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Reusable workflows

Reusable workflows allow you to call one workflow from another, enabling modular workflow design and reducing duplication.

Schema​

See Schema Reference for #Trigger (with callable field), #WorkflowCall, and #Job definitions.

Making Workflows Callable​

Mark a workflow as callable using trigger.callable: true:

id: deploy-model
name: Deploy Model

trigger:
  callable: true

inputs:
  model_path:
    type: string
    required: true
  deploy_token:
    type: string
    required: true
    secret: true  # Masked in logs and UI

jobs:
  deploy:
    steps:
      - name: Deploy
        env:
          TOKEN: ${{ .inputs.deploy_token }}
        run: h2o deploy ${{ .inputs.model_path }} --token $TOKEN

Workflow ID and Resource Names​

Each workflow has two identifiers:

  • Workflow ID (id field): User-defined unique identifier that forms part of the resource name.

    • Used when calling workflows via WorkflowCall.
    • Required field in workflow definition.

  • Workflow Name (name field): Optional display name.

    • Not unique — multiple workflows can have the same display name.
    • Not used for WorkflowCall references.

Resource Name Format​

The full resource name combines the workspace ID and workflow ID:

/workspaces/{workspace-id}/workflows/{workflow-id}

Example: /workspaces/abc123/workflows/deploy-model

Reference Formats in WorkflowCall​

When calling workflows, you can use two formats:

Short ID (same workspace):

workflow:
  name: deploy-model  # Expands to /workspaces/{current-workspace}/workflows/deploy-model

Full Resource Name (cross-workspace calls):

workflow:
  name: /workspaces/other-workspace-id/workflows/deploy-model

Calling Workflows​

Call a workflow from a job using the workflow field:

id: train-and-deploy
name: Train and Deploy

secrets:
  - name: workspaces/xxx/secrets/deploy-token
    as: deploy_token

jobs:
  train:
    steps:
      - name: Train model
        run: python train.py
      - name: Upload model
        upload:
          path: ./model/
          destination: drive://models/latest/

  deploy:
    depends_on: [train]
    workflow:
      name: deploy-model  # Short ID - references workflow in same workspace
      inputs:
        model_path: "drive://models/latest/"
        deploy_token: ${{ .secrets.deploy_token }}

Cross-workspace example:

  deploy-to-prod:
    workflow:
      name: /workspaces/prod-workspace-123/workflows/deploy-service  # Full resource name
      inputs:
        version: "v1.2.3"

Input Passing​

Regular Inputs​

Pass inputs using the workflow.inputs map. Supports expressions:

jobs:
  process:
    workflow:
      name: workflow-process-data
      inputs:
        dataset: ${{ .inputs.dataset_name }}
        preprocessing: "standard"

Secret Inputs​

Mark inputs as secret: true to mask values in logs and UI:

# Callable workflow
inputs:
  api_key:
    type: string
    secret: true  # Value will be masked

# Caller
secrets:
  - name: workspaces/xxx/secrets/api-key
    as: api_key

jobs:
  call:
    workflow:
      name: workflow-api-integration
      inputs:
        api_key: ${{ .secrets.api_key }}

Behavior​

Concurrency Control​

Each workflow uses its own concurrency settings independently. Called workflows do not inherit parent concurrency groups.

Failure Propagation​

When a called workflow fails, the parent job fails, triggering standard failure handling (cancel_on_failure, depends_on).

Example: Parallel Processing​

Callable workflow:

id: process-dataset

trigger:
  callable: true

inputs:
  dataset_name:
    type: string
    required: true

jobs:
  process:
    steps:
      - name: Download
        download:
          source: drive://datasets/raw/${{ .inputs.dataset_name }}/
          path: ./data/
      - name: Process
        run: python process.py --input ./data/
      - name: Upload
        upload:
          path: ./data/processed/
          destination: drive://datasets/processed/${{ .inputs.dataset_name }}/

Caller (parallel processing):

id: multi-dataset-processing

cancel_on_failure: false

jobs:
  process-users:
    workflow:
      name: workflow-process-dataset
      inputs:
        dataset_name: "users"

  process-transactions:
    workflow:
      name: workflow-process-dataset
      inputs:
        dataset_name: "transactions"

  train:
    depends_on: [process-users, process-transactions]
    steps:
      - name: Train model
        run: python train.py --data drive://datasets/processed/
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