Error: batch scoring with mlflow.keras flavor model

Miki
New Contributor II

‎03-15-2024 06:51 AM - edited ‎03-15-2024 07:10 AM

I am logging a trained keras model using the following: 

 fe.log_model(
 model=model,
 artifact_path="wine_quality_prediction",
 flavor= mlflow.keras,
 training_set=training_set,
 registered_model_name=model_name
 )

And when I call the following:

predictions_df = fe.score_batch(model_uri=f"models:/{model_name}/{latest_model_version}", df=batch_input_df)
display(predictions_df)

I get the following error:

OSError: [Errno 30] Read-only file system: '/local_disk0/.ephemeral_nfs/repl_tmp_data/ReplId-62b1a-a1cdf-baa5b-1/mlflow/models/tmpajr94lkz/raw_model/data/model.keras

I get the same 

I am essentially just trying to adapt this example to use a keras model instead of a Random Forest. The code runs fine with a Random Forest, and it runs fine if I just use the native mlflow log_model(), load_model() and predict() functions. However, I do get the error if I just use mlflow.pyfunc.load_model() to load the model and call model.predict(). This makes me think the bug is specific to the way in which the databricks FeatureEngineeringClient module is saving the keras model. 

I would appreciate any help with this issue.

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