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Launch Driverless AI

A callable workflow that creates a Driverless AI engine through AI Engine Manager, then blocks until it reaches STATE_RUNNING. Authenticates with the auto-injected H2O_CLOUD_CLIENT_PLATFORM_TOKEN — you don't pass credentials, the runner already has them.

Every input except engine_id is optional: leave them empty and the engine inherits the values from the selected profile.

Workflow​

id: launch-dai
name: Launch Driverless AI Instance

trigger:
  callable: true

inputs:
  engine_id:
    type: string
    required: true
    description: "Engine ID — used to connect to the engine from subsequent jobs"

  display_name:
    type: string
    required: false
    default: ""
    description: "Human-readable engine name"

  cpu:
    type: string
    required: false
    default: ""
    description: "CPU units (default from profile)"

  gpu:
    type: string
    required: false
    default: ""
    description: "GPU units (default from profile)"

  memory:
    type: string
    required: false
    default: ""
    description: "Memory allocation, e.g. 32Gi (default from profile)"

  storage:
    type: string
    required: false
    default: ""
    description: "Storage allocation, e.g. 64Gi (default from profile)"

  max_idle_duration:
    type: string
    required: false
    default: ""
    description: "Auto-pause after idle, e.g. 30m, 2h (default from profile)"

  max_running_duration:
    type: string
    required: false
    default: ""
    description: "Max running time, e.g. 12h, 2d (default from profile)"

  version:
    type: string
    required: false
    default: ""
    description: "DAI version resource name, e.g. workspaces/global/daiEngineVersions/1.11.23 (default: latest)"

  profile:
    type: string
    required: false
    default: ""
    description: "DAI profile resource name, e.g. workspaces/global/daiEngineProfiles/default (default: first available)"

jobs:
  launch-dai:
    name: Launch DAI instance
    timeout: "30m"

    steps:
      - name: Install AIEM client
        run: sudo uv pip install --system h2o-engine-manager

      - name: Launch DAI engine
        env:
          ENGINE_ID: ${{ .inputs.engine_id }}
          DISPLAY_NAME: ${{ .inputs.display_name }}
          CPU: ${{ .inputs.cpu }}
          GPU: ${{ .inputs.gpu }}
          MEMORY: ${{ .inputs.memory }}
          STORAGE: ${{ .inputs.storage }}
          MAX_IDLE: ${{ .inputs.max_idle_duration }}
          MAX_RUNNING: ${{ .inputs.max_running_duration }}
          VERSION: ${{ .inputs.version }}
          PROFILE: ${{ .inputs.profile }}
        run: |
          python3 -c "
          import os
          import h2o_engine_manager

          clients = h2o_engine_manager.login(
              platform_token=os.environ['H2O_CLOUD_CLIENT_PLATFORM_TOKEN'],
          )

          kwargs = {'engine_id': os.environ['ENGINE_ID']}
          if os.environ.get('DISPLAY_NAME'):
              kwargs['display_name'] = os.environ['DISPLAY_NAME']
          if os.environ.get('CPU'):
              kwargs['cpu'] = int(os.environ['CPU'])
          if os.environ.get('GPU'):
              kwargs['gpu'] = int(os.environ['GPU'])
          if os.environ.get('MEMORY'):
              kwargs['memory_bytes'] = os.environ['MEMORY']
          if os.environ.get('STORAGE'):
              kwargs['storage_bytes'] = os.environ['STORAGE']
          if os.environ.get('MAX_IDLE'):
              kwargs['max_idle_duration'] = os.environ['MAX_IDLE']
          if os.environ.get('MAX_RUNNING'):
              kwargs['max_running_duration'] = os.environ['MAX_RUNNING']
          if os.environ.get('VERSION'):
              kwargs['dai_engine_version'] = os.environ['VERSION']
          if os.environ.get('PROFILE'):
              kwargs['profile'] = os.environ['PROFILE']

          engine = clients.dai_engine_client.create_engine(**kwargs)
          print(f'Engine created: {engine.engine_id}')
          print(f'State: {engine.state.value}')

          print('Waiting for engine to become ready...')
          engine.wait()

          print(f'Engine is ready: {engine.state.value}')
          print(f'Engine URL: {engine.api_url}')
          "

Calling it from your workflow​

jobs:
  start-dai:
    workflow:
      name: launch-dai
      inputs:
        engine_id: "my-dai-engine"

Add display_name, cpu, gpu, memory, etc. as needed to override profile defaults.

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