Unable to register Scikit-learn or XGBoost model to unity catalog
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08-25-2025 02:12 AM
Hello, I'm following the tutorial provided here https://docs.databricks.com/aws/en/notebooks/source/mlflow/mlflow-classic-ml-e2e-mlflow-3.html for ML model management process using ML FLow, in a unity-catalog enabled workspace, however I'm facing an error I'm unable to understand and fix.
The error is specifically occurring in the section 4 of the tutorial: Log the model using MLflow:
# Incorporate MLflow evaluation
evaluation_data = X_test.copy()
evaluation_data["label"] = y_test
# Log the model and training metadata results
with mlflow.start_run(run_name="my_xgboost_regression") as run:
# Extract metrics
final_train_rmse = np.array(reg.evals_result()["validation_0"]["rmse"])[-1]
final_test_rmse = np.array(reg.evals_result()["validation_1"]["rmse"])[-1]
# Extract parameters for logging
feature_map = {key: value for key, value in reg.get_xgb_params().items() if value is not None}
# Generate a model signature using the infer_signature utility in MLflow
# A signature is required to register the model to Unity Catalog
# so that the model can be used in SQL queries
signature = infer_signature(X, reg.predict(X))
# Log parameters
mlflow.log_params(feature_map)
# Log the model to MLflow and register the model to Unity Catalog
# All model metrics and parameters will be available in Unity Catalog
model_info = mlflow.xgboost.log_model(
xgb_model=reg,
name="xgboost_regression_model",
input_example=X.iloc[[0]],
signature=signature,
registered_model_name="my_catalog.y_schema.xgboost_regression_model",
)Here's the stack trace of model logging error:
🔗 View Logged Model at: https://dbc-my-ws-account.cloud.databricks.com/ml/experiments/2706846431648976/models/m-782e22e859f24a63a29e0d9ee9227a6a?o=1366243967608557
/local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/xgboost/sklearn.py:1028: UserWarning: [08:35:04] WARNING: /workspace/src/c_api/c_api.cc:1427: Saving model in the UBJSON format as default. You can use file extension: `json`, `ubj` or `deprecated` to choose between formats.
self.get_booster().save_model(fname)
Successfully registered model 'my_catalag.my_schema.xgboost_regression_model'.
MlflowException: The following failures occurred while uploading one or more artifacts to s3://my-s3-catalog-bucket/models/95d12042-52cb-4348-a05f-4bbdfe0c71f1/versions/814a5856-83df-4ed2-b4df-dc3dfc5c34ad: {'/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/serving_input_example.json': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/conda.yaml': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/requirements.txt': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/MLmodel': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/input_example.json': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/model.xgb': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/python_env.yaml': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/metadata/conda.yaml': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/metadata/requirements.txt': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/metadata/MLmodel': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", '/local_disk0/repl_tmp_data/ReplId-198e0-2fc1c-a/tmp4w84_er0/model/metadata/python_env.yaml': "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')"}
File <command-2706846431648989>, line 24
20 mlflow.log_params(feature_map)
22 # Log the model to MLflow and register the model to Unity Catalog
23 # All model metrics and parameters will be available in Unity Catalog
---> 24 model_info = mlflow.xgboost.log_model(
25 xgb_model=reg,
26 name="xgboost_regression_model",
27 input_example=X.iloc[[0]],
28 signature=signature,
29 registered_model_name="my_catalog.my_schema.xgboost_regression_model",
30 )
32 # Log metrics to the run and model
33 mlflow.log_metric("train_rmse", final_train_rmse)
File <command-2706846431648989>, line 24
20 mlflow.log_params(feature_map)
22 # Log the model to MLflow and register the model to Unity Catalog
23 # All model metrics and parameters will be available in Unity Catalog
---> 24 model_info = mlflow.xgboost.log_model(
25 xgb_model=reg,
26 name="xgboost_regression_model",
27 input_example=X.iloc[[0]],
28 signature=signature,
29 registered_model_name="my_catalog.my_schema.xgboost_regression_model",
30 )
32 # Log metrics to the run and model
33 mlflow.log_metric("train_rmse", final_train_rmse)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/xgboost/__init__.py:279, in log_model(xgb_model, artifact_path, conda_env, code_paths, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, model_format, metadata, name, params, tags, model_type, step, model_id, **kwargs)
225 @format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name=FLAVOR_NAME))
226 def log_model(
227 xgb_model,
(...)
245 **kwargs,
246 ):
247 """Log an XGBoost model as an MLflow artifact for the current run.
248
249 Args:
(...)
277 metadata of the logged model.
