[BUG] MlflowException: No Gateway server uri has been set. (while using a deployments server) about mlflow HOT 5 OPEN

pip install git+https://github.com/mlflow/mlflow.git@refs/pull/11080/merge

---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\utils\request_utils.py:63, in augmented_raise_for_status(response)
     62 try:
---> 63     response.raise_for_status()
     64 except HTTPError as e:

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\requests\models.py:1021, in Response.raise_for_status(self)
   1020 if http_error_msg:
-> 1021     raise HTTPError(http_error_msg, response=self)

HTTPError: 400 Client Error: Bad Request for url: http://localhost:7000/gateway/completions/invocations

During handling of the above exception, another exception occurred:

HTTPError                                 Traceback (most recent call last)
Cell In[14], line 17
     14 print(data)
     16 import pandas as pd
---> 17 loaded_model.predict(pd.DataFrame(data))

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\pyfunc\__init__.py:501, in PyFuncModel.predict(self, data, params)
    498         if _MLFLOW_TESTING.get():
    499             raise
--> 501 return _predict()

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\pyfunc\__init__.py:489, in PyFuncModel.predict.<locals>._predict()
    487     return self._predict_fn(data, params=params)
    488 _log_warning_if_params_not_in_predict_signature(_logger, params)
--> 489 return self._predict_fn(data)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\promptlab\__init__.py:37, in _PromptlabModel.predict(self, inputs)
     34     model_parameters_as_dict = {param.key: param.value for param in self.model_parameters}
     35     query_data = self._construct_query_data(prompt)
---> 37     response = query(
     38         route=self.model_route, data={**query_data, **model_parameters_as_dict}
     39     )
     40     results.append(self._parse_gateway_response(response))
     42 return results

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\utils.py:144, in gateway_deprecated.<locals>.wrapper(*args, **kwargs)
    141 @functools.wraps(obj)
    142 def wrapper(*args, **kwargs):
    143     warnings.warn(msg, FutureWarning, stacklevel=2)
--> 144     return obj(*args, **kwargs)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\fluent.py:254, in query(route, data)
    188 @gateway_deprecated
    189 def query(route: str, data):
    190     """
    191     Issues a query request to a configured service through a named route on the Gateway Server.
    192     This function will interface with a configured route name (examples below) and return the
   (...)
    252         )
    253     """
--> 254     return MlflowGatewayClient().query(route, data)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\utils.py:144, in gateway_deprecated.<locals>.wrapper(*args, **kwargs)
    141 @functools.wraps(obj)
    142 def wrapper(*args, **kwargs):
    143     warnings.warn(msg, FutureWarning, stacklevel=2)
--> 144     return obj(*args, **kwargs)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\client.py:361, in MlflowGatewayClient.query(self, route, data)
    358 query_route = assemble_uri_path([MLFLOW_GATEWAY_ROUTE_BASE, route, MLFLOW_QUERY_SUFFIX])
    360 try:
--> 361     return self._call_endpoint("POST", query_route, data).json()
    362 except MlflowException as e:
    363     if isinstance(e.__cause__, requests.exceptions.Timeout):

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\client.py:106, in MlflowGatewayClient._call_endpoint(self, method, route, json_body)
     95     call_kwargs["json"] = json_body
     97 response = http_request(
     98     host_creds=self._host_creds,
     99     endpoint=route,
   (...)
    104     **call_kwargs,
    105 )
--> 106 augmented_raise_for_status(response)
    107 return response

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\utils\request_utils.py:66, in augmented_raise_for_status(response)
     64 except HTTPError as e:
     65     if response.text:
---> 66         raise HTTPError(
     67             f"{e}. Response text: {response.text}", request=e.request, response=e.response
     68         )
     69     else:
     70         raise e

HTTPError: 400 Client Error: Bad Request for url: http://localhost:7000/gateway/completions/invocations. Response text: {"detail":"Unrecognized request argument supplied: route_type"}

with mlflow.start_run():
    results = mlflow.evaluate(
        model=logged_model,
        data=data,
        # targets="ground_truth",
        model_type="text-summarization",
    )

---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\utils\request_utils.py:63, in augmented_raise_for_status(response)
     62 try:
---> 63     response.raise_for_status()
     64 except HTTPError as e:

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\requests\models.py:1021, in Response.raise_for_status(self)
   1020 if http_error_msg:
-> 1021     raise HTTPError(http_error_msg, response=self)

HTTPError: 400 Client Error: Bad Request for url: http://localhost:7000/gateway/completions/invocations

During handling of the above exception, another exception occurred:

HTTPError                                 Traceback (most recent call last)
Cell In[17], line 20
     17 # loaded_model.predict(pd.DataFrame(data))
     19 with mlflow.start_run():
---> 20     results = mlflow.evaluate(
     21         model=logged_model,
     22         data=data,
     23         # targets="ground_truth",
     24         model_type="text-summarization",
     25     )

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\models\evaluation\base.py:1880, in evaluate(model, data, model_type, targets, predictions, dataset_path, feature_names, evaluators, evaluator_config, custom_metrics, extra_metrics, custom_artifacts, validation_thresholds, baseline_model, env_manager, model_config, baseline_config)
   1877 predictions_expected_in_model_output = predictions if model is not None else None
   1879 try:
-> 1880     evaluate_result = _evaluate(
   1881         model=model,
   1882         model_type=model_type,
   1883         dataset=dataset,
   1884         run_id=run_id,
   1885         evaluator_name_list=evaluator_name_list,
   1886         evaluator_name_to_conf_map=evaluator_name_to_conf_map,
   1887         custom_metrics=custom_metrics,
   1888         extra_metrics=extra_metrics,
   1889         custom_artifacts=custom_artifacts,
   1890         baseline_model=baseline_model,
   1891         predictions=predictions_expected_in_model_output,
   1892     )
   1893 finally:
   1894     if isinstance(model, _ServedPyFuncModel):

