I am training an XGBClassifier model using Snowflake ML and attempting to register it using Snowflake’s model registry. The training and evaluation steps complete successfully, but when I try to log the model using log_model(), I get the following error:

Training Accuracy: 0.9610
Evaluation Accuracy: 0.8730
Traceback (most recent call last):
  File "C:\Mas\rla_projects\Claims-AI--lodgement\code\python\src\explain\train_model.py", line 56, in <module>
    model_version = native_registry.log_model(
  File "C:\Mas\py39_sf_ml\lib\site-packages\snowflake\ml\_internal\telemetry.py", line 542, in wrap
    return ctx.run(execute_func_with_statement_params)
  ...
  File "C:\Mas\py39_sf_ml\lib\site-packages\snowflake\ml\model\_packager\model_task\model_task_utils.py", line 149, in _get_model_task    
    raise ValueError(f"Model type {type(model)} is not supported")
ValueError: Model type <class 'NoneType'> is not supported

Code Snippet:

from snowflake.snowpark import Session
from snowflake.ml.registry import registry
from snowflake.ml.modeling.preprocessing import StandardScaler
from snowflake.ml.modeling.impute import SimpleImputer
from snowflake.ml.modeling.pipeline import Pipeline
from snowflake.ml.modeling.xgboost import XGBClassifier

# Snowflake connection parameters
conn_params = {
    "user": "<...>",
    "account": "<...>",
    "warehouse": "<...>",
    "database": "<...>",
    "schema": "<...>",
    "role": "<...>",
    "authenticator": "externalbrowser",
}

# Create session
session = Session.builder.configs(conn_params).create()

# Load and prepare data
all_data = session.sql("SELECT *, IFF(CLASS = 'g', 1.0, 0.0) AS LABEL FROM Gamma_Telescope_Data").drop("CLASS")
train_data, test_data = all_data.random_split(weights=[0.9, 0.1], seed=0)

# Define feature and label columns
FEATURE_COLS = [c for c in train_data.columns if c != "LABEL"]
LABEL_COLS = ["LABEL"]

# Construct pipeline
pipeline = Pipeline(steps=[
    ("impute", SimpleImputer(input_cols=FEATURE_COLS, output_cols=FEATURE_COLS)),
    ("scaler", StandardScaler(input_cols=FEATURE_COLS, output_cols=FEATURE_COLS)),
    ("model", XGBClassifier(input_cols=FEATURE_COLS, label_cols=LABEL_COLS))
])

# Train the pipeline
pipeline.fit(train_data)

# Register model
native_registry = registry.Registry(
    session=session, 
    database_name=session.get_current_database(), 
    schema_name=session.get_current_schema()
)

model_name = "Gamma_test"
version = "V8"

model_version = native_registry.log_model(
    model=pipeline,  # <-- This line triggers the error
    model_name=model_name,
    version_name=version,
    sample_input_data=test_data,
    comment="Gamma test",
    conda_dependencies=["snowflake-ml-python==1.7.4", "snowflake-snowpark-python==1.28.0"],
    options={"enable_explainability": True}
)

Observations & Debugging Attempts:

1. Pipeline Training Works – pipeline.fit(train_data) runs without errors.
2. Pipeline Predictions Work – Predictions on training and test data succeed.
3. Model Explanation Works Without Pipeline – If I train an XGBClassifier without a pipeline, I can successfully generate predictions and explanations.
4. Session is Active – session.get_current_database() and session.get_current_schema() return valid values.
5. Feature & Label Columns Look Correct – FEATURE_COLS and LABEL_COLS contain expected values.

Additional Context:

• Environment:
  • Win 10
  • Python 3.9
  • snowflake-connector-python 3.14.0
  • snowflake-ml-python 1.7.4
  • snowflake-snowpark-python 1.28.0
• This example is based on Snowflake’s official documentation:
  Feature Preprocessing and Training on Non-Synthetic Data
• The documentation suggests using a Pipeline, but it does not provide an example of registering a Pipeline-based model that has explainability.
• The error message suggests that the model is somehow being treated as NoneType when passed to log_model().
• As mentioned the whole pipeline can be registered and retrieved when I remove this options={"enable_explainability": True} from .log_model.
• This options={"enable_explainability": True} works if I don't have a pipeline: Snowflake ML: `There is no method with name explain available in the model`

Question:

Is it possible to register a pipeline with options={"enable_explainability": True}?

