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:
- Pipeline Training Works –
pipeline.fit(train_data) runs without errors.
- Pipeline Predictions Work – Predictions on training and test data succeed.
- Model Explanation Works Without Pipeline – If I train an
XGBClassifier without a pipeline, I can successfully generate predictions and explanations.
- Session is Active –
session.get_current_database() and session.get_current_schema() return valid values.
- Feature & Label Columns Look Correct –
FEATURE_COLS and LABEL_COLS contain expected values.
Additional Context:
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:Code Snippet:
Observations & Debugging Attempts:
pipeline.fit(train_data)runs without errors.XGBClassifierwithout a pipeline, I can successfully generate predictions and explanations.session.get_current_database()andsession.get_current_schema()return valid values.FEATURE_COLSandLABEL_COLScontain expected values.Additional Context:
Feature Preprocessing and Training on Non-Synthetic Data
Pipeline, but it does not provide an example of registering aPipeline-based model that has explainability.NoneTypewhen passed tolog_model().options={"enable_explainability": True}from.log_model.options={"enable_explainability": True}works if I don't have a pipeline: https://stackoverflow.com/questions/79488423/snowflake-ml-there-is-no-method-with-name-explain-available-in-the-modelQuestion:
Is it possible to register a pipeline with
options={"enable_explainability": True}?