def set_dependencies(self, env: BentoServiceEnv): # Note that keras module is not required, user can use tf.keras as an # replacement for the keras module. Although tensorflow module is required to # be used as the default Keras backend pip_deps = ['tensorflow'] if self._keras_module_name == 'keras': pip_deps.append('keras') env.add_pip_dependencies_if_missing(pip_deps)
def set_dependencies(self, env: BentoServiceEnv): logger.warning( "BentoML by default does not include spacy and torchvision package when " "using FastaiModelArtifact. To make sure BentoML bundle those packages if " "they are required for your model, either import those packages in " "BentoService definition file or manually add them via " "`@env(pip_dependencies=['torchvision'])` when defining a BentoService" ) env.add_pip_dependencies_if_missing(['torch', "fastai"])
def _init_env(self): self._env = self.__class__._env or BentoServiceEnv(self.name) for api in self._service_apis: self._env._add_pip_dependencies_if_missing(api.handler.pip_dependencies) for artifact in self._artifacts: self._env._add_pip_dependencies_if_missing(artifact.pip_dependencies)
def _init_env(self, env=None): if env is None: # By default use BentoServiceEnv defined on class via @env decorator env = self.__class__._env if isinstance(env, dict): self._env = BentoServiceEnv.fromDict(env) else: self._env = env
def decorator(bento_service_cls): bento_service_cls._env = BentoServiceEnv( bento_service_name=bento_service_cls.name(), setup_sh=setup_sh, pip_dependencies=pip_dependencies, conda_channels=conda_channels, conda_dependencies=conda_dependencies, ) return bento_service_cls
def decorator(bento_service_cls): bento_service_cls._env = BentoServiceEnv( bento_service_name=bento_service_cls.name(), pip_dependencies=pip_dependencies, auto_pip_dependencies=auto_pip_dependencies, requirements_txt_file=requirements_txt_file, conda_channels=conda_channels, conda_dependencies=conda_dependencies, setup_sh=setup_sh, ) return bento_service_cls
def _config_environments(self): self._env = self.__class__._env or BentoServiceEnv(self.name) for api in self._inference_apis: self._env.add_pip_dependencies_if_missing( api.handler.pip_dependencies) self._env.add_pip_dependencies_if_missing( api.output_adapter.pip_dependencies) for artifact in self.artifacts.get_artifact_list(): artifact.set_dependencies(self.env)
def _init_env(self, env=None): if env is None: # By default use BentoServiceEnv defined on class via @env decorator env = self.__class__._env if isinstance(env, dict): self._env = BentoServiceEnv.from_dict(env) else: self._env = env for api in self._service_apis: self._env.add_handler_dependencies(api.handler.pip_dependencies)
def _init_env(self): self._env = self.__class__._env or BentoServiceEnv(self.name) for api in self._service_apis: self._env._add_pip_dependencies_if_missing( api.handler.pip_dependencies) self._env._add_pip_dependencies_if_missing( api.output_adapter.pip_dependencies) # TODO(bojiang): adapter dependencies for artifact in self._artifacts: self._env._add_pip_dependencies_if_missing( artifact.pip_dependencies)
def __init__(self, artifacts, env=None): # TODO: validate artifacts arg matches self.__class__._artifacts_spec definition if isinstance(artifacts, ArtifactCollection): self._artifacts = artifacts else: self._artifacts = ArtifactCollection() for artifact in artifacts: self._artifacts[artifact.name] = artifact if env is None: # By default use BentoServiceEnv defined on class via @env decorator env = self.__class__._env if isinstance(env, dict): self._env = BentoServiceEnv.fromDict(env) else: self._env = env self._config_service_apis() self.name = self.__class__.name()
def _init_env(self): self._env = self.__class__._env or BentoServiceEnv(self.name) for api in self._service_apis: self._env.add_handler_dependencies(api.handler.pip_dependencies)
def set_dependencies(self, env: BentoServiceEnv): if self.backend == 'onnxruntime': env.add_pip_dependencies_if_missing(['onnxruntime'])
def decorator(bento_service_cls): bento_service_cls._env = BentoServiceEnv.from_dict(kwargs) return bento_service_cls
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(["fasttext"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(['h2o']) env.add_conda_dependencies(['openjdk'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(['coremltools>=4.0b2'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(['xgboost'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(['tensorflow'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_dependencies_if_missing(['lightgbm'])