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Monitoring Service Posting Slack Alerts

The Slack alerts example runs as a ClearML service, which monitors the completion and failure of Tasks, and posts alert messages on a specified Slack channel. In the example, we configure the Slack details when creating a Slack bot, and set parameters for monitoring. The Task name is Slack Alerts, and it is associated with the project Monitoring.

Slack Alerts executes in ClearML services mode and is configurable. It is pre-loaded in ClearML Server and its status is Draft (editable). Set the parameter values in the ClearML Web UI, and then enqueue the Task to the services queue. Or, run the script slack_alerts.py, with options to run locally, or enqueue the Task to the services queue.

Prerequisites#

Creating a Slack Bot#

Before configuring and running the Slack alert service, create a new Slack Bot (ClearML Bot).

important

The Slack API token and channel you create are required to configure the Slack alert service.

  1. Login to your Slack account.
  2. Go to https://api.slack.com/apps/new.
  3. In App Name, enter an app name; for example, "ClearML Bot".
  4. In Development Slack Workspace, select a workspace.
  5. Click Create App.
  6. In Basic Information, under Display Information, complete the following:
    • In Short description, enter "Allegro Train Bot".
    • In Background color, enter "#202432".
  7. Click Save Changes.
  8. In OAuth & Permissions, under Scopes, click Add an OAuth Scope, and then select the following permissions on the list:
    • channels:join
    • channels:read
    • chat:write
  9. In OAuth Tokens & Redirect URLs:
    1. Click Install App to Workspace
    2. In the confirmation dialog, click Allow.
    3. Click Copy to copy the Bot User OAuth Access Token.

Running the Service#

There are two options to run the Slack alerts service:

Running Using the ClearML Web UI#

Step 1. Configuring the Service#

  1. In the ClearML Web UI Projects page, click the Monitoring project > click the Slack Alerts Task.

  2. In the info panel, click the CONFIGURATION tab.

  3. In the GENERAL section, hover over the parameter area > EDIT.

  4. Configure the service parameters:

    • channel - The name of the Slack channel (MANDATORY).
    • include_completed_experiments - (bool) Whether to include completed experiments:
      • True - Include
      • False - Do not include (default)
    • include_manual_experiments - Whether to include experiments that are running locally:
      • True - Monitor local experiments, and remote experiments executed by ClearML Agent (default).
      • False - Remote experiments, only.
    • local - Run the monitor locally, instead of as a service. The default is False.
    • message_prefix - A message prefix. For example, to alert all channel members use: "Hey <!here>,"
    • min_num_iterations - The minimum number of iterations of failed/completed experiment to alert. The default is 0, indicating all alerts.
    • project - The name (or partial name) of the project to monitor, use empty for all projects.
    • refresh_rate - How often to run the monitoring service (seconds). The default value is 10.0.
    • service_queue - The queue that clearml-agent is listening to for Tasks to execute as a service. The default is services.
    • slack_api - The Slack API key. The default value can be set in the environment variable, SLACK_API_TOKEN (MANDATORY).

Step 2. Enqueuing the Service#

  • Right click the Monitoring Task > Enqueue > Select services > ENQUEUE.

Running Using the Script#

The slack_alerts.py allows to configure the monitoring service, and then either:

  • Run locally
  • Run in ClearML Agent services mode

To run the monitoring service locally:

python slack_alerts.py --channel <Slack-channel-name> --slack-api <Slack-API-token> --local True [...]

where,

  • channel - The Slack channel where alerts will be posted.
  • slack_api - Slack API key.
  • local - Run the monitoring service only locally. If True, then run locally. If False, then run locally and enqueue the Task to run in ClearML Agent services mode.

slack_alerts.py supports the following additional command line options:

  • message_prefix - The default value is an empty string.
  • min_num_iterations - Minimum number of iterations of failed / completed experiment to alert. Use this option to eliminate debug sessions that fail quickly. The default value is 0 (alerts for experiments).
  • include_manual_experiments - Include experiments running manually (i.e. not by clearml-agent). The default value is False.
  • include_completed_experiments - If False, then include send alerts for failed Tasks, only. If True, then send alert for completed and failed Tasks. The default value is False.
  • refresh_rate - How often to check the experiments, in seconds. The default value is 10 (seconds).
  • service_queue - The queue to use when running as a service. The default value is services.
  • local - If True, run locally only instead of as a service. If False, then automatically enqueue the Task to run in ClearML Agent services mode. The default value is False.

Additional Information about slack_alerts.py#

In slack_alerts.py, the class SlackMonitor inherits from the Monitor class in clearml.automation.monitor. SlackMonitor overrides the following Monitor class methods:

  • get_query_parameters - Get the query parameters for Task monitoring.
  • process_task - Get the information for a Task, post a Slack message, and output to console.

The example provides the option to run locally or execute remotely by calling the Task.execute_remotely method.

To interface to Slack, the example uses slack.WebClient and slack.errors.SlackApiError.