Launch H2O-3

A composite action that creates an H2O-3 cluster through AI Engine Manager, blocks until it is ready, and returns the cluster's API URL as an output — later steps in the calling job can connect to it directly. Authentication is handled via the auto-injected H2O_CLOUD_CLIENT_PLATFORM_TOKEN so there is no need to pass credentials (the runner already has them).

Because the action runs inline, it also leaves the h2o-engine-manager client installed in the job, so later steps can import it to talk to the cluster.

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

Action

id: launch-h2o3
name: Launch H2O-3 Instance

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

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

  node_count:
    type: string
    required: false
    default: ""
    description: "Cluster node count (default from profile)"

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

  memory:
    type: string
    required: false
    default: ""
    description: "Memory per node, e.g. 8Gi (default from profile)"

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

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

  version:
    type: string
    required: false
    default: ""
    description: "H2O-3 version resource name (default: latest)"

  profile:
    type: string
    required: false
    default: ""
    description: "H2O-3 profile resource name (default: first available)"

outputs:
  engine_url:
    description: "API URL of the ready cluster"
    value: ${{ .steps.launch.outputs.engine_url }}

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

  - id: launch
    name: Launch H2O-3 engine
    timeout: "30m"
    env:
      ENGINE_ID: ${{ .inputs.engine_id }}
      DISPLAY_NAME: ${{ .inputs.display_name }}
      NODE_COUNT: ${{ .inputs.node_count }}
      CPU: ${{ .inputs.cpu }}
      MEMORY: ${{ .inputs.memory }}
      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('NODE_COUNT'):
          kwargs['node_count'] = int(os.environ['NODE_COUNT'])
      if os.environ.get('CPU'):
          kwargs['cpu'] = int(os.environ['CPU'])
      if os.environ.get('MEMORY'):
          kwargs['memory_bytes'] = os.environ['MEMORY']
      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['h2o_engine_version'] = os.environ['VERSION']
      if os.environ.get('PROFILE'):
          kwargs['profile'] = os.environ['PROFILE']

      engine = clients.h2o_engine_client.create_engine(**kwargs)
      print(f'Engine created: {engine.name}')

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

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

      with open(os.environ['H2O_WORKFLOWS_OUTPUT'], 'a') as f:
          f.write(f'engine_url={engine.api_url}\n')
      "

Publish the action to make it callable from workflows.

Use it from your workflow

Give the uses step an id to read the cluster URL in later steps of the same job:

id: train-with-h2o3
name: Train with H2O-3

jobs:
  train:
    timeout: "1h"  # must cover the cluster launch wait (up to 30m)
    steps:
      - uses: <workspace-id>:launch-h2o3@latest
        id: h2o3
        with:
          engine_id: "my-h2o3-cluster"

      - name: Use the cluster
        env:
          H2O3_URL: ${{ .steps.h2o3.outputs.engine_url }}
        run: echo "H2O-3 is ready at $H2O3_URL"