Well i am not sure and i haven't tried it but i checked the source of the hub.load()
and i found some interesting facts may be they help you for your problem
First of all the doc says
This function is roughly equivalent to the TF2 function
tf.saved_model.load()
on the result of hub.resolve(handle)
.
Calling this function requires TF 1.14 or newer. It can be called
both in eager and graph mode.
that means the function can handle both URL or saved model in a file system, to confirm that i checked the documentation of hub.resolve()
which is being used internally in hub.load()
and there i found some thing of your interest
def resolve(handle):
"""Resolves a module handle into a path.
This function works both for plain TF2 SavedModels and the legacy TF1 Hub
format.
Resolves a module handle into a path by downloading and caching in
location specified by TF_HUB_CACHE_DIR if needed.
Currently, three types of module handles are supported:
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.
Args:
handle: (string) the Module handle to resolve.
Returns:
A string representing the Module path.
"""
return registry.resolver(handle)
The documentation clearly says it supports the path to the local file system which points to the module/model files, you should now perform some experiments and give it a try. For more details have a look on this source file