# Notification Triggers Notification triggers launch workloads automatically when events happen **inside Valohai**. Perfect for building event-driven ML workflows that react to data uploads, model approvals, or execution completions. *** ## Why Use Notification Triggers? **Event-driven automation:** React immediately when things happen in Valohai **Zero manual intervention:** Workflows advance automatically from step to step **Data freshness:** Process new datasets the moment they're uploaded **ML lifecycle automation:** Deploy models immediately when approved **Pipeline orchestration:** Chain workflows without manual clicks *** ## How Notification Triggers Work Notification triggers have two key components: ### 1. Managed Trigger Channel When you create a trigger of the `Notification` type, Valohai automatically creates a special notification channel for it. Notifications sent to this channel will be handled by the associated trigger. You'll see this as `Launches trigger: ` in your notification settings. ### 2. Notification Routing You connect specific events to your trigger channel through notification routing: 1. Choose which event type to monitor (e.g., "dataset version created") 2. Optionally filter by users (all users or specific ones) 3. Select your trigger's managed channel as a destination channel When the event occurs, the notification fires and launches your configured trigger action. *** ## Supported Events Notification triggers can respond to these Valohai events: ### Dataset Events * **Dataset version created** > :bulb:Useful for: Auto-preprocessing, validation, training on fresh data ### Datum Alias Events * **Datum alias is updated** ### Model Events * Model latest approved version changes * Model tags changed * Model version is created * Model version status change * Model version tags change > :bulb:Useful for: Auto-deployment, version tracking, downstream updates ### Execution Events * Execution is created * Execution is stopped * Execution completes * Execution fails * Watchdog detects a failed execution > :bulb:Useful for: Pipeline chaining, conditional logic, error handling ### Pipeline Events * Pipeline completes * Pipeline errors * Pipeline node approval required * Pipeline stops > :bulb:Useful for: Multi-stage workflows, reporting, deployment triggers ### Task Events * Task completes * Task fails * Task stops ### Deployment Events * Deployment finishes ### Resource Utilization Events * Worker CPU was underutilized * Worker memory was underutilized * Worker GPU memory was underutilized * Worker GPU processor was underutilized ### User Related Events * User is added as collaborator to project * User is added to team * Mentioned in a comment *** ## Common Notification Trigger Patterns ### Pattern: Auto-Process New Datasets **Goal:** Every time new dataset version is created, automatically preprocess it **Setup:** 1. Create notification trigger that runs preprocessing pipeline 2. Configure trigger to receive dataset version as input 3. Set notification routing: "dataset version created" → trigger channel **Result:** Upload data → preprocessing starts automatically → clean data ready for training [**Full dataset version trigger guide →**](/automation-overview/triggers/notification-triggers/dataset-version-trigger.md) *** ### Pattern: Deploy Approved Models **Goal:** When a model version is approved, automatically deploy it **Setup:** 1. Create notification trigger that runs deployment pipeline 2. Configure trigger to receive model version as input 3. Set notification routing: "model latest approved version changes" → trigger channel 4. Add payload filter for specific model **Result:** Approve model → deployment starts automatically → model live in production [**Full model version trigger guide →**](/automation-overview/triggers/notification-triggers/model-version-trigger.md) *** ### Pattern: Chain Pipeline Stages **Goal:** When training completes, automatically run evaluation **Setup:** 1. Training pipeline outputs model files 2. Create notification trigger that runs evaluation pipeline 3. Configure evaluation to receive model as input 4. Set notification routing: "pipeline completes" → trigger channel 5. Add payload filter to only trigger for training pipeline **Result:** Training finishes → evaluation launches → results available automatically *** ### Pattern: Failure Recovery **Goal:** When execution fails, notify team and launch diagnostic job **Setup:** 1. Create notification trigger that runs diagnostic execution 2. Configure trigger to receive failure details 3. Set notification routing: "execution fails" → trigger channel 4. Also set up outgoing webhook to Slack for alerts **Result:** Execution fails → team notified → diagnostics run automatically → faster debugging *** ## Payload Filtering Notification triggers can filter events to only launch for specific cases. > :bulb:Take a look at [Notification Payload Formats ](/automation-overview/triggers/notification-triggers/notification-payload-formats.md)for each notification