CODEWITHSUNDEEP
tensorflowtensorflow2.0tensorflow-hubelmo
Alex Kinman
Alex Kinman

Reputation: 2605

error when try to import the Elmo module from TensorFlow Hub?

I'm not able to import the elmo module from TensorFlow Hub. I am able to import other modules and use them successfully. I'm running TF2.0 on a GCP Jupyterlab instance with GPUs. When I try this: 

import tensorflow as tf
import tensorflow_hub as hub

elmo = hub.Module("https://tfhub.dev/google/elmo/3", trainable=True)

I get: 

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-2-caced7ee1735> in <module>
----> 1 elmo = hub.Module("https://tfhub.dev/google/elmo/3", trainable=True)

/usr/local/lib/python3.5/dist-packages/tensorflow_hub/module.py in __init__(self, spec, trainable, name, tags)
    174           name=self._name,
    175           trainable=self._trainable,
--> 176           tags=self._tags)
    177       # pylint: enable=protected-access
    178 

/usr/local/lib/python3.5/dist-packages/tensorflow_hub/native_module.py in _create_impl(self, name, trainable, tags)
    384         trainable=trainable,
    385         checkpoint_path=self._checkpoint_variables_path,
--> 386         name=name)
    387 
    388   def _export(self, path, variables_saver):

/usr/local/lib/python3.5/dist-packages/tensorflow_hub/native_module.py in __init__(self, spec, meta_graph, trainable, checkpoint_path, name)
    443     # TPU training code.
    444     with scope_func():
--> 445       self._init_state(name)
    446 
    447   def _init_state(self, name):

/usr/local/lib/python3.5/dist-packages/tensorflow_hub/native_module.py in _init_state(self, name)
    446 
    447   def _init_state(self, name):
--> 448     variable_tensor_map, self._state_map = self._create_state_graph(name)
    449     self._variable_map = recover_partitioned_variable_map(
    450         get_node_map_from_tensor_map(variable_tensor_map))

/usr/local/lib/python3.5/dist-packages/tensorflow_hub/native_module.py in _create_state_graph(self, name)
    503         meta_graph,
    504         input_map={},
--> 505         import_scope=relative_scope_name)
    506 
    507     # Build a list from the variable name in the module definition to the actual

/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/training/saver.py in import_meta_graph(meta_graph_or_file, clear_devices, import_scope, **kwargs)
   1451   return _import_meta_graph_with_return_elements(meta_graph_or_file,
   1452                                                  clear_devices, import_scope,
-> 1453                                                  **kwargs)[0]
   1454 
   1455 

/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/training/saver.py in _import_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, import_scope, return_elements, **kwargs)
   1461   """Import MetaGraph, and return both a saver and returned elements."""
   1462   if context.executing_eagerly():
-> 1463     raise RuntimeError("Exporting/importing meta graphs is not supported when "
   1464                        "eager execution is enabled. No graph exists when eager "
   1465                        "execution is enabled.")

RuntimeError: Exporting/importing meta graphs is not supported when eager execution is enabled. No graph exists when eager execution is enabled.

Upvotes: 2

Views: 1527

Answers (2)

Abhi25t
Abhi25t

Reputation: 4633

handle = "https://tfhub.dev/google/elmo/3"

If you want to use TF 2 for loading/inferencing, these 2 methods are recommended: hub.load() is the new low-level function to load a SavedModel from TensorFlow Hub (or compatible services). It wraps TF2's tf.saved_model.load()

model = hub.load(handle)
outputs = model(inputs)

or

model = hub.KerasLayer(handle, signature="sig")
outputs = model(inputs)

The hub.KerasLayer class calls hub.load() and adapts the result for use in Keras alongside other Keras layers. (It may even be a convenient wrapper for loaded SavedModels used in other ways.)

model = tf.keras.Sequential([
    hub.KerasLayer(handle),
    ...])

But if you want to finetune: Load a legacy model in TF1 Hub format

import tensorflow.compat.v1 as tf
import tensorflow_hub as hub    
tf.disable_v2_behavior()

elmo = hub.Module(handle, trainable=True)

Source: https://www.tensorflow.org/hub/model_compatibility

Upvotes: 0

arnoegw
arnoegw

Reputation: 1238

The hub.Module API does not work in Eager mode. Please check out https://www.tensorflow.org/hub/migration_tf2, and take note of the change in file format from TF1 hub.Module to TF2 SavedModel.

Upvotes: 2

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