278 """
--> 279 return Model.log(
280 artifact_path=artifact_path,
281 name=name,
282 flavor=mlflow.xgboost,
283 registered_model_name=registered_model_name,
284 xgb_model=xgb_model,
285 model_format=model_format,
286 conda_env=conda_env,
287 code_paths=code_paths,
288 signature=signature,
289 input_example=input_example,
290 await_registration_for=await_registration_for,
291 pip_requirements=pip_requirements,
292 extra_pip_requirements=extra_pip_requirements,
293 metadata=metadata,
294 params=params,
295 tags=tags,
296 model_type=model_type,
297 step=step,
298 model_id=model_id,
299 **kwargs,
300 )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/models/model.py:1357, in Model.log(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, run_id, resources, auth_policy, prompts, name, model_type, params, tags, step, model_id, **kwargs)
1354 _logger.warning("Failed to log model config as params: %s", str(e))
1356 if registered_model_name is not None:
-> 1357 registered_model = mlflow.tracking._model_registry.fluent._register_model(
1358 f"models:/{model.model_id}",
1359 registered_model_name,
1360 await_registration_for=await_registration_for,
1361 local_model_path=local_path,
1362 )
1363 model_info = mlflow_model.get_model_info(model)
1364 if registered_model is not None:
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/tracking/_model_registry/fluent.py:213, in _register_model(model_uri, name, await_registration_for, tags, local_model_path, env_pack)
207 with pack_env_for_databricks_model_serving(
208 model_uri,
209 enforce_pip_requirements=True,
210 ) as artifacts_path_with_env:
211 client.log_model_artifacts(model_id, artifacts_path_with_env)
--> 213 create_version_response = client._create_model_version(
214 name=name,
215 source=source,
216 run_id=run_id,
217 tags=tags,
218 await_creation_for=await_registration_for,
219 local_model_path=local_model_path,
220 model_id=model_id,
221 )
222 created_message = (
223 f"Created version '{create_version_response.version}' of model "
224 f"'{create_version_response.name}'"
225 )
226 # Print a link to the UC model version page if the model is in UC.
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/tracking/client.py:4198, in MlflowClient._create_model_version(self, name, source, run_id, tags, run_link, description, await_creation_for, local_model_path, model_id)
4195 # models:/<model_id> source is not supported by WSMR
4196 new_source = logged_model.artifact_location
-> 4198 return self._get_registry_client().create_model_version(
4199 name=name,
4200 source=new_source,
4201 run_id=run_id,
4202 tags=tags,
4203 run_link=run_link,
4204 description=description,
4205 await_creation_for=await_creation_for,
4206 local_model_path=local_model_path,
4207 model_id=model_id,
4208 )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/telemetry/track.py:22, in record_usage_event.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
19 @functools.wraps(func)
20 def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
21 if is_telemetry_disabled() or _is_telemetry_disabled_for_event(event):
---> 22 return func(*args, **kwargs)
24 success = True
25 start_time = time.time()
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/tracking/_model_registry/client.py:266, in ModelRegistryClient.create_model_version(self, name, source, run_id, tags, run_link, description, await_creation_for, local_model_path, model_id)
264 arg_names = _get_arg_names(self.store.create_model_version)
265 if "local_model_path" in arg_names:
--> 266 mv = self.store.create_model_version(
267 name,
268 source,
269 run_id,
270 tags,
271 run_link,
272 description,
273 local_model_path=local_model_path,
274 model_id=model_id,
275 )
276 else:
277 # Fall back to calling create_model_version without
278 # local_model_path since old model registry store implementations may not
279 # support the local_model_path argument.
280 mv = self.store.create_model_version(
281 name, source, run_id, tags, run_link, description, model_id=model_id
282 )
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/store/_unity_catalog/registry/rest_store.py:983, in UcModelRegistryStore.create_model_version(self, name, source, run_id, tags, run_link, description, local_model_path, model_id)
978 model_version = self._call_endpoint(
979 CreateModelVersionRequest, req_body, extra_headers=extra_headers
980 ).model_version
982 store = self._get_artifact_repo(model_version, full_name)
--> 983 store.log_artifacts(local_dir=local_model_dir, artifact_path="")
984 finalized_mv = self._finalize_model_version(
985 name=full_name, version=model_version.version
986 )
987 return model_version_from_uc_proto(finalized_mv)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-ea9e38f3-62ca-47d0-b399-bd069dfe7414/lib/python3.12/site-packages/mlflow/store/artifact/cloud_artifact_repo.py:168, in CloudArtifactRepository.log_artifacts(self, local_dir, artifact_path)
165 failed_uploads[src_file_path] = repr(e)
167 if len(failed_uploads) > 0:
--> 168 raise MlflowException(
169 message=(
170 "The following failures occurred while uploading one or more artifacts"
171 f" to {self.artifact_uri}: {failed_uploads}"
172 )
173 )
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Feature Store
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Model Serving
