tensorflow/models · error · ValueError
Export module not implemented for {} task.
Error message
Export module not implemented for {} task. What it means
Error "Export module not implemented for {} task." thrown in tensorflow/models.
Source
Thrown at official/vision/serving/export_tfhub_lib.py:56
model. Default is False.
Returns:
A tf_keras.Model instance.
Raises:
ValueError: If the task is not supported.
"""
input_specs = tf_keras.layers.InputSpec(shape=[batch_size] +
input_image_size + [num_channels])
if isinstance(params.task,
configs.image_classification.ImageClassificationTask):
model = factory.build_classification_model(
input_specs=input_specs,
model_config=params.task.model,
l2_regularizer=None,
skip_logits_layer=skip_logits_layer)
else:
raise ValueError('Export module not implemented for {} task.'.format(
type(params.task)))
return model
def export_model_to_tfhub(batch_size: Optional[int],
input_image_size: List[int],
params: cfg.ExperimentConfig,
checkpoint_path: str,
export_path: str,
num_channels: int = 3,
skip_logits_layer: bool = False):
"""Export a TF2 model to TF-Hub."""
model = build_model(batch_size, input_image_size, params, num_channels,
skip_logits_layer)
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(checkpoint_path).assert_existing_objects_matched()
model.save(export_path, include_optimizer=False, save_format='tf')
View on GitHub (pinned to e006f5f0d5)
Solutions
- Use a supported task for TF-Hub export (e.g. 'classification', 'detection', 'segmentation').
- Register an export module for your custom task.
When it happens
Trigger: Thrown at official/vision/serving/export_tfhub_lib.py:56 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/bf4e23b9ef00ce13.
Report an issue: GitHub.