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\models\evaluation\base.py:1120, in _evaluate(model, model_type, dataset, run_id, evaluator_name_list, evaluator_name_to_conf_map, custom_metrics, extra_metrics, custom_artifacts, baseline_model, predictions)
   1118     if evaluator.can_evaluate(model_type=model_type, evaluator_config=config):
   1119         _logger.info(f"Evaluating the model with the {evaluator_name} evaluator.")
-> 1120         eval_result = evaluator.evaluate(
   1121             model=model,
   1122             model_type=model_type,
   1123             dataset=dataset,
   1124             run_id=run_id,
   1125             evaluator_config=config,
   1126             custom_metrics=custom_metrics,
   1127             extra_metrics=extra_metrics,
   1128             custom_artifacts=custom_artifacts,
   1129             baseline_model=baseline_model,
   1130             predictions=predictions,
   1131         )
   1132         eval_results.append(eval_result)
   1134 _last_failed_evaluator = None

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\models\evaluation\default_evaluator.py:1883, in DefaultEvaluator.evaluate(self, model_type, dataset, run_id, evaluator_config, model, custom_metrics, extra_metrics, custom_artifacts, baseline_model, predictions, **kwargs)
   1881     if baseline_model:
   1882         _logger.info("Evaluating candidate model:")
-> 1883     evaluation_result = self._evaluate(model, is_baseline_model=False)
   1885 if not baseline_model:
   1886     return evaluation_result

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\models\evaluation\default_evaluator.py:1734, in DefaultEvaluator._evaluate(self, model, is_baseline_model, **kwargs)
   1732         self.extra_metrics.remove(extra_metric)
   1733         break
-> 1734 self._generate_model_predictions(compute_latency=compute_latency)
   1735 if self.model_type in (_ModelType.CLASSIFIER, _ModelType.REGRESSOR):
   1736     self._compute_builtin_metrics()

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\models\evaluation\default_evaluator.py:1369, in DefaultEvaluator._generate_model_predictions(self, compute_latency)
   1367         model_predictions = predict_with_latency(X_copy)
   1368     else:
-> 1369         model_predictions = self.model.predict(X_copy)
   1370 else:
   1371     if self.dataset.predictions_data is None:

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\pyfunc\__init__.py:501, in PyFuncModel.predict(self, data, params)
    498         if _MLFLOW_TESTING.get():
    499             raise
--> 501 return _predict()

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\pyfunc\__init__.py:489, in PyFuncModel.predict.<locals>._predict()
    487     return self._predict_fn(data, params=params)
    488 _log_warning_if_params_not_in_predict_signature(_logger, params)
--> 489 return self._predict_fn(data)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\promptlab\__init__.py:37, in _PromptlabModel.predict(self, inputs)
     34     model_parameters_as_dict = {param.key: param.value for param in self.model_parameters}
     35     query_data = self._construct_query_data(prompt)
---> 37     response = query(
     38         route=self.model_route, data={**query_data, **model_parameters_as_dict}
     39     )
     40     results.append(self._parse_gateway_response(response))
     42 return results

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\utils.py:144, in gateway_deprecated.<locals>.wrapper(*args, **kwargs)
    141 @functools.wraps(obj)
    142 def wrapper(*args, **kwargs):
    143     warnings.warn(msg, FutureWarning, stacklevel=2)
--> 144     return obj(*args, **kwargs)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\fluent.py:254, in query(route, data)
    188 @gateway_deprecated
    189 def query(route: str, data):
    190     """
    191     Issues a query request to a configured service through a named route on the Gateway Server.
    192     This function will interface with a configured route name (examples below) and return the
   (...)
    252         )
    253     """
--> 254     return MlflowGatewayClient().query(route, data)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\utils.py:144, in gateway_deprecated.<locals>.wrapper(*args, **kwargs)
    141 @functools.wraps(obj)
    142 def wrapper(*args, **kwargs):
    143     warnings.warn(msg, FutureWarning, stacklevel=2)
--> 144     return obj(*args, **kwargs)

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\client.py:361, in MlflowGatewayClient.query(self, route, data)
    358 query_route = assemble_uri_path([MLFLOW_GATEWAY_ROUTE_BASE, route, MLFLOW_QUERY_SUFFIX])
    360 try:
--> 361     return self._call_endpoint("POST", query_route, data).json()
    362 except MlflowException as e:
    363     if isinstance(e.__cause__, requests.exceptions.Timeout):

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\gateway\client.py:106, in MlflowGatewayClient._call_endpoint(self, method, route, json_body)
     95     call_kwargs["json"] = json_body
     97 response = http_request(
     98     host_creds=self._host_creds,
     99     endpoint=route,
   (...)
    104     **call_kwargs,
    105 )
--> 106 augmented_raise_for_status(response)
    107 return response

File c:\Users\_\miniconda3\envs\ml-3.10\lib\site-packages\mlflow\utils\request_utils.py:66, in augmented_raise_for_status(response)
     64 except HTTPError as e:
     65     if response.text:
---> 66         raise HTTPError(
     67             f"{e}. Response text: {response.text}", request=e.request, response=e.response
     68         )
     69     else:
     70         raise e

HTTPError: 400 Client Error: Bad Request for url: http://localhost:7000/gateway/completions/invocations. Response text: {"detail":"Unrecognized request argument supplied: route_type"}