How to load TF hub model from local system

Question

One way is to download the model each time from tensorflow_hub like following

import tensorflow as tf
import tensorflow_hub as hub

hub_url = "https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1"
embed = hub.KerasLayer(hub_url)
embeddings = embed(["A long sentence.", "single-word", "http://example.com"])
print(embeddings.shape, embeddings.dtype)

I want to download the file once and use again and again with out downloading each time

Answer 1

You can use the hub.load() method to load a TF Hub module. Also, the docs say,

Currently this method is fully supported only with TensorFlow 2.x and with modules created by calling tensorflow.saved_model.save(). The method works in both eager and graph modes.

The hub.load method has an argument handle. The types of modules handles are,

1. Smart URL resolvers such as tfhub.dev, e.g.: https://tfhub.dev/google/nnlm-en-dim128/1.

2. A directory on a file system supported by Tensorflow containing module files. This may include a local directory (e.g. /usr/local/mymodule) or a Google Cloud Storage bucket (gs://mymodule).

3. A URL pointing to a TGZ archive of a module, e.g. https://example.com/mymodule.tar.gz.

You can use the 2nd and the 3rd points.