CODEWITHSUNDEEP
pythontensorflowkerasazure-machine-learning-servicemlflow
webber
webber

Reputation: 1886

Unknown model type error when logging Keras model using MLflow in Azure ML

I'm getting a “Unknown model type error” when training a model on Azure ML. It's a Keras TensorFlow model and here is my code that's failing:

mlflow.keras.log_model(
    model=model,
    registered_model_name=registered_model_name,
    artifact_path=registered_model_name,
    extra_pip_requirements=["protobuf~=3.20"],
)
mlflow.keras.save_model(
    keras_model=model,
    path=os.path.join(registered_model_name, "trained_model"),
    extra_pip_requirements=["protobuf~=3.20"],
)

Error:

Epoch 20/20 - 3s - loss: 0.0047 - accuracy: 0.9988 - val_loss: 0.1677 - val_accuracy: 0.9809 2023/08/12 14:33:23 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs. Test loss: 0.1676583289883102 Test accuracy: 0.98089998960495 Registering the model via MLFlow -- 1 Traceback (most recent call last): File "keras_mnist.py", line 176, in mlflow.keras.log_model( File "/azureml-envs/azureml_e6c91049350b2ff55519ca4d0d2aa0dc/lib/python3.8/site-packages/mlflow/tensorflow/init.py", line 208, in log_model return Model.log( File "/azureml-envs/azureml_e6c91049350b2ff55519ca4d0d2aa0dc/lib/python3.8/site-packages/mlflow/models/model.py", line 572, in log flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs) File "/azureml-envs/azureml_e6c91049350b2ff55519ca4d0d2aa0dc/lib/python3.8/site-packages/mlflow/tensorflow/init.py", line 451, in save_model raise MlflowException(f"Unknown model type: {type(model)}") mlflow.exceptions.MlflowException: Unknown model type: <class 'keras.engine.sequential.Sequential'>

Conda Environment:

name: keras-env channels:
  - conda-forge dependencies:
  - python=3.8
  - pip=21.2.4
  - pip:
    - protobuf~=3.20
    - numpy==1.21.2
    - tensorflow-gpu==2.2.0
    - keras==2.3.1
    - matplotlib
    - mlflow==2.5.0
    - azureml-mlflow==1.52.0

Upvotes: 0

Views: 577

Answers (1)

webber
webber

Reputation: 1886

This turned out to be a dependencies issue. Here are the versions that worked for me.

name: keras-env
channels:
  - conda-forge
dependencies:
  - python=3.8
  - pip=21.2.4
  - pip:
    - protobuf==3.19.6
    - numpy==1.24.4
    - tensorflow-gpu==2.11.0
    - keras==2.11.0
    - matplotlib==3.7.2
    - azureml-mlflow==1.52.0
    - mlflow==2.5.0

Upvotes: 0

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