Accessing Databricks Volumes from a Serving Endpoint Using a Custom Model Class in Unity Catalog

[VELU1122]

Hi everyone,

I’m looking for accessing Unity Catalog (UC) Volumes from a Databricks Serving Endpoint. Here’s my current setup:

• I have a custom AI model class for inference, which I logged into Unity Catalog using mlflow.pyfunc.log_model.
• I’ve created a Serving Endpoint for this model.

Challenges:

1. When trying to access UC Volumes directly from my custom class during inference, I get a "No such file or directory" error.
2. I attempted to mount the UC Volumes within the custom class using dbutils.fs.mount, but when logging the model (mlflow.pyfunc.log_model), I encountered an error that dbutils can’t be used in the Spark environment.

Question:

Since the Serving Endpoint runs in an isolated environment, how can I access Unity Catalog Volumes from within my custom model class during inference?

Any guidance on solving this issue or alternative methods to access UC Volumes from a Serving Endpoint would be greatly appreciated.

Thanks in advance

[VELU1122]

Additionally, I log the model as shown below, with MicrosoftResnet50Model being my custom inference class with load_context and predict methods:
with mlflow.start_run():
model_info = mlflow.pyfunc.log_model(
REGISTERED_MODEL_NAME,
python_model=MicrosoftResnet50Model(),
input_example=api_input_example,
artifacts={"model_path": MODEL_PATH},
pip_requirements=[
f"transformers=={transformers.__version__}",
"torch==2.0.1"
],
signature=signature,
registered_model_name=f"{CATALOG}.{SCHEMA}.{REGISTERED_MODEL_NAME}"
)