MLFLOW on Databricks - Cannot log a Keras model as a mlflow.pyfunc model. Get TypeError: cannot pickle 'weakref' object - tensorflow

Question

Hi all: this is one of my first posts on Stackoverflow - so apologies in advance if i'm not conforming to certain standards!
I'm having trouble saving my Keras model as a mlflow.pyfunc model as it's giving me a "cannot pickle a 'weakref' object when I try to log it.
So why am i saving my Keras model as a pyfunc model object in the first place? This is because I want to override the default predict method and output something custom. I also want to do some pre-processing steps on the X_test or new data by encoding it with a tf.keras.StringLookup and then invert it back to get the original categorical variable class. For this reason, I was advised by Databricks that the mlflow.pyfunc flavor is the best way to go for these types of use-cases
The Keras model works just fine and i'm able to log it using mlflow.keras.log_model. But it fails when i try to wrap it inside a cutomer "KerasWrapper" class.
Here are some snippets of my code. For the purpose of debugging, the current predict method in the custom class is just the default. I simplified it to help debug, but obviously I haven't been able to resolve it.
I would be extremely grateful for any help. Thanks in advance!
ALL CODE ON AZURE DATABRICKS
Custom mlflow.pyfunc class
class KerasWrapper(mlflow.pyfunc.PythonModel):
def __init__(self, keras_model, labelEncoder, labelDecoder, n):
self.keras_model = keras_model
self.labelEncoder = labelEncoder
self.labelDecoder = labelDecoder
self.topn = n
def load_context(self, context):
self.keras_model = mlflow.keras.load_model(model_uri=context.artifacts[self.keras_model], compile=False)
def predict(self, context, input_data):
scores = self.keras_model.predict(input_data)
return scores
My Keras Deep Learning Model (this works fine by the way)
def build_model(vocab_size, steps, drop_embed, n_dim, encoder, modelType):
model = None
i = Input(shape=(None,), dtype="int64")
#embedding layer
e = Embedding(vocab_size, 16)(i)
s = SpatialDropout1D(drop_embed)(e)
x = Conv1D(256, steps, activation='relu')(s)
x = GlobalMaxPooling1D()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.2)(x)
#output layer
x = Dense(vocab_size, activation='softmax')(x)
model = Model(i, x)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
return model
MLFLOW Section
with mlflow.start_run(run_name=runName):
mlflow.tensorflow.autolog()
#Build the model, compile and train on the training set
#signature: build_model(vocab_size, steps, drop_embed, n_dim, encoder, modelType):
keras_model = build_model((vocab_size + 1), timeSteps, drop_embed, embedding_dimensions, encoder, modelType)
keras_model.fit(X_train_encoded, y_train_encoded, epochs=epochs, verbose=1, batch_size=32, use_multiprocessing = True,
validation_data=(X_test_encoded, y_test_encoded))
# Log the model parameters used for this run.
mlflow.log_param("numofActionsinWorkflow", numofActionsinWf)
mlflow.log_param("timeSteps", timeSteps)
#wrap it up in a pyfunc model
wrappedModel = KerasWrapper(keras_model, encoder, decoder, bestActionCount)
# Create a model signature using the tensor input to store in the MLflow model registry
signature = infer_signature(X_test_encoded, wrappedModel.predict(None, X_test_encoded))
# Let's check out how it looks
print(signature)
# Create an input example to store in the MLflow model registry
input_example = np.expand_dims(X_train[17], axis=0)
# The necessary dependencies are added to a conda.yaml file which is logged along with the model.
model_env = mlflow.pyfunc.get_default_conda_env()
# Record specific additional dependencies required by the serving model
model_env['dependencies'][-1]['pip'] += [
f'tensorflow=={tf.__version__}',
f'mlflow=={mlflow.__version__}',
f'sklearn=={sklearn.__version__}',
f'cloudpickle=={cloudpickle.__version__}',
]
#log the model to experiment
#mlflow.keras.log_model(keras_model, artifact_path = runName, signature=signature, input_example=input_example, conda_env = model_env)
wrapped_model_path = runName
if (os.path.exists(wrapped_model_path)):
shutil.rmtree(wrapped_model_path)
#Log model as pyfunc model
mlflow.pyfunc.log_model(runName, python_model=wrappedModel, signature=signature, input_example=input_example, conda_env = model_env)
#return the run ID for model registration
run_id = mlflow.active_run().info.run_id
mlflow.end_run()
Here is the error that i receive