def configure(self, params):
super(PredictionServer, self).configure(params)
self._show_perf = self._params.get("show_perf")
self._run_language = RunLanguage(params.get("run_language"))
self._target_type = TargetType(params[TARGET_TYPE_ARG_KEYWORD])
self._stats_collector = StatsCollector(
disable_instance=not self._show_perf)
self._stats_collector.register_report("run_predictor_total", "finish",
StatsOperation.SUB, "start")
self._memory_monitor = MemoryMonitor(monitor_current_process=True)
if self._run_language == RunLanguage.PYTHON:
from datarobot_drum.drum.language_predictors.python_predictor.python_predictor import (
PythonPredictor, )
self._predictor = PythonPredictor()
elif self._run_language == RunLanguage.JAVA:
from datarobot_drum.drum.language_predictors.java_predictor.java_predictor import (
JavaPredictor, )
self._predictor = JavaPredictor()
elif self._run_language == RunLanguage.R:
# this import is here, because RPredictor imports rpy library,
# which is not installed for Java and Python cases.
from datarobot_drum.drum.language_predictors.r_predictor.r_predictor import RPredictor
self._predictor = RPredictor()
else:
raise DrumCommonException(
"Prediction server doesn't support language: {} ".format(
self._run_language))
self._predictor.configure(params)
def configure(self, params):
super(GenericPredictorComponent, self).configure(params)
self._run_language = RunLanguage(params.get("run_language"))
self._target_type = TargetType(params[TARGET_TYPE_ARG_KEYWORD])
if self._run_language == RunLanguage.PYTHON:
from datarobot_drum.drum.language_predictors.python_predictor.python_predictor import (
PythonPredictor, )
self._predictor = PythonPredictor()
elif self._run_language == RunLanguage.JAVA:
from datarobot_drum.drum.language_predictors.java_predictor.java_predictor import (
JavaPredictor, )
self._predictor = JavaPredictor()
elif self._run_language == RunLanguage.R:
# this import is here, because RPredictor imports rpy library,
# which is not installed for Java and Python cases.
from datarobot_drum.drum.language_predictors.r_predictor.r_predictor import RPredictor
self._predictor = RPredictor()
else:
raise DrumCommonException(
"Prediction server doesn't support language: {} ".format(
self._run_language))
self._predictor.configure(params)
def _resolve_target_type(self):
if self.run_mode == RunMode.NEW:
return
target_type_options = getattr(self.options, "target_type", None)
target_type_options = (None if target_type_options is None else
TargetType(target_type_options))
target_type_model_config = None
if self.options.model_config is not None:
target_type_model_config = TargetType(
self.options.model_config["targetType"])
if target_type_options is None and target_type_model_config is None:
raise DrumCommonException(
"Target type is missing. It must be provided in --target-type argument, {} env var or model config file."
.format(ArgumentOptionsEnvVars.TARGET_TYPE))
elif (all([target_type_options, target_type_model_config])
and target_type_options != target_type_model_config):
raise DrumCommonException(
"Target type provided in --target-type argument doesn't match target type from model config file. "
"Use either one of them or make them match.")
else:
self.target_type = (target_type_options if target_type_options
is not None else target_type_model_config)
if self.target_type != TargetType.UNSTRUCTURED:
if getattr(self.options, "query", None):
raise DrumCommonException(
"--query argument can be used only with --target-type unstructured"
)
if getattr(self.options, "content_type", None):
raise DrumCommonException(
"--content-type argument can be used only with --target-type unstructured"
)
else:
if self.options.content_type is None:
self.options.content_type = "text/plain; charset=utf8"
def _resolve_target_type(self):
target_type_options = None
target_type_model_config = None
if hasattr(self.options,
"target_type") and self.options.target_type is not None:
target_type_options = TargetType(self.options.target_type)
if self.options.model_config is not None:
target_type_model_config = TargetType(
self.options.model_config["targetType"])
if self.run_mode not in [RunMode.NEW]:
if target_type_options is None and target_type_model_config is None:
raise DrumCommonException(
"Target type is missing. It must be provided in either --target-type argument or model config file."
)
elif (all([target_type_options, target_type_model_config])
and target_type_options != target_type_model_config):
raise DrumCommonException(
"Target type provided in --target-type argument doesn't match target type from model config file."
"Use either one of them or make them match.")
else:
self.target_type = (target_type_options if target_type_options
is not None else target_type_model_config)
if self.target_type != TargetType.UNSTRUCTURED:
if getattr(self.options, "query", None):
raise DrumCommonException(
"--query argument can be used only with --target-type unstructured"
)
if getattr(self.options, "content_type", None):
raise DrumCommonException(
"--content-type argument can be used only with --target-type unstructured"
)
else:
if self.options.content_type is None:
self.options.content_type = "text/plain; charset=utf8"
def configure(self, params):
self._custom_model_path = params["__custom_model_path__"]
self._positive_class_label = params.get("positiveClassLabel")
self._negative_class_label = params.get("negativeClassLabel")
self._target_type = TargetType(params.get("target_type"))
self._params = params
if self._params["monitor"] == "True":
if not mlops_loaded:
raise Exception("MLOps module was not imported: {}".format(
mlops_import_error))
# TODO: if server use async, if batch, use sync etc.. some way of passing params
self._mlops = (MLOps().set_model_id(
self._params["model_id"]).set_deployment_id(
self._params["deployment_id"]).set_channel_config(
self._params["monitor_settings"]).init())
def configure(self, params):
"""
@brief It is called in within the 'deputy' context
"""
super(UwsgiServing, self).configure(params)
self._show_perf = self._params.get("show_perf")
self._run_language = RunLanguage(params.get("run_language"))
self._target_type = TargetType(params[TARGET_TYPE_ARG_KEYWORD])
self._stats_collector = StatsCollector(disable_instance=not self._show_perf)
self._stats_collector.register_report(
"run_predictor_total", "finish", StatsOperation.SUB, "start"
)
self._memory_monitor = MemoryMonitor()
self._logger.info(
"Configure component with input params, name: {}, params: {}".format(
self.name(), params
)
)