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name: export-and-save description: "Create TensorFlow Hub-consumable TF2 SavedModels and export text embedding SavedModels." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Export and Save

Use this sub-skill when the task is to create a TensorFlow Hub-consumable TF2 SavedModel, prepare a local model for later use with tensorflow_hub.load or tensorflow_hub.KerasLayer, or export a text embedding model from token/vector text files.

Do not use this sub-skill as a current recipe for legacy TF1 TensorFlow Hub Module publishing. In this package version, the current top-level public API is tensorflow_hub.load, tensorflow_hub.resolve, and tensorflow_hub.KerasLayer; legacy top-level publishers such as Module, create_module_spec, load_module_spec, add_signature, and attach_message are absent.

Quick routing

• For general TF2 tf.Module or Keras SavedModel export patterns, read references/savedmodel-export.md.
• For token/vector text embedding export, read references/text-embedding-export.md and use scripts/export_text_embeddings_v2.py.
• For old TF1 TensorFlow Hub Module exporter snippets, read references/legacy-tf1-notes.md before deciding whether to stop or request an archived runtime.
• For export, reload, signature, asset, OOV, and path failures, read references/troubleshooting.md.

Quick workflow

1. Decide the export shape: a callable tf.Module/Keras object, an explicit signature, or the bundled text embedding workflow.
2. Save a plain TF2 SavedModel with tf.saved_model.save(...). Give callable models an @tf.function __call__ with an input_signature; give signature-only models explicit named signatures.
3. Validate from the public TensorFlow Hub side with tensorflow_hub.load(export_path). For Keras integration, validate with tensorflow_hub.KerasLayer(...) using signature and output_key when the SavedModel exposes only a dict signature.
4. If validation needs deeper loader, cache, or KerasLayer diagnosis, route through the sibling load-and-wrap sub-skill via the TensorFlow Hub root router.

Minimal bundled text embedding command

python scripts/export_text_embeddings_v2.py \
  --embedding-file embeddings.txt \
  --export-path exported_text_embedding \
  --verify \
  --sample-text "cat dog" --sample-text "unknown-token" ""

The embedding file must contain one whitespace-separated token and numeric vector per line. The exporter writes a TF2 SavedModel, not a legacy TF1 TensorFlow Hub Module.