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_packages(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_packages=['torchvision'])` when defining a BentoService" ) env.add_pip_packages(['torch', "fastai<2.0.0"])
def _config_environments(self): self._env = self.__class__._env or BentoServiceEnv() for api in self._inference_apis: self._env.add_pip_packages(api.input_adapter.pip_dependencies) self._env.add_pip_packages(api.output_adapter.pip_dependencies) for artifact in self.artifacts.get_artifact_list(): artifact.set_dependencies(self.env)
def decorator(bento_service_cls): bento_service_cls._env = BentoServiceEnv( pip_packages=pip_packages or pip_dependencies, pip_index_url=pip_index_url, pip_trusted_host=pip_trusted_host, pip_extra_index_url=pip_extra_index_url, infer_pip_packages=infer_pip_packages or auto_pip_dependencies, requirements_txt_file=requirements_txt_file, conda_channels=conda_channels, conda_dependencies=conda_dependencies, conda_env_yml_file=conda_env_yml_file, setup_sh=setup_sh, docker_base_image=docker_base_image, ) return bento_service_cls
def decorator(bento_service_cls): artifact_names = set() for artifact in artifacts: if not isinstance(artifact, BentoServiceArtifact): raise InvalidArgument( "BentoService @artifacts decorator only accept list of " "BentoServiceArtifact instances, instead got type: '%s'" % type(artifact)) if artifact.name in artifact_names: raise InvalidArgument( "Duplicated artifact name `%s` detected. Each artifact within one" "BentoService must have an unique name" % artifact.name) artifact_names.add(artifact.name) bento_service_cls._declared_artifacts = artifacts bento_service_cls._env = BentoServiceEnv(infer_pip_packages=True) return bento_service_cls
def set_dependencies(self, env: BentoServiceEnv): if env._infer_pip_packages: env.add_pip_packages(['xgboost'])
def set_dependencies(self, env: BentoServiceEnv): if self.backend == 'onnxruntime': env.add_pip_packages(['onnxruntime'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(["fasttext"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(["easyocr>=1.3.0"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['h2o']) env.add_conda_dependencies(['openjdk'])
def set_dependencies(self, env: BentoServiceEnv): if env._infer_pip_packages: env.add_pip_packages(["mxnet"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(["numpy"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['pytorch-lightning'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['tensorflow'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['torch', "detectron2"])
def set_dependencies(self, env: BentoServiceEnv): if self.backend == "onnxruntime": env.add_pip_packages(["onnxruntime"]) elif self.backend == "onnxruntime-gpu": env.add_pip_packages(["onnxruntime-gpu"])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['spacy'])
def set_dependencies(self, env: BentoServiceEnv): env.add_pip_packages(['coremltools>=4.0b2'])
def set_dependencies(self, env: BentoServiceEnv): if env._infer_pip_packages: env.add_pip_packages(['scikit-learn'])