0.55.3
Ask or search…
K
Links

Slack Alerter

Sending automated alerts to a Slack channel.
The SlackAlerter enables you to send messages to a dedicated Slack channel directly from within your ZenML pipelines.
The slack integration contains the following two standard steps:
  • slack_alerter_post_step takes a string message or a custom Slack block, posts it to a Slack channel, and returns whether the operation was successful.
  • slack_alerter_ask_step also posts a message or a custom Slack block to a Slack channel, but waits for user feedback, and only returns True if a user explicitly approved the operation from within Slack (e.g., by sending "approve" / "reject" to the bot in response).
Interacting with Slack from within your pipelines can be very useful in practice:
  • The slack_alerter_post_step allows you to get notified immediately when failures happen (e.g., model performance degradation, data drift, ...),
  • The slack_alerter_ask_step allows you to integrate a human-in-the-loop into your pipelines before executing critical steps, such as deploying new models.

How to use it

Requirements

Before you can use the SlackAlerter, you first need to install ZenML's slack integration:
zenml integration install slack -y
See the Integrations page for more details on ZenML integrations and how to install and use them.

Setting Up a Slack Bot

In order to use the SlackAlerter, you first need to have a Slack workspace set up with a channel that you want your pipelines to post to.
Then, you need to create a Slack App with a bot in your workspace.
Make sure to give your Slack bot chat:write and chat:write.public permissions in the OAuth & Permissions tab under Scopes.

Registering a Slack Alerter in ZenML

Next, you need to register a slack alerter in ZenML and link it to the bot you just created. You can do this with the following command:
zenml alerter register slack_alerter \
--flavor=slack \
--slack_token=<SLACK_TOKEN> \
--default_slack_channel_id=<SLACK_CHANNEL_ID>
Here is where you can find the required parameters:
  • <SLACK_CHANNEL_ID>: Open your desired Slack channel in a browser, and copy out the last part of the URL starting with C.....
  • <SLACK_TOKEN>: This is the Slack token of your bot. You can find it in the Slack app settings under OAuth & Permissions. IMPORTANT: Please make sure that the token is the Bot User OAuth Token not the User OAuth Token.
Slack Token Image
After you have registered the slack_alerter, you can add it to your stack like this:
zenml stack register ... -al slack_alerter

How to Use the Slack Alerter

After you have a SlackAlerter configured in your stack, you can directly import the slack_alerter_post_step and slack_alerter_ask_step steps and use them in your pipelines.
Since these steps expect a string message as input (which needs to be the output of another step), you typically also need to define a dedicated formatter step that takes whatever data you want to communicate and generates the string message that the alerter should post.
As an example, adding slack_alerter_ask_step() to your pipeline could look like this:
from zenml.integrations.slack.steps.slack_alerter_ask_step import slack_alerter_ask_step
from zenml import step, pipeline
@step
def my_formatter_step(artifact_to_be_communicated) -> str:
return f"Here is my artifact {artifact_to_be_communicated}!"
@pipeline
def my_pipeline(...):
...
artifact_to_be_communicated = ...
message = my_formatter_step(artifact_to_be_communicated)
approved = slack_alerter_ask_step(message)
... # Potentially have different behavior in subsequent steps if `approved`
if __name__ == "__main__":
my_pipeline()
An example of adding a custom Slack block as part of any alerter logic for your pipeline could look like this:
from typing import List, Dict
from zenml.integrations.slack.steps.slack_alerter_ask_step import slack_alerter_post_step
from zenml.integrations.slack.alerters.slack_alerter import SlackAlerterParameters
from zenml import step, pipeline
@step
def my_custom_block_step(block_message) -> List[Dict]:
my_custom_block = [
{
"type": "header",
"text": {
"type": "plain_text",
"text": f":tada: {block_message}",
"emoji": true
}
}
]
return SlackAlerterParameters(blocks = my_custom_block)
@pipeline
def my_pipeline(...):
...
message_blocks = my_custom_block_step("my custom block!")
post_message = slack_alerter_post_step(params = message_blocks)
return post_message
if __name__ == "__main__":
my_pipeline()
For more information and a full list of configurable attributes of the Slack alerter, check out the API Docs .
ZenML Scarf