I am training an XGBClassifier model using Snowflake ML and attempting to register it using Snowflake’s model registry. The training and evaluation steps complete successfully, but when I try to log the model using log_model(), I get the following error:

Training Accuracy: 0.9610
Evaluation Accuracy: 0.8730
Traceback (most recent call last):
  File "C:\Mas\rla_projects\Claims-AI--lodgement\code\python\src\explain\train_model.py", line 56, in <module>
    model_version = native_registry.log_model(
  File "C:\Mas\py39_sf_ml\lib\site-packages\snowflake\ml\_internal\telemetry.py", line 542, in wrap
    return ctx.run(execute_func_with_statement_params)
  ...
  File "C:\Mas\py39_sf_ml\lib\site-packages\snowflake\ml\model\_packager\model_task\model_task_utils.py", line 149, in _get_model_task    
    raise ValueError(f"Model type {type(model)} is not supported")
ValueError: Model type <class 'NoneType'> is not supported

Code Snippet:

from snowflake.snowpark import Session
from snowflake.ml.registry import registry
from snowflake.ml.modeling.preprocessing import StandardScaler
from snowflake.ml.modeling.impute import SimpleImputer
from snowflake.ml.modeling.pipeline import Pipeline
from snowflake.ml.modeling.xgboost import XGBClassifier

# Snowflake connection parameters
conn_params = {
    "user": "<...>",
    "account": "<...>",
    "warehouse": "<...>",
    "database": "<...>",
    "schema": "<...>",
    "role": "<...>",
    "authenticator": "externalbrowser",
}

# Create session
session = Session.builder.configs(conn_params).create()

# Load and prepare data
all_data = session.sql("SELECT *, IFF(CLASS = 'g', 1.0, 0.0) AS LABEL FROM Gamma_Telescope_Data").drop("CLASS")
train_data, test_data = all_data.random_split(weights=[0.9, 0.1], seed=0)

# Define feature and label columns
FEATURE_COLS = [c for c in train_data.columns if c != "LABEL"]
LABEL_COLS = ["LABEL"]

# Construct pipeline
pipeline = Pipeline(steps=[
    ("impute", SimpleImputer(input_cols=FEATURE_COLS, output_cols=FEATURE_COLS)),
    ("scaler", StandardScaler(input_cols=FEATURE_COLS, output_cols=FEATURE_COLS)),
    ("model", XGBClassifier(input_cols=FEATURE_COLS, label_cols=LABEL_COLS))
])

# Train the pipeline
pipeline.fit(train_data)

# Register model
native_registry = registry.Registry(
    session=session, 
    database_name=session.get_current_database(), 
    schema_name=session.get_current_schema()
)

model_name = "Gamma_test"
version = "V8"

model_version = native_registry.log_model(
    model=pipeline,  # <-- This line triggers the error
    model_name=model_name,
    version_name=version,
    sample_input_data=test_data,
    comment="Gamma test",
    conda_dependencies=["snowflake-ml-python==1.7.4", "snowflake-snowpark-python==1.28.0"],
    options={"enable_explainability": True}
)

Observations & Debugging Attempts:

1. Pipeline Training Works – pipeline.fit(train_data) runs without errors.
2. Pipeline Predictions Work – Predictions on training and test data succeed.
3. Model Explanation Works Without Pipeline – If I train an XGBClassifier without a pipeline, I can successfully generate predictions and explanations.
4. Session is Active – session.get_current_database() and session.get_current_schema() return valid values.
5. Feature & Label Columns Look Correct – FEATURE_COLS and LABEL_COLS contain expected values.

Additional Context:

• Environment:
  • Win 10
  • Python 3.9
  • snowflake-connector-python 3.14.0
  • snowflake-ml-python 1.7.4
  • snowflake-snowpark-python 1.28.0
• This example is based on Snowflake’s official documentation:
  Feature Preprocessing and Training on Non-Synthetic Data
• The documentation suggests using a Pipeline, but it does not provide an example of registering a Pipeline-based model that has explainability.
• The error message suggests that the model is somehow being treated as NoneType when passed to log_model().
• As mentioned the whole pipeline can be registered and retrieved when I remove this options={"enable_explainability": True} from .log_model.
• This options={"enable_explainability": True} works if I don't have a pipeline: Snowflake ML: `There is no method with name explain available in the model`

Question:

Is it possible to register a pipeline with options={"enable_explainability": True}?

• python
• machine-learning
• snowflake-cloud-data-platform

1 Answer 1

it seems that in the new version (1.7.5) they have added support for

• Explainability: Support native and snowml sklearn pipeline

issue I raises:
https://github/snowflakedb/snowflake-ml-python/issues/145