Notifications

MLRun supports configuring notifications on jobs and scheduled jobs. This section describes the SDK for notifications.

• The notification object

• Local vs. remote

• Notification parameters and secrets

• Notification kinds

• Mail notifications

• Configuring notifications for runs

• Configuring notifications for pipelines

• Setting notifications on live runs

• Setting notifications on scheduled runs

• Notification conditions

The notification object

See Notification.

Local vs. remote

Notifications can be sent either locally from the SDK, or remotely from the MLRun API. Usually, a local run sends locally, and a remote run sends remotely. However, there are several special cases where the notification is sent locally either way. These cases are:

• Local: To conserve backwards compatibility, the SDK sends the notifications as it did before adding the run notifications mechanism. This means you need to watch the pipeline in order for its notifications to be sent. (Remote pipelines act differently. See Configuring Notifications For Pipelines for more details.

• Dask: Dask runs are always local (against a remote Dask cluster), so the notifications are sent locally as well.

Disclaimer: Notifications of local runs aren't persisted.

Notification parameters and secrets

The notification parameters often contain sensitive information, such as Slack webhooks, Git tokens, etc. To ensure the safety of this sensitive data, the parameters are split into two objects - params and secret_params. Either can be used to store any notification parameter. However the secret_params is protected by project secrets. When a notification is created, its secret_params are automatically masked and stored in an mlrun project secret. The name of the secret is built from the hash of the parameters themselves (so if multiple notifications use the same secret, it doesn't waste space in the project secret). Inside the notification's secret_params, you'll find a reference to the secret under the secret key after it's masked. For non-sensitive notification parameters, you can simply use the params parameter, which doesn't go through this masking process. It's essential to utilize secret_params exclusively for handling sensitive information, ensuring secure data management.

Notification kinds

See NotificationKind.

Mail notifications

To send mail notifications, you need an existing SMTP server.

mail_notification = mlrun.model.Notification(
    kind="mail",
    when=["completed", "error", "running"],
    name="mail-notification",
    message="",
    condition="",
    severity="verbose",
    params={
        "start_tls": True,
        "use_tls": False,
        "validate_certs": False,
        "email_addresses": ["user.name@domain.com"],
    },
)

We use the aiosmtplib library for sending mail notifications. The params argument is a dictionary, that supports the following fields:

• server_host (string): The SMTP server host.

• server_port (int): The SMTP server port.

• sender_address (string): The sender email address.

• username (string): The username for the SMTP server.

• password (string): The password for the SMTP server.

• email_addresses (list of strings): The list of email addresses to send the mail to.

• start_tls (boolean): Whether to start the TLS connection.

• use_tls (boolean): Whether to use TLS.

• validate_certs (boolean): Whether to validate the certificates.

You can read more about start_tls and use_tls on the aiosmtplib docs. Missing params are enriched with default values which can be configured in the mlrun-smtp-config kubernetes (see below).

MLRun on Iguazio

If MLRun is deployed on the Iguazio platform, an SMTP server already exists. To use it, run the following (with privileged user - IT Admin):

import mlrun

mlrun.get_run_db().refresh_smtp_configuration()

The refresh_smtp_configuration method will get the smtp configuration from the Iguazio platform and set it as the default smtp configuration (create a mlrun-smtp-config with the smtp configuration). If you edit the configuration on the Iguazio platform, you should run the refresh_smtp_configuration method again.

MLRun CE

In the community edition, you can use your own SMTP server. To configure it, manually create the mlrun-smtp-config kubernetes secret with the default params for the SMTP server (server_host, server_port, username, password, etc…). After creating or editing the secret, refresh the mlrun SMTP configuration by running the refresh_smtp_configuration method.

