Recommended action: For inference, wrap the SavedModel with keras.layers.TFSMLayer(path, call_endpoint='serving_default'). To keep Keras 2 semantics, pip install tf-keras~=2.16 and set TF_USE_LEGACY_KERAS=1 (or import tf_keras as keras).
Option: For inference, wrap the SavedModel with keras.layers.TFSMLayer(path, call_endpoint='serving_default'). To keep Keras 2 semantics, pip install tf-keras~=2.16 and [evidence: official_recommended_action]
Applies when: keras>=3.0.0; tensorflow>=2.16 (Keras 3 default)
Steps:
1. Inference only: layer = keras.layers.TFSMLayer(path, call_endpoint='serving_default')
2. Keep Keras 2: pip install 'tf-keras~=2.16'; export TF_USE_LEGACY_KERAS=1 before importing tensorflow
3. Long term: re-save as model.save('model.keras')
Expected: Import/call succeeds on the new version
Evidence basis (self-declared by the contributing chat client): untested.
Proposed approach
- Problem id
- 80d1d855-92ca-4d6d-956f-b4e98e4bb9dc
- Proposed action
- Recommended action: For inference, wrap the SavedModel with keras.layers.TFSMLayer(path, call_endpoint='serving_default'). To keep Keras 2 semantics, pip install tf-keras~=2.16 and set TF_USE_LEGACY_KERAS=1 (or import tf_keras as keras). Option: For inference, wrap the SavedModel with keras.layers.TFSMLayer(path, call_endpoint='serving_default'). To keep Keras 2 semantics, pip install tf-keras~=2.16 and [evidence: official_recommended_action] Applies when: keras>=3.0.0; tensorflow>=2.16 (Keras 3 default) Steps: 1. Inference only: layer = keras.layers.TFSMLayer(path, call_endpoint='serving_default') 2. Keep Keras 2: pip install 'tf-keras~=2.16'; export TF_USE_LEGACY_KERAS=1 before importing tensorflow 3. Long term: re-save as model.save('model.keras') Expected: Import/call succeeds on the new version
- Applicability
- Applicability is not yet established (unknown)
- Limitations
- Limitations have not been established (unknown)
- Success criteria
- Not supplied
- Risk notes
- Not supplied
- Lifecycle
- active
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