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License: Apache License 2.0
Multi-dimensional/Multi-input/Multi-output Data preprocessing and Batch Generators for Keras models
License: Apache License 2.0
Since keras.Model.predict
method has proved memory leaks, a workaround is needed and periodically clear the Keras backend state by calling K.clear_session()
.
Please add this functionality to ModelObject in the next way:
class ModelObject(ABC):
def __init__(
self,
mp: ModelParams,
model: Model,
encoders: Dict[str, List[DataEncoder]],
state_autoclear: int = 128,
) -> None:
"""
:param mp: the model parameters
:param model: the keras model
:param encoders: the encoders used to encode/decode the data
:param state_autoclear: the number of calls to predict/evaluate methods after which the keras session is cleared
"""
self.mp = mp
self.model = model
self.encoders = encoders
self._state_autoclear = state_autoclear
self._state_calls = 0
def _check_clear_state(self):
self._state_calls += 1 # since it's an atomic operation, it's thread safe due to GIL
if self._state_calls >= self._state_autoclear:
K.clear_session()
logging.debug(f"Cleared keras session after {self._state_calls} calls to predict methods")
self._state_calls = 0
and add _check_clear_state to predict()
and evaluate()
methods
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