---

name: tensorflow-hub description: "Use TensorFlow Hub to resolve, load, wrap, and export Hub-compatible TensorFlow SavedModels." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TensorFlow Hub

Use this repo skill when a task involves the tensorflow-hub package, the tensorflow_hub import, TensorFlow Hub/Kaggle Models handles, tfhub.dev URLs, local Hub-compatible SavedModel directories, hub.load, hub.resolve, or hub.KerasLayer.

The current package surface verified for this skill exposes only these top-level APIs:

import tensorflow_hub as hub

hub.load
hub.resolve
hub.KerasLayer

Feature-column text embedding support is available through a submodule import, not as a top-level hub attribute:

import tensorflow_hub.feature_column_v2 as hub_feature_column_v2

Quick install and import check

Use a TensorFlow runtime that matches the Python environment. In Keras 3 environments, install the matching tf-keras compatibility package as well.

python -m pip install tensorflow-hub tensorflow tf-keras
python - <<'PY'
import tensorflow as tf
import tensorflow_hub as hub
print("tensorflow", tf.__version__)
print("tensorflow_hub", hub.__version__)
print("exports", hub.__all__)
PY

If the import check fails, read references/troubleshooting.md before changing package versions.

Route map

User task or symptom	Read next
Resolve or load a Hub handle, local SavedModel path, tfhub.dev/Kaggle Models URL, or archive	sub-skills/load-and-wrap/SKILL.md
Diagnose TFHUB_CACHE_DIR, TFHUB_MODEL_LOAD_FORMAT, download locks, corrupt archives, or certificate validation	sub-skills/load-and-wrap/references/load-resolve-cache.md
Wrap a Hub SavedModel in Keras, choose signature, output_key, signature_outputs_as_dict, output_shape, or trainable	sub-skills/load-and-wrap/references/keras-layer.md
Use text_embedding_column_v2, DenseFeatures, or recover from hub.text_embedding_column_v2 AttributeError	sub-skills/load-and-wrap/references/feature-column-v2.md
Create a TF2 SavedModel that can be consumed by TensorFlow Hub	sub-skills/export-and-save/SKILL.md
Export text embeddings from a token/vector file	sub-skills/export-and-save/references/text-embedding-export.md
Migrate or triage old TF1 hub.Module, create_module_spec, or add_signature snippets	sub-skills/export-and-save/references/legacy-tf1-notes.md
Check whether this skill matches the current source checkout	references/repo-provenance.md

Safe local validation

Before using a network-backed handle or a large model, run the no-download smoke script from the loading sub-skill:

python sub-skills/load-and-wrap/scripts/smoke_load_and_wrap.py --feature-column

For text embedding export tasks, use the bundled exporter under the export sub-skill:

python sub-skills/export-and-save/scripts/export_text_embeddings_v2.py --help

Both scripts create only temporary/local outputs chosen by the caller and do not read the original repository checkout.

Boundaries

Use this skill for:

• loading or resolving TensorFlow Hub handles and local SavedModels;
• Keras integration through hub.KerasLayer;
• feature-column text embedding workflows through tensorflow_hub.feature_column_v2;
• creating TF2 SavedModel exports that are meant to be reloaded by TensorFlow Hub;
• text embedding SavedModel export from small or production token/vector files;
• troubleshooting TensorFlow Hub import, cache, signature, and legacy API confusion.

Do not use this skill as the main route for:

• general TensorFlow model training unrelated to TensorFlow Hub handles or exports;
• deprecated TF1 image-retraining scripts;
• TensorFlow Hub repository maintenance, Bazel release builds, or pull-request policy;
• current code that assumes hub.Module, hub.create_module_spec, hub.add_signature, or top-level hub.text_embedding_column_v2 exist without first checking an older archived runtime.