Hello!

I'm having an issue registering a model saved in a mounted S3 bucket using mlflow.

Let me give a little bit more context:

1. First I mounted my S3 with all the corresponding IAM permissions:

s3_bucket_name = f"s3a://{s3_bucket}"
dbutils.fs.mount(source=s3_bucket_name, mount_point=f"/mnt/{s3_bucket}")

2. Then I created an experiment pointing to that artifact path

artifact_location=f"dbfs:/mnt/{s3_bucket}/experiments/{model_name}"
mlflow.create_experiment(experiment_name,artifact_location=artifact_location)

3. I started my run and logged my model without registering them. I am logging my model using mlflow and FeatureStoreClient(). (I find the exact same problem with any of the methods of logging the model). 

mlflow.start_run(run_name=run_name)

mlflow.pyfunc.log_model("model_mlflow", python_model=model)

fs = FeatureStoreClient()
fs.log_model(model=model,
artifact_path="model_feature_store",
flavor=flavor,
training_set=training_set)

mlflow.end_run()

To this point, everything is working fine. 

I can find the models in DBFS in:

• dbfs:/mnt/my-s3-bucket/my-experiment/run_id/artifacts/model_feature_store. 
• dbfs:/mnt/my-s3-bucket/my-experiment/run_id/artifacts/model_mlflow

I can find the models in my S3 bucket too.

I can access my mlflow model with load_model without any problem from my notebook

model_mlflow = mlflow.pyfunc.load_model(model_uri=f"runs:/{active_run_id}/model_mlflow")
model_mlflow 

I can do fs.score_batch() with the Feature Store Client model as well without problem:
fs.score_batch(model_uri=f"runs:/{active_run_id}/model_feature_store", df = df)

The problem comes at the final step when trying to register my model:
If I try to either:

• Input the parameter of registered_model_name in any of the log_model() functions,
• Or, Try to register any model with:

  • mlflow.register_model(model_uri=f"runs:/{active_run_id}/model_mlflow",name=model_name) 

• Or, Try to register my model from the UI with the Register Model button

I get the exact same error:

MlflowException: Model version creation failed for model name: model version: 4 with status: FAILED_REGISTRATION and message:
Failed registration. The given source path `dbfs:/mnt/<my-s3-bucket>/<my-experiment-name>/<run_id>/artifacts/model_mlflow` does not exist.

If I follow the exact same steps but save the experiment in a DBFS path outside the mounted S3 bucket I have no problem at all with the registration step. It only happens when I save the experiment in the S3-mounted bucket inside the S3. 

Why can't I register a model in Databricks when it is saved in dbfs:/mnt/S3-bucket?

Thank you in advance!!