apache/beam · error · ValueError

Missing parameters in model_handler: {missing_params}

Error message

Missing parameters in model_handler: {missing_params}

What it means

The YAML run_inference wrapper found the model_handler spec missing required keys (only 'type' and 'config' are expected); the missing key names are interpolated so the user knows exactly what to add to the YAML.

Source

Thrown at sdks/python/apache_beam/yaml/yaml_ml.py:561

      see which args are allowed.

  """

  options.YamlOptions.check_enabled(pcoll.pipeline, 'ML')

  if not isinstance(model_handler, dict):
    raise ValueError(
        'Invalid model_handler specification. Expected dict but was '
        f'{type(model_handler)}.')
  expected_model_handler_params = {'type', 'config'}
  given_model_handler_params = set(
      SafeLineLoader.strip_metadata(model_handler).keys())
  extra_params = given_model_handler_params - expected_model_handler_params
  if extra_params:
    raise ValueError(f'Unexpected parameters in model_handler: {extra_params}')
  missing_params = expected_model_handler_params - given_model_handler_params
  if missing_params:
    raise ValueError(f'Missing parameters in model_handler: {missing_params}')
  typ = model_handler['type']
  model_handler_provider_type = ModelHandlerProvider.handler_types.get(
      typ, None)
  if not model_handler_provider_type:
    raise NotImplementedError(f'Unknown model handler type: {typ}.')

  model_handler_provider = ModelHandlerProvider.create_handler(model_handler)
  model_handler_provider.validate(model_handler['config'])
  schema = RowTypeConstraint.from_fields(
      named_fields_from_element_type(pcoll.element_type) +
      [(str(inference_tag), model_handler_provider.inference_output_type())])

  return (
      pcoll | RunInference(
          model_handler=KeyedModelHandler(
              model_handler_provider.underlying_handler()).with_preprocess_fn(
                  model_handler_provider._preprocess_fn_internal()).
          with_postprocess_fn(

View on GitHub (pinned to 12126d8942)

Solutions

1. Add the missing 'type' key naming the handler
2. Add the missing 'config' key (use an empty dict {} if all defaults suffice)
3. Verify YAML indentation nests both keys under model_handler

Example fix

# before
model_handler:
  type: VertexAI
# after
model_handler:
  type: VertexAI
  config:
    endpoint_id: '123'
    project: 'my-project'

def check_handler_required(handler):
    missing = {'type', 'config'} - set(handler)
    if missing:
        raise ValueError(f'model_handler missing: {missing}')

def is_complete_handler_spec(h):
    return isinstance(h, dict) and {'type', 'config'} <= set(h)

When it happens

Trigger: model_handler dict given with only 'type' but no 'config', or only 'config' but no 'type', e.g. {'type': 'VertexAI'} without config.

Common situations: Omitting the config block when all defaults apply; forgetting the type key when only options are supplied; YAML nodes dropped due to bad indentation; programmatic dict built conditionally.

Understand the failure class

Background: "is required", "must be set", "missing required field": configuration validation errors across open-source libraries — this error's family across 36 libraries.

Related errors

• Unexpected parameters in model_handler: {extra_params}
• Missing type in ML transform spec {spec}
• Missing config in ML transform spec {spec}
• Node ID cannot be empty
• Edge source and target cannot be empty

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/9cba5566649a5686. Report an issue: GitHub.