Configuring notifications for runs

In any run method you can configure the notifications via their model. For example:

notification = mlrun.model.Notification(
    kind="webhook",
    when=["completed", "error", "running"],
    name="notification-1",
    message="completed",
    severity="info",
    secret_params={"url": "<webhook url>"},
    params={"method": "GET", "verify_ssl": True},
)
function.run(handler=handler, notifications=[notification])

To add run details to the notification:

notifications_func = [
    mlrun.model.Notification.from_dict(
        {
            "kind": "webhook",
            "name": "Test",
            "severity": "info",
            "when": ["error", "completed"],
            "condition": "",
            "params": {
                "url": webhook_test,
                "method": "POST",
                "override_body": {"message": "Run Completed {{ runs }}"},
            },
        }
    ),
]

The results look like:

{
  "message": "Run Completed [{'project': 'test-remote-workflow', 'name': 'func-func', 'host': 'func-func-pkt97', 'status': {'state': 'completed', 'results': {'return': 1}}}]"
}

Configuring notifications for pipelines

To set notifications on pipelines, supply the notifications in the run method of either the project or the pipeline. For example:

notification = mlrun.model.Notification(
    kind="webhook",
    when=["completed", "error", "running"],
    name="notification-1",
    message="completed",
    severity="info",
    secret_params={"url": "<webhook url>"},
    params={"method": "GET", "verify_ssl": True},
)
project.run(..., notifications=[notification])

Running notifications

MLRun can also send a pipeline started notification. To do that, configure a notification that includes when=running. The pipeline started notification uses its own parameters, for example the webhook, credentials, etc., for the notification message. You can set only the webhook; the message is the default message.

If the webhook is stored in the secret_params, you should first set the project secret and then use this project secret in the notification. For example:

import mlrun

project = mlrun.get_or_create_project("ycvqowgpie")
project.set_secrets({"SLACK_SECRET1": '{"webhook":"<WEBHOOK_URL>"}'})
slack_notification = mlrun.model.Notification(
    kind="slack",
    when=["running"],
    name="name",
    condition="",
    secret_params={"secret": "SLACK_SECRET1"},
)

Remote pipeline notifications

In remote pipelines, the pipeline end notifications are sent from the MLRun API. This means you don't need to watch the pipeline in order for its notifications to be sent. The pipeline start notification is still sent from the SDK when triggering the pipeline.

Local and KFP engine pipeline notifications

In these engines, the notifications are sent locally from the SDK. This means you need to watch the pipeline in order for its notifications to be sent. This is a fallback to the old notification behavior, therefore not all of the new notification features are supported. Only the notification kind and params are taken into account. In these engines the old way of setting project notifiers is still supported:

project.notifiers.add_notification(
    notification_type="slack", params={"webhook": "<slack webhook url>"}
)
project.notifiers.add_notification(
    notification_type="git",
    params={"repo": "<repo>", "issue": "<issue>", "token": "<token>"},
)

Instead of passing the webhook in the notification params, it is also possible in a Jupyter notebook to use the %env magic command:

%env SLACK_WEBHOOK=<slack webhook url>

Editing and removing notifications is done similarly with the following methods:

project.notifiers.edit_notification(
    notification_type="slack", params={"webhook": "<new slack webhook url>"}
)
project.notifiers.remove_notification(notification_type="slack")

Setting notifications on live runs

You can set notifications on live runs via the set_run_notifications method. For example:

import mlrun

mlrun.get_run_db().set_run_notifications(
    "<project-name>", "<run-uid>", [notification1, notification2]
)

Using the set_run_notifications method overrides any existing notifications on the run. To delete all notifications, pass an empty list.

Setting notifications on scheduled runs

You can set notifications on scheduled runs via the set_schedule_notifications method. For example:

import mlrun

mlrun.get_run_db().set_schedule_notifications(
    "<project-name>", "<schedule-name>", [notification1, notification2]
)

Using the set_schedule_notifications method overrides any existing notifications on the schedule. To delete all notifications, pass an empty list.

Notification conditions

You can configure the notification to be sent only if the run meets certain conditions. This is done using the condition parameter in the notification object. The condition is a string that is evaluated using a jinja templator with the run object in its context. The jinja template should return a boolean value that determines whether the notification is sent or not. If any other value is returned or if the template is malformed, the condition is ignored and the notification is sent as normal.

Take the case of a run that calculates and outputs model drift. This example code sets a notification to fire only if the drift is above a certain threshold:

notification = mlrun.model.Notification(
    kind="slack",
    when=["completed", "error"],
    name="notification-1",
    message="completed",
    severity="info",
    secret_params={"webhook": "<slack webhook url>"},
    condition='{{ run["status"]["results"]["drift"] > 0.1 }}',
)