type. ### Filter by Model or Dataset Only trigger for a specific resource using payload filters: **Condition type:** Payload Filter * **Lookup Path:** `data.model.slug` (for models) or `data.dataset.name` (for datasets) * **Operation:** Equals * **Invariant:** `your-model-name` or `your-dataset-name` This prevents the trigger from firing for every model/dataset in your project. ### Filter by User In notification routing settings, choose: * **All users:** Trigger for events from any team member * **Specific users:** Trigger only for selected users Useful for personal workflows without spamming the team. *** ## Passing Data to Triggered Workflows ### The Notification Payload When a notification event occurs, Valohai generates a JSON payload with event details: ```json { "type": "dataset_version_created", "data": { "version": { "uri": "datum://dataset-name/version-id", "ctime": "2024-01-15T10:30:00Z" } } } ``` This payload becomes an input file in your triggered execution or pipeline. ### Parsing the Payload Your code extracts data from the payload: ```python import json import valohai # Read the notification payload with open(valohai.inputs("payload").path()) as f: payload = json.load(f) # Extract the dataset version URI dataset_uri = payload["data"]["version"]["uri"] # Use it as needed print(f"Processing dataset: {dataset_uri}") ``` ### Passing Data Through Pipelines Use pipeline edges to pass extracted data to downstream nodes: ```yaml - pipeline: name: Dataset Handler nodes: - name: parse-notification step: parse-notification - name: process-data step: process-data edges: - [parse-notification.metadata.dataset, process-data.parameter.dataset_url] ``` The parsing node extracts the URI and outputs it as metadata. The edge connects it to the processing node's parameter. *** ## Setting Up a Notification Trigger ### Step 1: Create the Trigger 1. Go to Project Settings → Triggers 2. Click **Create Trigger** 3. **Trigger Type:** Select "Notification" 4. **Title:** Give it a descriptive name 5. **Conditions:** Add payload filters if needed 6. **Actions:** Choose what to launch * **Run Execution:** Launch a single step * **Run Pipeline:** Launch a multi-step workflow 7. **Payload Input Name:** Specify where to send the notification data * For execution: `input-name` * For pipeline: `node-name.input-name` 8. Save the trigger A managed trigger channel is automatically created. ### Step 2: Set Up Notification Routing 1. Go to Project Settings → Notifications → Project Notifications 2. Click **Create new notification routing** 3. **Event:** Select the event type 4. **Filter by users:** Choose "All users" or specific users 5. **Channel:** Select `Launches trigger: ` 6. Save the routing Now when the event occurs, your trigger launches automatically! *** ## Testing Your Trigger ### Manual Testing Before relying on real events, test your trigger: 1. **Create test payload:** Make a JSON file matching the expected structure 2. **Create execution manually:** Select your parsing step and upload the test payload 3. **Verify parsing:** Check that your code extracts data correctly 4. **Test full pipeline:** Run the pipeline manually with test inputs ### Trigger a Real Event Once manual testing passes: 1. **For dataset triggers:** Upload a new dataset version 2. **For model triggers:** Approve a model version 3. **For execution triggers:** Run a test execution Check the execution list to verify the trigger launched automatically. ### Debug Failed Triggers If triggers don't launch: 1. **Check trigger logs:** Project Settings → Triggers → ... menu → View Logs 2. **Verify routing:** Confirm event type matches your routing configuration 3. **Check payload filters:** Ensure filters aren't too restrictive 4. **Validate inputs:** Make sure step declares the payload input in YAML *** ## Notification Triggers vs. Webhook Triggers Both respond to events, but from different sources: | Feature | Notification Triggers | Webhook Triggers | | ------------------ | ----------------------- | ------------------------ | | **Event source** | Inside Valohai | External services | | **Setup** | Notification routing | Webhook URL | | **Authentication** | Not needed | Required | | **Use cases** | React to Valohai events | Integrate external tools | {% hint style="info" %} Use notification triggers for Valohai-internal workflows. Use webhook triggers when external services need to start work in Valohai. {% endhint %} *** ## Next Steps * **Process datasets automatically:** [Dataset Version Trigger Guide](/automation-overview/triggers/notification-triggers/dataset-version-trigger.md) * **Auto-deploy models:** [Model Version Trigger Guide](/automation-overview/triggers/notification-triggers/model-version-trigger.md) * **Need scheduled automation?** [Scheduled Triggers](/automation-overview/triggers/scheduled-triggers.md) * **Integrate external services:** [Webhook Triggers](/automation-overview/triggers/webhooks.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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