively, you can utilize the '--values' option by specifying a file path containing key-value pairs in either JSON or YAML format.
zenml secret create <SECRET_NAME> \
The CLI also includes commands that can be used to list, update and delete secrets. A full guide on using the CLI to create, access, update and delete secrets is available here.
Interactively register missing secrets for your stack
If you're using components with secret references in your stack, you need to make sure that all the referenced secrets exist. To make this process easier, you can use the following CLI command to interactively register all secrets for a stack:
zenml stack register-secrets [<STACK_NAME>]
The ZenML client API offers a programmatic interface to create, e.g.:
from zenml.client import Client
client = Client()
"username": "admin",
"password": "abc123"
Other Client methods used for secrets management include get_secret to fetch a secret by name or id, update_secret to update an existing secret, list_secrets to query the secrets store using a variety of filtering and sorting criteria, and delete_secret to delete a secret. The full Client API reference is available here.

Set scope for secrets

ZenML secrets can be scoped to a workspace or a user. This allows you to create secrets that are only accessible within a specific workspace or to one user.
By default, all created secrets are scoped to the active workspace. To create a secret and scope it to your active user instead, you can pass the --scope argument to the CLI command:
zenml secret create <SECRET_NAME> \
--scope user \
--<KEY_1>=<VALUE_1> \
Scopes also act as individual namespaces. When you are referencing a secret by name in your pipelines and stacks, ZenML will first look for a secret with that name scoped to the active user, and if it doesn't find one, it will look for one in the active workspace.

Accessing registered secrets

Reference secrets in stack component attributes and settings

Some of the components in your stack require you to configure them with sensitive information like passwords or tokens so they can connect to the underlying infrastructure. Secret references allow you to configure these components in a secure way by not specifying the value directly but instead referencing a secret by providing the secret name and key. Referencing a secret for the value of any string attribute of your stack components, simply specify the attribute using the following syntax: {{<SECRET_NAME>.<SECRET_KEY>}}
For example:
In the dashboard values that are considered Secret can be set using registered secrets with the Syntax mentioned above: {{<SECRET_NAME>.<SECRET_KEY>}}. Alternatively, the values you enter at creation time will be saved as a secret.
Registering a stack component with secret values.
# Register a secret called `mlflow_secret` with key-value pairs for the
# username and password to authenticate with the MLflow tracking server
# Using central secrets management
zenml secret create mlflow_secret \
--username=admin \
# Then reference the username and password in our experiment tracker component
zenml experiment-tracker register mlflow \
--flavor=mlflow \
--tracking_username={{mlflow_secret.username}} \
--tracking_password={{mlflow_secret.password}} \
When using secret references in your stack, ZenML will validate that all secrets and keys referenced in your stack components exist before running a pipeline. This helps us fail early so your pipeline doesn't fail after running for some time due to some missing secret.
This validation by default needs to fetch and read every secret to make sure that both the secret and the specified key-value pair exist. This can take quite some time and might fail if you don't have permission to read secrets.
You can use the environment variable ZENML_SECRET_VALIDATION_LEVEL to disable or control the degree to which ZenML validates your secrets:
  • Setting it to NONE disables any validation.
  • Setting it to SECRET_EXISTS only validates the existence of secrets. This might be useful if the machine you're running on only has permission to list secrets but not actually read their values.
  • Setting it to SECRET_AND_KEY_EXISTS (the default) validates both the secret existence as well as the existence of the exact key-value pair.

Fetch secret values in a step

If you are using centralized secrets management, you can access secrets directly from within your steps through the ZenML Client API. This allows you to use your secrets for querying APIs from within your step without hard-coding your access keys:
from zenml import step
from zenml.client import Client
def secret_loader() -> None:
"""Load the example secret from the server."""
# Fetch the secret from ZenML.
secret = Client().get_secret( < SECRET_NAME >)
# `secret.secret_values` will contain a dictionary with all key-value
# pairs within your secret.
ZenML Scarf